Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, as models scale to larger systems like biomolecules and electrolytes, they struggle to accurately capture long-range (LR) interactions, leading current approaches to rely on explicit physics-based terms or components. In this work, we propose AllScAIP, a straightforward, attention-based, and energy-conserving MLIP model that scales to O(100 million) training samples. It addresses the long-range challenge using an all-to-all node attention component that is data-driven. Extensive ablations reveal that in low-data/small-model regimes, inductive biases improve sample efficiency. However, as data and model size scale, these benefits diminish or even reverse, while all-to-all attention remains critical for capturing LR interactions. Our model achieves state-of-the-art energy/force accuracy on molecular systems, as well as a number of physics-based evaluations (OMol25), while being competitive on materials (OMat24) and catalysts (OC20). Furthermore, it enables stable, long-timescale MD simulations that accurately recover experimental observables, including density and heat of vaporization predictions.
@article{qu2026a_TiIb,title={A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention},author={Qu, Eric and Wood, Brandon M and Krishnapriyan, Aditi S and Ulissi, Zachary W},journal={arXiv preprint arXiv:2603.06567},year={2026},url={https://arxiv.org/abs/2603.06567},}
Catalysis
Roadmap for Transforming Heterogeneous Catalysis with Artificial Intelligence
Hongliang Xin, John Kitchin, Núria López, and 8 more authors
@article{xin2025roadmap,title={Roadmap for Transforming Heterogeneous Catalysis with Artificial Intelligence},author={Xin, Hongliang and Kitchin, John and L{\'o}pez, N{\'u}ria and Schweitzer, Neil and Artrith, Nongnuch and Che, Fanglin and Grabow, Lars and Gunasooriya, Kasun and Kulik, Heather and Laino, Teo and others},journal={Nature Catalysis},year={2026},volume={9},number={2},pages={102--111},doi={10.1038/s41929-026-01479-x},url={https://doi.org/10.1038/s41929-026-01479-x},}
ML Datasets, AI/ML Models, Molecules
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang, and 16 more authors
@article{gharakhanyan2025openmolecularcrystals2025,title={Open Molecular Crystals 2025 (OMC25) Dataset and Models},author={Gharakhanyan, Vahe and Barroso-Luque, Luis and Yang, Yi and Shuaibi, Muhammed and Michel, Kyle and Levine, Daniel S. and Dzamba, Misko and Fu, Xiang and Gao, Meng and Liu, Xingyu and Ni, Haoran and Noori, Keian and Wood, Brandon M. and Uyttendaele, Matt and Boromand, Arman and Zitnick, C. Lawrence and Marom, Noa and Ulissi, Zachary W. and Sriram, Anuroop},journal={Scientific Data},year={2026},volume={13},number={1},pages={354},doi={10.1038/s41597-026-06628-2},url={https://doi.org/10.1038/s41597-026-06628-2},archiveprefix={arXiv},primaryclass={physics.chem-ph}}
@article{wood2025umafamilyuniversalmodels,title={UMA: A Family of Universal Models for Atoms},author={Wood, Brandon M. and Dzamba, Misko and Fu, Xiang and Gao, Meng and Shuaibi, Muhammed and Barroso-Luque, Luis and Abdelmaqsoud, Kareem and Gharakhanyan, Vahe and Kitchin, John R. and Levine, Daniel S. and Michel, Kyle and Sriram, Anuroop and Cohen, Taco and Das, Abhishek and Rizvi, Ammar and Sahoo, Sushree Jagriti and Ulissi, Zachary W. and Zitnick, C. Lawrence},booktitle={Advances in Neural Information Processing Systems (NeurIPS)},year={2026},volume={38},pages={129391--129427},url={https://arxiv.org/abs/2505.08760},archiveprefix={arXiv},primaryclass={cs.LG}}
ML Datasets, AI/ML Models, Inorganic Materials
The Open Materials 2024 (OMat24) inorganic materials dataset and models
Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, and 6 more authors
@article{barroso2024open,title={The Open Materials 2024 (OMat24) inorganic materials dataset and models},author={Barroso-Luque, Luis and Shuaibi, Muhammed and Fu, Xiang and Wood, Brandon M and Dzamba, Misko and Gao, Meng and Rizvi, Ammar and Zitnick, C Lawrence and Ulissi, Zachary W},journal={Nature Computational Science},year={2026},pages={1--11},doi={10.1038/s43588-026-00996-w},url={https://doi.org/10.1038/s43588-026-00996-w},}
2025
AI/ML Models, Catalysis
CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin, and 2 more authors
@article{doi:10.1021/acscatal.4c04272,title={CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks},author={Wander, Brook and Shuaibi, Muhammed and Kitchin, John R. and Ulissi, Zachary W. and Zitnick, C. Lawrence},journal={ACS Catalysis},year={2025},volume={15},number={7},pages={5283-5294},doi={10.1021/acscatal.4c04272},url={https://doi.org/10.1021/acscatal.4c04272},}
ML Datasets, AI/ML Models, Molecules
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, and 20 more authors
@article{levine2025openmolecules2025omol25,title={The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models},author={Levine, Daniel S. and Shuaibi, Muhammed and Spotte-Smith, Evan Walter Clark and Taylor, Michael G. and Hasyim, Muhammad R. and Michel, Kyle and Batatia, Ilyes and Csányi, Gábor and Dzamba, Misko and Eastman, Peter and Frey, Nathan C. and Fu, Xiang and Gharakhanyan, Vahe and Krishnapriyan, Aditi S. and Rackers, Joshua A. and Raja, Sanjeev and Rizvi, Ammar and Rosen, Andrew S. and Ulissi, Zachary and Vargas, Santiago and Zitnick, C. Lawrence and Blau, Samuel M. and Wood, Brandon M.},journal={arXiv preprint arxiv:2505.08762},year={2025},volume={2505},url={https://arxiv.org/abs/2505.08762},archiveprefix={arXiv},primaryclass={physics.chem-ph}}
ML Datasets, AI/ML Models, Catalysis
The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces
Sushree Jagriti Sahoo, Mikael Maraschin, Daniel S. Levine, and 6 more authors
@article{sahoo2025opencatalyst2025oc25,title={The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces},author={Sahoo, Sushree Jagriti and Maraschin, Mikael and Levine, Daniel S. and Ulissi, Zachary and Zitnick, C. Lawrence and Varley, Joel B and Gauthier, Joseph A. and Govindarajan, Nitish and Shuaibi, Muhammed},journal={arXiv preprint arxiv:2509.17862},year={2025},url={https://arxiv.org/abs/2509.17862},archiveprefix={arXiv},primaryclass={cond-mat.mtrl-sci}}
ML Datasets, Metal-Organic Frameworks
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu, and 12 more authors
@article{sriram2025opendac2025dataset,title={The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture},author={Sriram, Anuroop and Brabson, Logan M. and Yu, Xiaohan and Choi, Sihoon and Abdelmaqsoud, Kareem and Moubarak, Elias and de Haan, Pim and Löwe, Sindy and Brehmer, Johann and Kitchin, John R. and Welling, Max and Zitnick, C. Lawrence and Ulissi, Zachary and Medford, Andrew J. and Sholl, David S.},journal={arXiv preprint arxiv:2508.03162},year={2025},url={https://arxiv.org/abs/2505.08761},archiveprefix={arXiv},primaryclass={cond-mat.mtrl-sci}}
AI/ML Models, Molecules
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, and 21 more authors
@article{gharakhanyan2025fastcspacceleratedmolecularcrystal,title={FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms},author={Gharakhanyan, Vahe and Yang, Yi and Barroso-Luque, Luis and Shuaibi, Muhammed and Levine, Daniel S. and Michel, Kyle and Bernat, Viachaslau and Dzamba, Misko and Fu, Xiang and Gao, Meng and Liu, Xingyu and Noori, Keian and Purvis, Lafe J. and Rao, Tingling and Wood, Brandon M. and Rizvi, Ammar and Uyttendaele, Matt and Ouderkirk, Andrew J. and Daraio, Chiara and Zitnick, C. Lawrence and Boromand, Arman and Marom, Noa and Ulissi, Zachary W. and Sriram, Anuroop},journal={arXiv preprint arxiv:2508.02641},year={2025},url={https://arxiv.org/abs/2505.08763},archiveprefix={arXiv},primaryclass={physics.chem-ph}}
Molecules
Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties
Jianing Zhou, Yuge Huang, Arman Boromand, and 7 more authors
@article{zhou2025geneticalgorithmacceleratedcomputationaldiscovery,title={Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties},author={Zhou, Jianing and Huang, Yuge and Boromand, Arman and Noori, Keian and Purvis, Lafe and Oh, Chulwoo and Lu, Lu and Ulissi, Zachary W. and Gharakhanyan, Vahe and Zhang, Xinyue},journal={arXiv preprint arxiv:2505.13477},year={2025},doi={10.1039/d5ra04477d},url={https://doi.org/10.1039/d5ra04477d},archiveprefix={arXiv},primaryclass={cond-mat.soft}}
AI/ML Models
Multi-Physics Inverse Design of Varifocal Optical Devices using Data-Driven Surrogates and Differential Modeling
Zeqing Jin, Zhaocheng Liu, Nagi Elabbasi, and 3 more authors
@article{jin2025multiphysicsinversedesignvarifocal,title={Multi-Physics Inverse Design of Varifocal Optical Devices using Data-Driven Surrogates and Differential Modeling},author={Jin, Zeqing and Liu, Zhaocheng and Elabbasi, Nagi and Ulissi, Zachary and Gu, Grace X. and Nie, Zhaoyu},journal={arXiv preprint arxiv:2503.18911},year={2025},url={https://arxiv.org/abs/2503.18911},archiveprefix={arXiv},primaryclass={cs.CE}}
AI/ML Models
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, and 4 more authors
@article{joshi2025allatomdiffusiontransformersunified,title={All-atom Diffusion Transformers: Unified generative modelling of molecules and materials},author={Joshi, Chaitanya K. and Fu, Xiang and Liao, Yi-Lun and Gharakhanyan, Vahe and Miller, Benjamin Kurt and Sriram, Anuroop and Ulissi, Zachary W.},journal={arXiv preprint arxiv:2503.03965},year={2025},url={https://arxiv.org/abs/2505.08764},archiveprefix={arXiv},primaryclass={cs.LG}}
Inorganic Materials
Constraint Active Search in Process Window Optimization for Powder Feed Directed Energy Deposition
Xiaoxiao Wang, Jose A Loli, Zachary W Ulissi, and 3 more authors
Integrating Materials and Manufacturing Innovation, 2025
@article{wang2025constraint,title={Constraint Active Search in Process Window Optimization for Powder Feed Directed Energy Deposition},author={Wang, Xiaoxiao and Loli, Jose A and Ulissi, Zachary W and de Boer, Maarten P and Webler, Bryan A and Kurchin, Rachel C},journal={Integrating Materials and Manufacturing Innovation},year={2025},volume={14},number={1},pages={106--114},publisher={Springer},doi={10.1007/s40192-025-00393-7},url={https://doi.org/10.1007/s40192-025-00393-7},}
ML Datasets, AI/ML Models, Catalysis, Inorganic Materials
Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations
Sushree Jagriti Sahoo, Mikael Maraschin, Joel B Varley, and 7 more authors
Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introduce Open Catalyst 2025 (OC25), the largest dataset for solid-liquid interfaces. To demonstrate OC25-trained models as practical tools for electrocatalysis, we investigate CO dimerization on Cu surfaces, a key step in CO electroreduction. Using large cells (>800 atoms) and enhanced sampling up to 7 ns - the largest explicit-solvent CO dimerization study to date - we compute free-energy profiles under varied surface charge, cation identity, and surface facet. We find that dimerization is weakly sensitive to charge and cation identity, with appreciable stabilization only at the most negative charge densities, while extension to stepped Cu(310) reveals a more favorable pathway at modest reducing potentials. Our results demonstrate that OC25-trained models provide a scalable tool for investigating electrocatalytic transformations at solid-liquid interfaces, enabling simulations orders of magnitude beyond ab initio methods.
@article{sahoo2025insights_1paM,title={Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations},author={Sahoo, Sushree Jagriti and Maraschin, Mikael and Varley, Joel B and Levine, Daniel S and Ulissi, Zachary and Zitnick, C Lawrence and Takemura, Wayu and Gauthier, Joseph A and Govindarajan, Nitish and Shuaibi, Muhammed},journal={arXiv preprint arXiv:2509.17862},year={2025},url={https://arxiv.org/abs/2509.17862},}
AI/ML Models, Molecules
EVA-Flow: Environment-Aware Flow Matching for Unified 3D Molecular Conformation Generation
Bing Yan, Benjamin Kurt Miller, Anuroop Sriram, and 3 more authors
Predicting the 3D geometry of molecules is central to applications in drug discovery, materials design, and molecular modeling. However, molecular geometry can change dramatically across environments (e.g., crystal lattice versus protein binding pocket). Existing generative approaches are typically environment-agnostic or require separate models for each environment, which limits generalization. We introduce EVA-Flow, a unified framework for environment-aware conformation generation. EVA-Flow combines a variational autoencoder with a flow matching decoder and incorporates environment information through a learned embedding. Across four environments including vacuum, protein-ligand docking, solvation, and crystal packing, EVA-Flow substantially improves generation accuracy through pretraining and unification. Analysis of shared molecules that appear in multiple environments further shows that EVA-Flow generates distinct, environment-specific conformations rather than memorizing a single geometry.
@article{yanevaflow_bCjg,title={EVA-Flow: Environment-Aware Flow Matching for Unified 3D Molecular Conformation Generation},author={Yan, Bing and Miller, Benjamin Kurt and Sriram, Anuroop and Ulissi, Zachary Ward and Cho, Kyunghyun and Chen, Ricky TQ},year={2025},url={https://openreview.net/forum?id=g8oocfZmA0},}
@article{abed2024pourbaix,title={Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution},author={Abed, Jehad and Heras-Domingo, Javier and Sanspeur, Rohan Yuri and Luo, Mingchuan and Alnoush, Wajdi and Meira, Debora Motta and Wang, Hsiaotsu and Wang, Jian and Zhou, Jigang and Zhou, Daojin and others},journal={Journal of the American Chemical Society},year={2024},publisher={ACS Publications},doi={10.1021/jacs.4c01353},url={https://doi.org/10.1021/jacs.4c01353},}
AI/ML Models
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R Kitchin, and 3 more authors
@article{shoghi2023molecules,title={From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction},author={Shoghi, Nima and Kolluru, Adeesh and Kitchin, John R and Ulissi, Zachary W and Zitnick, C Lawrence and Wood, Brandon M},journal={arXiv preprint arXiv:2310.16802},year={2024},}
ML Datasets, Metal-Organic Frameworks
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Sihoon Choi, Xiaohan Yu, and 6 more authors
@article{doi:10.1021/acscentsci.3c01629,title={The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture},author={Sriram, Anuroop and Choi, Sihoon and Yu, Xiaohan and Brabson, Logan M. and Das, Abhishek and Ulissi, Zachary and Uyttendaele, Matt and Medford, Andrew J. and Sholl, David S.},journal={ACS Central Science},year={2024},volume={10},number={5},pages={923-941},doi={10.1021/acscentsci.3c01629},url={https://doi.org/10.1021/acscentsci.3c01629},}
AI/ML Models, Inorganic Materials
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto, and 3 more authors
@article{gruver2023fine,title={Fine-Tuned Language Models Generate Stable Inorganic Materials as Text},author={Gruver, Nate and Sriram, Anuroop and Madotto, Andrea and Wilson, Andrew and Zitnick, C Lawrence and Ulissi, Zachary},journal={ICLR},year={2024},}
AI/ML Models
Generalization of graph-based active learning relaxation strategies across materials
Xiaoxiao Wang, Joseph Musielewicz, Richard Tran, and 6 more authors
@article{wang2024generalization,title={Generalization of graph-based active learning relaxation strategies across materials},author={Wang, Xiaoxiao and Musielewicz, Joseph and Tran, Richard and Ethirajan, Sudheesh Kumar and Fu, Xiaoyan and Mera, Hilda and Kitchin, John R and Kurchin, Rachel C and Ulissi, Zachary W},journal={Machine Learning: Science and Technology},year={2024},volume={5},number={2},pages={025018},publisher={IOP Publishing},doi={10.1088/2632-2153/ad37f0},url={https://doi.org/10.1088/2632-2153/ad37f0},}
ML Datasets, AI/ML Models, Catalysis
Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials
Christian M. Clausen, Jan Rossmeisl, and Zachary W. Ulissi
@article{doi:10.1021/acs.jpcc.4c01704,title={Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials},author={Clausen, Christian M. and Rossmeisl, Jan and Ulissi, Zachary W.},journal={The Journal of Physical Chemistry C},year={2024},volume={128},number={27},pages={11190-11195},doi={10.1021/acs.jpcc.4c01704},url={https://doi.org/10.1021/acs.jpcc.4c01704},}
ML Datasets, Inorganic Materials
Enumeration of surface site nuclearity and shape in a database of intermetallic low-index surface facets
Unnatti Sharma, Angela Nguyen, John R Kitchin, and 2 more authors
@article{sharma2024enumeration,title={Enumeration of surface site nuclearity and shape in a database of intermetallic low-index surface facets},author={Sharma, Unnatti and Nguyen, Angela and Kitchin, John R and Ulissi, Zachary W and Janik, Michael J},journal={Journal of Catalysis},year={2024},volume={440},pages={115795},publisher={Academic Press},doi={10.2139/ssrn.4921171},url={https://doi.org/10.2139/ssrn.4921171},}
ML Datasets, AI/ML Models, Catalysis
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi, and 17 more authors
@article{abed2024opencatalystexperiments2024,title={Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models},author={Abed, Jehad and Kim, Jiheon and Shuaibi, Muhammed and Wander, Brook and Duijf, Boris and Mahesh, Suhas and Lee, Hyeonseok and Gharakhanyan, Vahe and Hoogland, Sjoerd and Irtem, Erdem and Lan, Janice and Schouten, Niels and Vijayakumar, Anagha Usha and Hattrick-Simpers, Jason and Kitchin, John R. and Ulissi, Zachary W. and van Vugt, Aaike and Sargent, Edward H. and Sinton, David and Zitnick, C. Lawrence},year={2024},url={https://arxiv.org/abs/2411.11783},archiveprefix={arXiv},primaryclass={cond-mat.mtrl-sci}}
Catalysis, Inorganic Materials
Practical Application of Machine Learning in Catalysis
Zachary W Ulissi, Kevin Tran, Junwoong Yoon, and 5 more authors
Enthusiasm for the application of artificial intelligence/machine learning (AI/ML) in catalysis is high because of the complex challenges the community routinely faces. As discussed in the other chapters, the space of possible materials that the field consider for catalytic applications is large, and encompasses metals, oxides, nitrides, alloys, 2D materials, nanoparticles, and metal–organic frameworks, among many others. Structure, composition, and morphology at the surface are all important, and defects, grain boundaries, dynamic restructuring, and segregation further complicate the matter. There are many potential surface reactions to consider, and reaction mechanisms vary between different catalysts. Reaction conditions exert significant effects on kinetics and catalyst stability, especially in electrochemical systems. AI/ML will not magically permit understanding of all of these effects, but it can accelerate …
@incollection{catalysischapter,title={Practical Application of Machine Learning in Catalysis},author={Ulissi, Zachary W and Tran, Kevin and Yoon, Junwoong and Shuaibi, Muhammed and Liu, Mingjie and Zhan, Ni and Broderick, Kirby and Kitchin, John R},booktitle={Computational Catalysis},year={2024},pages={224--279},publisher={Royal Society of Chemistry},doi={10.1039/9781837670178-00224},url={https://doi.org/10.1039/9781837670178-00224},chapter={Chapter 6: Practical Application of Machine Learning in Catalysis}}
2023
Catalysis
Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions
Kirby Broderick, Eric Lopato, Brook Wander, and 3 more authors
@article{broderick2023identifying,title={Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions},author={Broderick, Kirby and Lopato, Eric and Wander, Brook and Bernhard, Stefan and Kitchin, John and Ulissi, Zachary},journal={Applied Catalysis B: Environmental},year={2023},month=jan,volume={320},pages={121959},publisher={Elsevier},doi={10.1016/j.apcatb.2022.121959},url={https://doi.org/10.1016/j.apcatb.2022.121959},}
ML Datasets, Catalysis
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi, and 14 more authors
@article{doi:10.1021/acscatal.2c05426,title={The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts},author={Tran, Richard and Lan, Janice and Shuaibi, Muhammed and Wood, Brandon M. and Goyal, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Therrien, Félix and Abed, Jehad and Voznyy, Oleksandr and Sargent, Edward H. and Ulissi, Zachary and Zitnick, C. Lawrence},journal={ACS Catalysis},year={2023},volume={13},number={5},pages={3066-3084},doi={10.1021/acscatal.2c05426},url={https://doi.org/10.1021/acscatal.2c05426},}
ML Datasets, AI/ML Models, Catalysis
AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials
Janice Lan, Aini Palizhati, Muhammed Shuaibi, and 6 more authors
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy-the adsorption energy-for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.63% of the …
@article{lan2023adsorbml,title={AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials},author={Lan, Janice and Palizhati, Aini and Shuaibi, Muhammed and Wood, Brandon M and Wander, Brook and Das, Abhishek and Uyttendaele, Matt and Zitnick, C Lawrence and Ulissi, Zachary W},journal={npj Computational Materials},year={2023},volume={9},number={1},pages={172},publisher={Nature Publishing Group UK London},doi={10.1038/s41524-023-01121-5},url={https://doi.org/10.1038/s41524-023-01121-5},}
AI/ML Models
Beyond independent error assumptions in large GNN atomistic models
Janghoon Ock, Tian Tian, John Kitchin, and 1 more author
@article{ock2023beyond,title={Beyond independent error assumptions in large GNN atomistic models},author={Ock, Janghoon and Tian, Tian and Kitchin, John and Ulissi, Zachary},journal={The Journal of Chemical Physics},year={2023},volume={158},number={21},publisher={AIP Publishing},doi={10.1063/5.0151159},url={https://doi.org/10.1063/5.0151159},}
Catalysis
WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions
Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin, and 1 more author
Journal of Chemical Information and Modeling, 2023
@article{doi:10.1021/acs.jcim.3c00142,title={WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions},author={Sanspeur, Rohan Yuri and Heras-Domingo, Javier and Kitchin, John R. and Ulissi, Zachary},journal={Journal of Chemical Information and Modeling},year={2023},volume={63},number={8},pages={2427-2437},doi={10.1021/acs.jcim.3c00142},url={https://doi.org/10.1021/acs.jcim.3c00142},note={PMID: 37017312},}
AI/ML Models
AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification
Muhammed Shuaibi, Yuge Hu, Xiangyun Lei, and 8 more authors
@article{shuaibi2023amptorch,title={AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification},author={Shuaibi, Muhammed and Hu, Yuge and Lei, Xiangyun and Comer, Benjamin M and Adams, Matt and Paras, Jacob and Chen, Rui Qi and Musa, Eric and Musielewicz, Joseph and Peterson, Andrew A and others},journal={Journal of Open Source Software},year={2023},volume={8},number={87},pages={5035},doi={10.21105/joss.05035},url={https://doi.org/10.21105/joss.05035},}
AI/ML Models
Chemical Properties from Graph Neural Network-Predicted Electron Densities
Ethan M Sunshine, Muhammed Shuaibi, Zachary W Ulissi, and 1 more author
@article{sunshine2023chemical,title={Chemical Properties from Graph Neural Network-Predicted Electron Densities},author={Sunshine, Ethan M and Shuaibi, Muhammed and Ulissi, Zachary W and Kitchin, John R},journal={The Journal of Physical Chemistry C},year={2023},volume={127},number={48},pages={23459--23466},publisher={ACS Publications},doi={10.1021/acs.jpcc.3c06157},url={https://doi.org/10.1021/acs.jpcc.3c06157},}
ML Datasets, AI/ML Models, Molecules
Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set
Aaron G. Garrison, Javier Heras-Domingo, John R. Kitchin, and 3 more authors
Journal of Chemical Information and Modeling, 2023
@article{doi:10.1021/acs.jcim.3c01226,title={Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM\_wB97MV Data Set},author={Garrison, Aaron G. and Heras-Domingo, Javier and Kitchin, John R. and dos Passos Gomes, Gabriel and Ulissi, Zachary W. and Blau, Samuel M.},journal={Journal of Chemical Information and Modeling},year={2023},volume={0},number={0},pages={null},doi={10.1021/acs.jcim.3c01226},url={https://doi.org/10.1021/acs.jcim.3c01226},note={PMID: 38049389}}
AI/ML Models
Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters
Rajesh K Raju, Saurabh Sivakumar, Xiaoxiao Wang, and 1 more author
Journal of Chemical Information and Modeling, 2023
@article{raju2023cluster,title={Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters},author={Raju, Rajesh K and Sivakumar, Saurabh and Wang, Xiaoxiao and Ulissi, Zachary W},journal={Journal of Chemical Information and Modeling},year={2023},publisher={ACS Publications},doi={10.1021/acs.jcim.3c01431},url={https://doi.org/10.1021/acs.jcim.3c01431},}
2022
Catalysis
Heterogeneous Catalysis in Grammar School
Johannes T. Margraf, Zachary W. Ulissi, Yousung Jung, and 1 more author
@article{doi:10.1021/acs.jpcc.1c10285,title={Heterogeneous Catalysis in Grammar School},author={Margraf, Johannes T. and Ulissi, Zachary W. and Jung, Yousung and Reuter, Karsten},journal={The Journal of Physical Chemistry C},year={2022},volume={126},number={6},pages={2931-2936},doi={10.1021/acs.jpcc.1c10285},url={https://doi.org/10.1021/acs.jpcc.1c10285},}
AI/ML Models
How Do Graph Networks Generalize to Large and Diverse Molecular Systems?
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, and 4 more authors
@article{gasteiger2022how,title={How Do Graph Networks Generalize to Large and Diverse Molecular Systems?},author={Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and Günnemann, Stephan and Ulissi, Zachary and Zitnick, C. Lawrence and Das, Abhishek},journal={arXiv preprint arXiv:2204.02782},year={2022},doi={10.48550/arXiv.2204.02782},url={https://doi.org/10.48550/arXiv.2204.02782},}
@article{Musielewicz_2022,title={{FINETUNA}: Fine-tuning Accelerated Molecular Simulations},author={Musielewicz, Joseph and Wang, Xiaoxiao and Tian, Tian and Ulissi, Zachary W},journal={Machine Learning: Science and Technology},year={2022},month=sep,publisher={{IOP} Publishing},doi={10.1088/2632-2153/ac8fe0},url={https://doi.org/10.1088/2632-2153/ac8fe0},}
AI/ML Models
Transfer learning using attentions across atomic systems with graph neural networks (TAAG)
Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, and 4 more authors
@article{doi:10.1063/5.0088019,title={Transfer learning using attentions across atomic systems with graph neural networks (TAAG)},author={Kolluru, Adeesh and Shoghi, Nima and Shuaibi, Muhammed and Goyal, Siddharth and Das, Abhishek and Zitnick, C. Lawrence and Ulissi, Zachary},journal={The Journal of Chemical Physics},year={2022},volume={156},number={18},pages={184702},doi={10.1063/5.0088019},url={https://doi.org/10.1063/5.0088019},}
AI/ML Models
Spherical Channels for Modeling Atomic Interactions
C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, and 5 more authors
@article{https://doi.org/10.48550/arxiv.2206.14331,title={Spherical Channels for Modeling Atomic Interactions},author={Zitnick, C. Lawrence and Das, Abhishek and Kolluru, Adeesh and Lan, Janice and Shuaibi, Muhammed and Sriram, Anuroop and Ulissi, Zachary and Wood, Brandon},journal={NeurIPS},year={2022},month=dec,publisher={arXiv},doi={10.48550/ARXIV.2206.14331},url={https://doi.org/10.48550/ARXIV.2206.14331},keywords={Chemical Physics (physics.chem-ph), Computational Engineering, Finance, and Science (cs.CE), Machine Learning (cs.LG), Computational Physics (physics.comp-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.6; J.2},copyright={arXiv.org perpetual, non-exclusive license}}
Catalysis, Inorganic Materials
Site Geometry as a Descriptor for Catalyst Selectivity in Intermetallics
Unnatti Sharma, Angela Nguyen, Michael John Janik, and 1 more author
@article{sharma4145497site,title={Site Geometry as a Descriptor for Catalyst Selectivity in Intermetallics},author={Sharma, Unnatti and Nguyen, Angela and Janik, Michael John and Ulissi, Zachary},journal={Preprint available at SSRN 4145497},year={2022},doi={10.2139/ssrn.4145496},url={https://doi.org/10.2139/ssrn.4145496},}
Catalysis
Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation
Bjarne Kreitz, Patrick Lott, Jongyoon Bae, and 6 more authors
@article{doi:10.1021/acscatal.2c03378,title={Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation},author={Kreitz, Bjarne and Lott, Patrick and Bae, Jongyoon and Blöndal, Katrín and Angeli, Sofia and Ulissi, Zachary W. and Studt, Felix and Goldsmith, C. Franklin and Deutschmann, Olaf},journal={ACS Catalysis},year={2022},volume={12},number={18},pages={11137-11151},doi={10.1021/acscatal.2c03378},url={https://doi.org/10.1021/acscatal.2c03378},}
Catalysis
Screening of bimetallic electrocatalysts for water purification with machine learning
Richard Tran, Duo Wang, Ryan Kingsbury, and 4 more authors
@article{tran2022screening,title={Screening of bimetallic electrocatalysts for water purification with machine learning},author={Tran, Richard and Wang, Duo and Kingsbury, Ryan and Palizhati, Aini and Persson, Kristin Aslaug and Jain, Anubhav and Ulissi, Zachary W},journal={The Journal of Chemical Physics},year={2022},volume={157},number={7},pages={074102},publisher={AIP Publishing LLC},doi={10.1063/5.0092948},url={https://doi.org/10.1063/5.0092948},}
ML Datasets, AI/ML Models
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Yuge Hu, Joseph Musielewicz, Zachary W Ulissi, and 1 more author
Machine Learning: Science and Technology, Dec 2022
Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Established a-posteriori UQ methods, including ensemble methods, the dropout method, the delta method, and various heuristic distance metrics, have limitations such as being computationally challenging for large models due to model re-training. In addition, the uncertainty estimates are often not rigorously calibrated. In this work, we propose combining the distribution-free UQ method, known as conformal prediction (CP), with the distances in the neural network’s latent space to estimate the uncertainty of energies predicted by neural network force fields. We evaluate this method (CP+latent) along with other UQ methods on two essential aspects, calibration, and sharpness, and find this method to be both calibrated and sharp under the assumption of independent and identically-distributed (i.i.d.) data. We show that the method is relatively insensitive to hyperparameters selected, and test the limitations of the method when the i.i.d. assumption is violated. Finally, we demonstrate that this method can be readily applied to trained neural network force fields with traditional and graph neural network architectures to obtain estimates of uncertainty with low computational costs on a training dataset of 1 million images to showcase its scalability and portability. Incorporating the CP method with latent distances offers a calibrated, sharp and efficient strategy to estimate the uncertainty of neural network force fields. In addition, the CP approach can also function as a promising strategy for calibrating uncertainty estimated by other approaches.
@article{Hu_2022,title={Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials},author={Hu, Yuge and Musielewicz, Joseph and Ulissi, Zachary W and Medford, Andrew J},journal={Machine Learning: Science and Technology},year={2022},month=dec,volume={3},number={4},pages={045028},publisher={IOP Publishing},doi={10.1088/2632-2153/aca7b1},url={https://doi.org/10.1088/2632-2153/aca7b1},}
ML Datasets, Catalysis
The Open Catalyst Challenge 2021: Competition Report
Abhishek Das, Muhammed Shuaibi, Aini Palizhati, and 22 more authors
@article{pmlr-v176-das22a,title={The Open Catalyst Challenge 2021: Competition Report},author={Das, Abhishek and Shuaibi, Muhammed and Palizhati, Aini and Goyal, Siddharth and Grover, Aditya and Kolluru, Adeesh and Lan, Janice and Rizvi, Ammar and Sriram, Anuroop and Wood, Brandon and Parikh, Devi and Ulissi, Zachary and Zitnick, C. Lawrence and Ke, Guolin and Zheng, Shuxin and Shi, Yu and He, Di and Liu, Tie-Yan and Ying, Chengxuan and You, Jiacheng and He, Yihan and Grigoriev, Rostislav and Lukin, Ruslan and Yarullin, Adel and Faleev, Max},booktitle={Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track},year={2022},month=dec,volume={176},pages={29--40},publisher={PMLR},url={https://proceedings.mlr.press/v176/das22a.html},editor={Kiela, Douwe and Ciccone, Marco and Caputo, Barbara},series={Proceedings of Machine Learning Research}}
Inorganic Materials
Predicting Oxidation Behavior of Multi-Principal Element Alloys by Machine Learning Methods
Jose A Loli, Amish R Chovatiya, Yining He, and 3 more authors
@article{D2CY01267G,title={Predicting Oxidation Behavior of Multi-Principal Element Alloys by Machine Learning Methods},author={Loli, Jose A and Chovatiya, Amish R and He, Yining and Ulissi, Zachary W and de Boer, Maarten P and Webler, Bryan A},journal={Oxidation of Metals},year={2022},month=jul,volume={98},number={5},pages={429--450},publisher={Springer},doi={10.1007/s11085-022-10129-z},url={https://doi.org/10.1007/s11085-022-10129-z},}
ML Datasets, AI/ML Models, Catalysis, Molecules
GemNet-OC: developing graph neural networks for large and diverse molecular simulation datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, and 4 more authors
Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shift (similarity of the training and test set). Despite these large differences, benchmarks on small and narrow datasets remain the predominant method of demonstrating progress in graph neural networks (GNNs) for molecular simulation, likely due to cheaper training compute requirements. This raises the question – does GNN progress on small and narrow datasets translate to these more complex datasets? This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous state-of-the-art on OC20 by 16% while reducing training time by a factor of 10. We then compare the impact of 18 model components and hyperparameter choices on performance in multiple datasets. We find that the resulting model would be drastically different depending on the dataset used for making model choices. To isolate the source of this discrepancy we study six subsets of the OC20 dataset that individually test each of the above-mentioned four dataset aspects. We find that results on the OC-2M subset correlate well with the full OC20 dataset while being substantially cheaper to train on. Our findings challenge the common practice of developing GNNs solely on small datasets, but highlight ways of achieving fast …
@article{gasteiger2022gemnetoc_Gb6H,title={GemNet-OC: developing graph neural networks for large and diverse molecular simulation datasets},author={Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and Günnemann, Stephan and Ulissi, Zachary and Zitnick, C Lawrence and Das, Abhishek},journal={arXiv preprint arXiv:2204.02782},year={2022},url={https://arxiv.org/abs/2204.02782},}
ML Datasets, AI/ML Models, Catalysis
Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, and 6 more authors
The development of machine-learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While they are effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst data sets has offered the opportunity to build a universal machine-learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without the additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 Data set (OC20) has begun just that, pushing the heterogeneous catalysis and machine-learning communities toward building more accurate and robust models. In this Perspective, we discuss …
@article{kolluru2022open_kO05,title={Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery},author={Kolluru, Adeesh and Shuaibi, Muhammed and Palizhati, Aini and Shoghi, Nima and Das, Abhishek and Wood, Brandon and Zitnick, C Lawrence and Kitchin, John R and Ulissi, Zachary W},journal={Acs Catalysis},year={2022},volume={12},number={14},pages={8572-8581},publisher={American Chemical Society},doi={10.1021/acscatal.2c02291},url={https://doi.org/10.1021/acscatal.2c02291},}
AI/ML Models, Catalysis
Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion
Brook Wander, Kirby Broderick, and Zachary W Ulissi
Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 …
@article{wander2022catlas_5LPo,title={Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion},author={Wander, Brook and Broderick, Kirby and Ulissi, Zachary W},journal={Catalysis Science & Technology},year={2022},volume={12},number={20},pages={6256-6267},publisher={The Royal Society of Chemistry},doi={10.1039/d2cy01267g},url={https://doi.org/10.1039/d2cy01267g},}
AI/ML Models, Catalysis
Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
Catalysts are critical for most large-scale energy intensive chemical transformation processes, such as energy storage, liquid fuel production, and the formation of chemical building blocks. The catalyst composition, structure, and morphology impact the performance under reaction conditions and influences properties like activity and selectivity. Many industrial catalysts offer imperfect activity/selectivity, or contain expensive metals. Further, catalysts can deactivate over time as the morphology changes or harsh reactive environments alter the surface structure. Methods to automatically model and predict the kinetics of how catalyst surfaces will restructure would enable engineers to design around these challenges, improve performance, increase longevity. This project investigated a specific type of machine learning model, deep reinforcement learning, machine learning models to act as surrogates for the physical system, and compared the results of these approaches with a specialized high-throughput experimental synthesis and measurement platform.
@article{ulissi2022predicting_XtJa,title={Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials},author={Ulissi, Zachary},year={2022},number={DOE-CMU--0001221-1},publisher={Carnegie Mellon Univ., Pittsburgh, PA (United States)},doi={10.2172/2324766},url={https://doi.org/10.2172/2324766},}
2021
ML Datasets, Catalysis
Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, and 14 more authors
@article{doi:10.1021/acscatal.0c04525,title={Open Catalyst 2020 (OC20) Dataset and Community Challenges},author={Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Lavril, Thibaut and Shuaibi, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary},journal={ACS Catalysis},year={2021},month=apr,volume={11},number={10},pages={6059-6072},doi={10.1021/acscatal.0c04525},url={https://doi.org/10.1021/acscatal.0c04525},}
AI/ML Models, Catalysis
Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H_2O_2 Synthesis through Active Motif Screening
@article{doi:10.1021/acscatal.0c05494,title={Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H$_2$O$_2$ Synthesis through Active Motif Screening},author={Back, Seoin and Na, Jonggeol and Ulissi, Zachary W.},journal={ACS Catalysis},year={2021},month=feb,volume={11},number={5},pages={2483-2491},doi={10.1021/acscatal.0c05494},url={https://doi.org/10.1021/acscatal.0c05494},}
AI/ML Models, Catalysis
Computational catalyst discovery: Active classification through myopic multiscale sampling
Kevin Tran, Willie Neiswanger, Kirby Broderick, and 3 more authors
@article{tran2021computational,title={Computational catalyst discovery: Active classification through myopic multiscale sampling},author={Tran, Kevin and Neiswanger, Willie and Broderick, Kirby and Xing, Eric and Schneider, Jeff and Ulissi, Zachary W},journal={The Journal of Chemical Physics},year={2021},volume={154},number={12},pages={124118},publisher={AIP Publishing LLC},doi={10.1063/5.0044989},url={https://doi.org/10.1063/5.0044989},}
Biochemistry
Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA
Abhishek Debnath, Sanchari Saha, Duo O. Li, and 4 more authors
@article{doi:10.1021/acsami.0c22941,title={Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA},author={Debnath, Abhishek and Saha, Sanchari and Li, Duo O. and Chu, Ximo S. and Ulissi, Zachary W. and Green, Alexander A. and Wang, Qing Hua},journal={ACS Applied Materials \& Interfaces},year={2021},volume={13},number={7},pages={8082-8094},doi={10.1021/acsami.0c22941},url={https://doi.org/10.1021/acsami.0c22941},note={PMID: 33570927},}
AI/ML Models
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
Junwoong Yoon, Zhonglin Cao, Rajesh K Raju, and 5 more authors
@article{yoon2021deep,title={Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy},author={Yoon, Junwoong and Cao, Zhonglin and Raju, Rajesh K and Wang, Yuyang and Burnley, Robert and Gellman, Andrew J and Farimani, Amir Barati and Ulissi, Zachary W},journal={Machine Learning: Science and Technology},year={2021},volume={2},number={4},pages={045018},publisher={IOP Publishing},doi={10.1088/2632-2153/ac191c},url={https://doi.org/10.1088/2632-2153/ac191c},}
AI/ML Models
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, and 4 more authors
@article{shuaibi2021rotation,title={Rotation Invariant Graph Neural Networks using Spin Convolutions},author={Shuaibi, Muhammed and Kolluru, Adeesh and Das, Abhishek and Grover, Aditya and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C Lawrence},journal={arXiv preprint arXiv:2106.09575},year={2021},doi={10.48550/arXiv.2106.09575},url={https://doi.org/10.48550/arXiv.2106.09575},}
ML Datasets, AI/ML Models
Enabling robust offline active learning for machine learning potentials using simple physics-based priors
Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen, and 1 more author
Machine Learning: Science and Technology, Dec 2021
Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning and uncertainty estimates. When starting with small datasets, convergence of active learning approaches is a major outstanding challenge which limited most demonstrations to online active learning. In this work we demonstrate a Δ-machine learning approach that enables stable convergence in offline active learning strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70-90%. The approach is incorporated and developed alongside AMP\textittorch, an open-source machine learning potential package, along with interactive Google Colab notebook examples.
@article{10.1088/2632-2153/abcc44,title={Enabling robust offline active learning for machine learning potentials using simple physics-based priors},author={Shuaibi, Muhammed and Sivakumar, Saurabh and Chen, Rui Qi and Ulissi, Zachary W},journal={Machine Learning: Science and Technology},year={2021},month=dec,doi={10.1088/2632-2153/abcc44},url={https://doi.org/10.1088/2632-2153/abcc44},}
2020
Catalysis, Molecules
Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces
@article{tran2020methods,title={Methods for comparing uncertainty quantifications for material property predictions},author={Tran, Kevin and Neiswanger, Willie and Yoon, Junwoong and Zhang, Qingyang and Xing, Eric and Ulissi, Zachary W},journal={Machine Learning: Science and Technology},year={2020},month=may,volume={1},number={2},pages={025006},publisher={IOP Publishing},doi={10.1088/2632-2153/ab7e1a},url={https://doi.org/10.1088/2632-2153/ab7e1a},}
AI/ML Models, Catalysis
Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution
Eric M Lopato, Emily A Eikey, Zoe C Simon, and 8 more authors
@article{lopato2020parallelized,title={Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution},author={Lopato, Eric M and Eikey, Emily A and Simon, Zoe C and Back, Seoin and Tran, Kevin and Lewis, Jacqueline and Kowalewski, Jakub F and Yazdi, Sadegh and Kitchin, John R and Ulissi, Zachary W and others},journal={ACS Catalysis},year={2020},month=mar,volume={10},number={7},pages={4244--4252},publisher={ACS Publications},doi={10.1021/acscatal.9b05404},url={https://doi.org/10.1021/acscatal.9b05404},}
AI/ML Models
Computational Notebooks in Chemical Engineering Curricula
Jonathan Verrett, Fani Boukouvala, Alexander Dowling, and 2 more authors
@article{verrett2020computational,title={Computational Notebooks in Chemical Engineering Curricula},author={Verrett, Jonathan and Boukouvala, Fani and Dowling, Alexander and Ulissi, Zachary and Zavala, Victor},journal={Chemical Engineering Education},year={2020},month=jul,volume={54},number={3},pages={143--150},doi={10.18260/2-1-370.660-116661},url={https://doi.org/10.18260/2-1-370.660-116661},}
Catalysis, Inorganic Materials
Accelerated discovery of CO2 electrocatalysts using active machine learning
Miao Zhong, Kevin Tran, Yimeng Min, and 19 more authors
The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3-8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9-16, and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.
@article{zhong2020accelerated,title={Accelerated discovery of CO2 electrocatalysts using active machine learning},author={Zhong, Miao and Tran, Kevin and Min, Yimeng and Wang, Chuanhao and Wang, Ziyun and Dinh, Cao-Thang and De Luna, Phil and Yu, Zongqian and Rasouli, Armin Sedighian and Brodersen, Peter and Sun, Song and Voznyy, Oleksandr and Tan, Chih-Shan and Askerka, Mikhail and Che, Fanglin and Liu, Min and Seifitokaldani, Ali and Pang, Yuanjie and Lo, Shen-Chuan and Ip, Alexander and Ulissi, Zachary and Sargent, Edward H.},journal={Nature},year={2020},month=may,volume={581},number={7807},pages={178--183},doi={10.1038/s41586-020-2242-8},url={https://doi.org/10.1038/s41586-020-2242-8},issn={1476-4687},refid={Zhong2020}}
AI/ML Models, Catalysis
Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening
Geun Ho Gu, Juhwan Noh, Sungwon Kim, and 3 more authors
The Journal of Physical Chemistry Letters, Mar 2020
@article{gu2020practical,title={Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening},author={Gu, Geun Ho and Noh, Juhwan and Kim, Sungwon and Back, Seoin and Ulissi, Zachary and Jung, Yousung},journal={The Journal of Physical Chemistry Letters},year={2020},month=mar,volume={11},pages={3185--3191},publisher={ACS Publications},doi={10.1021/acs.jpclett.0c00634},url={https://doi.org/10.1021/acs.jpclett.0c00634},}
Catalysis
Discovery of Acid-Stable Oxygen Evolution Catalysts: High-throughput Computational Screening of Equimolar Bimetallic Oxides
@article{yoon2020differentiable,title={Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)},author={Yoon, Junwoong and Ulissi, Zachary W},journal={Physical Review Letters},year={2020},volume={125},number={17},pages={173001},publisher={APS},doi={10.1103/PhysRevLett.125.173001},url={https://doi.org/10.1103/PhysRevLett.125.173001},}
Catalysis
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
C Lawrence Zitnick, Lowik Chanussot, Abhishek Das, and 8 more authors
@article{zitnick2020introduction,title={An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage},author={Zitnick, C Lawrence and Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Lavril, Thibaut and Palizhati, Aini and Riviere, Morgane and others},journal={arXiv preprint arXiv:2010.09435},year={2020},doi={10.48550/arXiv.2010.09435},url={https://doi.org/10.48550/arXiv.2010.09435},}
ML Datasets, Catalysis
In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation
Seoin Back, Jonggeol Na, Kevin Tran, and 1 more author
Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*), respectively, we evaluate multiple aspects of catalysts to discover active, stable, CO-tolerant, and cost-effective hydrogen evolution and oxidation catalysts. Finally, we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability, bridging the gap between simulations and experiments. Furthermore, it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials.
@article{back2020in_rqnD,title={In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation},author={Back, Seoin and Na, Jonggeol and Tran, Kevin and Ulissi, Zachary W},journal={Physical Chemistry Chemical Physics},year={2020},volume={22},number={35},pages={19454-19458},publisher={The Royal Society of Chemistry},url={https://pubs.rsc.org/cp/article-abstract/22/35/19454/680095},}
2019
AI/ML Models, Catalysis
Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
Seoin Back, Junwoong Yoon, Nianhan Tian, and 3 more authors
The Journal of Physical Chemistry Letters, Jul 2019
@article{back2019convolutional,title={Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts},author={Back, Seoin and Yoon, Junwoong and Tian, Nianhan and Zhong, Wen and Tran, Kevin and Ulissi, Zachary W.},journal={The Journal of Physical Chemistry Letters},year={2019},month=jul,volume={10},number={15},pages={4401-4408},doi={10.1021/acs.jpclett.9b01428},url={https://doi.org/10.1021/acs.jpclett.9b01428},}
Catalysis
Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
@article{back2019towards,title={Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning},author={Back, Seoin and Tran, Kevin and Ulissi, Zachary W.},journal={ACS Catalysis},year={2019},month=jul,volume={0},number={0},pages={7651-7659},doi={10.1021/acscatal.9b02416},url={https://doi.org/10.1021/acscatal.9b02416},}
ML Datasets, AI/ML Models, Inorganic Materials
Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks
Aini Palizhati, Wen Zhong, Kevin Tran, and 2 more authors
Journal of Chemical Information and Modeling, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies ab initio using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energies with a mean absolute test error of 0.0082 eV/Å2 and can qualitatively predict surface energies.
@article{palizhati2019toward,title={Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks},author={Palizhati, Aini and Zhong, Wen and Tran, Kevin and Back, Seoin and Ulissi, Zachary W.},journal={Journal of Chemical Information and Modeling},year={2019},volume={59},number={11},pages={4742--4749},publisher={American Chemical Society},doi={10.1021/acs.jcim.9b00550},url={https://doi.org/10.1021/acs.jcim.9b00550},}
Nanotechnology
Optimization-Based Design of Active and Stable Nanostructured Surfaces
Christopher L. Hanselman, Wen Zhong, Kevin Tran, and 2 more authors
@article{doi:10.1021/acs.jpcc.9b08431,title={Optimization-Based Design of Active and Stable Nanostructured Surfaces},author={Hanselman, Christopher L. and Zhong, Wen and Tran, Kevin and Ulissi, Zachary W. and Gounaris, Chrysanthos E.},journal={The Journal of Physical Chemistry C},year={2019},volume={123},number={48},pages={29209-29218},doi={10.1021/acs.jpcc.9b08431},url={https://doi.org/10.1021/acs.jpcc.9b08431},}
2018
Machine Learning
Dynamic workflows for routine materials discovery in surface science
Kevin Tran, Aini Palizhati, Seoin Back, and 1 more author
Journal of Chemical Information and Modeling, 2018
@article{tran2018dynamic,title={Dynamic workflows for routine materials discovery in surface science},author={Tran, Kevin and Palizhati, Aini and Back, Seoin and Ulissi, Zachary W},journal={Journal of Chemical Information and Modeling},year={2018},volume={58},number={12},pages={2392--2400},publisher={ACS Publications},doi={10.1021/acs.jcim.8b00386},url={https://doi.org/10.1021/acs.jcim.8b00386},}
ML Datasets, AI/ML Models, Catalysis, Inorganic Materials
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
@article{tran2018active,title={Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution},author={Tran, Kevin and Ulissi, Zachary W.},journal={Nature Catalysis},year={2018},month=sep,volume={1},number={9},pages={696},publisher={Nature Publishing Group},doi={10.1038/s41929-018-0142-1},url={https://doi.org/10.1038/s41929-018-0142-1},}
Catalysis
Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes
Drew Higgins, Melissa Wette, Brenna M. Gibbons, and 10 more authors
@article{doi:10.1021/acsaem.8b00090,title={Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes},author={Higgins, Drew and Wette, Melissa and Gibbons, Brenna M. and Siahrostami, Samira and Hahn, Christopher and Escudero-Escribano, Marıa and Garcia-Melchor, Max and Ulissi, Zachary W. and Davis, Ryan C. and Mehta, Apurva and Clemens, Bruce M. and N\o{}rskov, Jens K. and Jaramillo, Thomas F.},journal={ACS Applied Energy Materials},year={2018},month=may,doi={10.1021/acsaem.8b00090},url={https://doi.org/10.1021/acsaem.8b00090},}
AI/ML Models
Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment
Michael T. Tang, Zachary W. Ulissi, and Karen Chan
@article{doi:10.1021/acs.jpcc.8b02094,title={Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment},author={Tang, Michael T. and Ulissi, Zachary W. and Chan, Karen},journal={The Journal of Physical Chemistry C},year={2018},volume={122},number={26},pages={14481-14487},doi={10.1021/acs.jpcc.8b02094},url={https://doi.org/10.1021/acs.jpcc.8b02094},}
2017
Catalysis
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Zachary W. Ulissi, A. J. Medford, Thomas Bligaard, and 1 more author
@article{bib:networkreduction,title={To address surface reaction network complexity using scaling relations machine learning and DFT calculations},author={Ulissi, Zachary W. and Medford, A. J. and Bligaard, Thomas and N\o{}rskov, Jens K.},journal={Nature Communications},year={2017},month=mar,volume={8},doi={10.1038/ncomms14621},url={https://doi.org/10.1038/ncomms14621},owner={zulissi},timestamp={2017.01.05}}
Catalysis
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi, Michael T. Tang, Jianping Xiao, and 9 more authors
@article{doi:10.1021/acscatal.7b01648,title={Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction},author={Ulissi, Zachary W. and Tang, Michael T. and Xiao, Jianping and Liu, Xinyan and Torelli, Daniel A. and Karamad, Mohammadreza and Cummins, Kyle and Hahn, Christopher and Lewis, Nathan S. and Jaramillo, Thomas F. and Chan, Karen and N\o{}rskov, Jens K.},journal={ACS Catalysis},year={2017},month=oct,volume={7},number={10},pages={6600-6608},doi={10.1021/acscatal.7b01648},url={https://doi.org/10.1021/acscatal.7b01648},}
Biochemistry
Polarimetric accessory for colposcope
Amir Gandjbakhche, Victor Chernomordik, Moinuddin Hassan, and 4 more authors
A polarization-based colposcopy apparatus includes a polar ization exchanging beam splitter pair oriented so that s-and p-polarizations are exchanged. An optical flux from a speci men is directed through the pair, and orthogonal components thereof are alternately or selectively coupled to an array detector. The detected images are processed based on image correlations to reveal specimen structures.
@article{gandjbakhche2017polarimetric_as0K,title={Polarimetric accessory for colposcope},author={Gandjbakhche, Amir and Chernomordik, Victor and Hassan, Moinuddin and Sviridov, Alexander and Ulissi, Zachary and Smith, Paul D and Boccara, Albert C},year={2017},url={https://patents.google.com/patent/US9801536B2/en},}
2016
Inorganic Materials, Nanotechnology
Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain
Zachary W Ulissi, Ananth Govind Rajan, and Michael S Strano
@article{ulissi2016persistently,title={Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain},author={Ulissi, Zachary W and Govind Rajan, Ananth and Strano, Michael S},journal={ACS Nano},year={2016},volume={10},number={8},pages={7542--7549},publisher={American Chemical Society},doi={10.1021/acsnano.6b02512},url={https://doi.org/10.1021/acsnano.6b02512},}
AI/ML Models, Catalysis
Automated Discovery and Construction of Surface Phase Diagrams using Machine Learning
Zachary W Ulissi, Aayush R Singh, Charlie Tsai, and 1 more author
Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO2(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS2 surface.
@article{ulissi2016automated,title={Automated Discovery and Construction of Surface Phase Diagrams using Machine Learning},author={Ulissi, Zachary W and Singh, Aayush R and Tsai, Charlie and N\o{}rskov, Jens K.},journal={The Journal of Physical Chemistry Letters},year={2016},publisher={American Chemical Society},doi={10.1021/acs.jpclett.6b01254},url={https://doi.org/10.1021/acs.jpclett.6b01254},}
Corona phase molecular recognition (CoPhMoRe) is a new technique that generates a nanoparticle-coupled polymer phase, capable of recognizing a specific molecule with high affinity and selectivity. CoPhMoRe has been successfully demonstrated using polymer wrapped single walled carbon nanotubes, resulting in molecular recognition complexes, to date, for dopamine, estradiol, riboflavin, and l-thyroxine, utilizing combinatorial library screening. A rational alternative design to this empirical library screening is to solve the mathematical formulation that we introduce as the CoPhMoRe inverse problem. This inverse problem seeks a linear function representing the position of monomers or functional groups along a polymer backbone that results in a 3-dimensional structure capable of recognizing a specific molecule when mapped to a nanoparticle surface. The potential solution space for such an inverse problem is infinite in general, but for the specific constraint of a helically wrapping polymer, mapped to a cylindrical nanoparticle, we show in this work that two types of inverse problems are exactly solvable. In one case, the polymer pitch and composition can be designed to allow for the specific binding of a small molecule analyte in the occluded space on the nanotube surface. In the other, a larger macromolecule can interact with a deformed helix, which partially conforms to it. A simplified, coarse-grained molecular model of a helically wrapping polymer demonstrates the inhomogeneous binding potential formed by a wrapping with a given pitch. Calculating the potential maps for various pitch values illustrates that there is an optimal pitch that enables the selective and specific binding of the target analyte. An additional coarse-grained model of a helical wrapping by a polymer consisting of alternating hydrophobic–hydrophilic segments demonstrates the resulting deformed helix corona around the nanotube, which forms accessible binding pockets between the hydrophilic loops. While these are the idealized forms of actual CoPhMoRe phases, the formation and solution of such inverse problems 5 serve to reduce the dimensionality of library screening for CoPhMoRe discoveries, as well as provide a theoretical basis for understanding certain types of CoPhMoRe recognition.
@article{bisker2015mathematical,title={A Mathematical Formulation and Solution of the CoPhMoRe Inverse Problem for Helically Wrapping Polymer Corona Phases on Cylindrical Substrates},author={Bisker, Gili and Ahn, Jiyoung and Kruss, Sebastian and Ulissi, Zachary W and Salem, Daniel P and Strano, Michael S},journal={The Journal of Physical Chemistry C},year={2015},publisher={American Chemical Society},doi={10.1021/acs.jpcc.5b01705},url={https://doi.org/10.1021/acs.jpcc.5b01705},owner={zulissi},timestamp={2015.05.19}}
AI/ML Models, Biochemistry, Nanotechnology
A 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces
Zachary W. Ulissi, Jingqing Zhang, Vishnu Sresht, and 2 more authors
@article{bib:2deos,title={A 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces},author={Ulissi, Zachary W. and Zhang, Jingqing and Sresht, Vishnu and Blankschtein, Daniel and Strano, Michael S.},journal={Langmuir},year={2015},volume={31},number={1},pages={628–636},doi={10.1021/la503899e},url={https://doi.org/10.1021/la503899e},owner={zulissi},timestamp={2014.12.03}}
Shrinking sensors to the nanoscale introduces novel selectivity mechanisms and enables the ultimate sensitivity limit, single-molecule detection. Single-walled carbon nanotubes, with a bright fluorescence signal and no photobleaching, are a platform for implantable near-IR sensors capable of selectively detecting a range of small-molecules including the radical signalling molecule nitric oxide, the hormone estradiol, and sugars such as glucose. Selectivity is achieved by engineering an adsorbed phase of polymers, DNA, or surfactants at the nanotube/solution interface. Understanding these sensors requires a range of modeling and simulation tools and presents a unique opportunity to learn how these phases interact with small molecules. This thesis work discusses methods and limits to integrating data from many noisy stochastic sensors, show how these sensors can be used to monitor nitric oxide inside cells with unprecedented spatiotemporal resolution, and describes what is needed to engineer a selective adsorbed phase. In addition, another method of stochastic detection is described based on the stochastic ionic pore-blocking of transport inside individual single-walled carbon nanotubes. We discuss the current state-of-the-art for making and analysing devices with a single nanometer-scale pore, which necessarily leads to stochastic transport fluctuations. We also present work on the analysis on many devices with single characterized SWCNT pores. A maximum in transport rates inside SWCNTs with diameters of approximately 1.6 nm is shown and discussed, with implications for how we model transport at this scale and the design of …
@article{ulissi2015modeling_idth,title={Modeling and simulation of stochastic phenomena in carbon nanotube-based single molecule sensors},author={Ulissi, Zachary Ward},year={2015},url={https://dspace.mit.edu/handle/1721.1/98716},}
2014
AI/ML Models, Nanotechnology
Deterministic modelling of carbon nanotube near-infrared solar cells
Darin O. Bellisario, Rishabh M. Jain, Zachary W. Ulissi, and 1 more author
@article{C4EE01765J,title={Deterministic modelling of carbon nanotube near-infrared solar cells},author={Bellisario, Darin O. and Jain, Rishabh M. and Ulissi, Zachary W. and Strano, Michael S.},journal={Energy Environ. Sci.},year={2014},volume={7},pages={3769-3781},issue={11},publisher={The Royal Society of Chemistry},doi={10.1039/C4EE01765J},url={https://doi.org/10.1039/C4EE01765J},owner={zulissi},timestamp={2014.12.11}}
Recently, several important advances in techniques for the separation of single-walled carbon nanotubes (SWNTs) by chiral index have been developed. These new methods allow for the separation of SWNTs through selective adsorption and desorption of different (n,m) chiral indices to and from a specific hydrogel. Our group has previously developed a kinetic model for the chiral elution order of separation; however, the underlying mechanism that allows for this separation remains unknown. In this work, we develop a quantitative theory that provides the first mechanistic insights for the separation order and binding kinetics of each SWNT chirality (n,m) based on the surfactant-induced, linear charge density, which we find ranges from 0.41 e–/nm for (7,3) SWNTs in 17 mM sodium dodecyl sulfate (SDS) to 3.32 e–/nm for (6,5) SWNTs in 105 mM SDS. Adsorption onto the hydrogel support is balanced by short-distance hard-surface and long-distance electrostatic repulsive SWNT/substrate forces, the latter of which we postulate is strongly dependent on surfactant concentration and ultimately leads to gel-based single-chirality semiconducting SWNT separation. These molecular-scale properties are derived using bulk-phase, forward adsorption rate constants for each SWNT chirality in accordance with our previously published model. The theory developed here quantitatively describes the experimental elution profiles of 15 unique SWNT chiralities as a function of anionic surfactant concentration between 17 and 105 mM, as well as phenomenological observations of the impact of varying preparatory conditions such as extent of ultrasonication and ultracentrifugation. We find that SWNT elution order and separation efficiency are primarily driven by the morphological change of SDS surfactant wrapping on the surface of the nanotube, mediated by SWNT chirality and the ionic strength of the surrounding medium. This work provides a foundational understanding for high-purity, preparative-scale separation of as-produced SWNT mixtures into isolated, single-chirality fractions.
@article{doi:10.1021/nn4058402,title={Quantitative Theory of Adsorptive Separation for the Electronic Sorting of Single-Walled Carbon Nanotubes},author={Jain, Rishabh M. and Tvrdy, Kevin and Han, Rebecca and Ulissi, Zachary W. and Strano, Michael S.},journal={ACS Nano},year={2014},volume={8},number={4},pages={3367-3379},doi={10.1021/nn4058402},url={https://doi.org/10.1021/nn4058402},owner={zulissi},timestamp={2014.07.12}}
Biochemistry, Nanotechnology
Spatiotemporal Intracellular Nitric Oxide Signaling Captured using Internalized, Near Infrared Fluorescent Carbon Nanotube Nanosensors
Zachary W. Ulissi, Fatih Sen, Xun Gong, and 7 more authors
Fluorescent nanosensor probes have suffered from limited molecular recognition and a dearth of strategies for spatial-temporal operation in cell culture. In this work, we spatially imaged the dynamics of nitric oxide (NO) signaling, important in numerous pathologies and physiological functions, using intracellular near-infrared fluorescent single-walled carbon nanotubes. The observed spatial-temporal NO signaling gradients clarify and refine the existing paradigm of NO signaling based on averaged local concentrations. This work enables the study of transient intracellular phenomena associated with signaling and therapeutics.
@article{bib:jsk,title={Spatiotemporal Intracellular Nitric Oxide Signaling Captured using Internalized, Near Infrared Fluorescent Carbon Nanotube Nanosensors},author={Ulissi, Zachary W. and Sen, Fatih and Gong, Xun and Sen, Selda and Iverson, Nicole and Boghossian, Ardemis A. and Godoy, Luiz and Wogan, Gerald and Mukhopadhyay, D. and Strano, Michael S.},journal={Nano Letters},year={2014},volume={14},pages={4887-4894},doi={10.1021/nl502338y},url={https://doi.org/10.1021/nl502338y},owner={zulissi},timestamp={2014.07.12}}
Nanotechnology
Low Dimensional Carbon Materials for Applications in Mass and Energy Transport
Qing Hua Wang, Darin O. Bellisario, Lee W. Drahushuk, and 7 more authors
Low dimensional materials are those that possess at least one physical boundary small enough to confine the electrons or phonons. This quantum confinement reduces the dimensionality of the material and imparts unique and novel properties that are not seen in their bulk forms. Examples include quantum dots (0-D), carbon nanotubes (1-D), and graphene (2-D). Accordingly, these materials exhibit new concepts in mass and energy transport that can be exploited for technological applications. In this Perspective, we review several topics related to mass and energy transport in and around carbon-based low dimensional materials. Recent developments in the study of matter being transported through carbon nanotube and graphene nanopores are reviewed, as well as applications of excitonic, thermal, and electronic energy transport in carbon nanotubes. The nanometer-scale interior of a single-walled carbon nanotube (SWCNT) has been studied as a unique nanopore, exhibiting periodic ionic conduction currents and dimensionally confined material phases. The mechanism of gas transport through atomic-scale holes in graphene, which is otherwise a perfect barrier material, has been analytically studied. These insights on nanoscale mass transport will have important implications in systems ranging from biological nanopores to advanced water filtration devices. The electronic structure of semiconducting SWCNTs allows photogenerated excitons to be harnessed for single-molecule biosensing and as elements of a new class of all-nanocarbon near-infrared photovoltaics. The extremely high thermal and electrical conductivities of carbon nanotubes allows the generation of electrical energy from chemical reactions. The understanding of how low dimensional physics and chemistry influences mass and energy transport will facilitate the application of these materials to a variety of scientific challenges.
@article{RefWorks:1037,title={Low Dimensional Carbon Materials for Applications in Mass and Energy Transport},author={Wang, Qing Hua and Bellisario, Darin O. and Drahushuk, Lee W. and Jain, Rishabh M. and Kruss, Sebastian and Landry, Markita P. and Mahajan, Sayalee G. and Shimizu, Steven F. E. and Ulissi, Zachary W. and Strano, Michael S.},journal={Chemistry of Materials},year={2014},month=jan,volume={26},number={1},pages={172-183},doi={10.1021/cm402895e},url={https://doi.org/10.1021/cm402895e},}
2013
AI/ML Models, Nanotechnology
A Quantitative and Predictive Model of Electromigration-Induced Breakdown of Metal Nanowires
Darin O. Bellisario, Zachary W. Ulissi, and Michael S. Strano
An isothermal model of electromigration breakdown of metal nanowires 80-700 nm in diameter is developed and validated using experimental data obtained from isolated cylindrical Au nanowires. The model considers electromigration from an applied current producing a net flux of metal atoms, reducing the nanowire radius and conductivity precipitously and accounting for both mass and electronic carrier transport. The model successfully predicts the observed critical failure current, the correct scaling with nanowire radius to 3/2 power, and the impedance evolution prior to breakdown. Application to the case where feedback control is employed to limit the rate of nanowire thinning reproduces key features, including slowed necking, a threshold current and voltage after which lower bias is required to advance formation, and the dependence of these values on feedback parameters.
@article{ISI:000320640500056,title={A Quantitative and Predictive Model of Electromigration-Induced Breakdown of Metal Nanowires},author={Bellisario, Darin O. and Ulissi, Zachary W. and Strano, Michael S.},journal={Journal of Physical Chemistry C},year={2013},month=jun,volume={117},number={23},pages={12373--12378},doi={10.1021/jp40357761},url={https://doi.org/10.1021/jp40357761},issn={1932-7447},unique-id={ISI:000320640500056}}
Nanotechnology
Charge Transfer at Junctions of a Single Layer of Graphene and a Metallic Single Walled Carbon Nanotube
Geraldine L. C. Paulus, Qing Hua Wang, Zachary W. Ulissi, and 5 more authors
Junctions between a single walled carbon nanotube (SWNT) and a monolayer of graphene are fabricated and studied for the first time. A single layer graphene (SLG) sheet grown by chemical vapor deposition (CVD) is transferred onto a SiO2/Si wafer with aligned CVD-grown SWNTs. Raman spectroscopy is used to identify metallic-SWNT/SLG junctions, and a method for spectroscopic deconvolution of the overlapping G peaks of the SWNT and the SLG is reported, making use of the polarization dependence of the SWNT. A comparison of the Raman peak positions and intensities of the individual SWNT and graphene to those of the SWNT-graphene junction indicates an electron transfer of 1.12 x 1013 cm-2 from the SWNT to the graphene. This direction of charge transfer is in agreement with the work functions of the SWNT and graphene. The compression of the SWNT by the graphene increases the broadening of the radial breathing mode (RBM) peak from 3.6 +/- 0.3 to 4.6 +/- 0.5 cm-1 and of the G peak from 13 +/- 1 to 18 +/- 1 cm-1, in reasonable agreement with molecular dynamics simulations. However, the RBM and G peak position shifts are primarily due to charge transfer with minimal contributions from strain. With this method, the ability to dope graphene with nanometer resolution is demonstrated.
@article{ISI:000319833700012,title={Charge Transfer at Junctions of a Single Layer of Graphene and a Metallic Single Walled Carbon Nanotube},author={Paulus, Geraldine L. C. and Wang, Qing Hua and Ulissi, Zachary W. and McNicholas, Thomas P. and Vijayaraghavan, Aravind and Shih, Chih-Jen and Jin, Zhong and Strano, Michael S.},journal={Small},year={2013},month=jun,volume={9},number={11},pages={1954--1963},doi={10.1002/smll.201201034},url={https://doi.org/10.1002/smll.201201034},issn={1613-6810},orcid-numbers={Vijayaraghavan, Aravind/0000-0001-8289-2337},researcherid-numbers={Vijayaraghavan, Aravind/E-1087-2011 Jin, Zhong/D-1742-2012 Shih, Chih-Jen/B-1185-2013},unique-id={ISI:000319833700012}}
Nanotechnology
Stochastic Pore Blocking and Gating in PDMS-Glass Nanopores from Vapor-Liquid Phase Transitions
Steven Shimizu, Mark Ellison, Kimberly Aziz, and 5 more authors
Polydimethylsiloxane (PDMS) is commonly used in research for microfluidic devices and for making elastomeric stamps for soft lithography. Its biocompatibility and nontoxicitiy also allow it to be used in personal care, food, and medical products. Herein we report a phenomenon observed when patch clamp, a technique normally used to study biological ion channels, is performed on both grooved and planar PDMS surfaces, resulting in stochastic current fluctuations that are due to a nanopore being formed at the interface of the PDMS and glass surfaces and being randomly blocked. Deformable pores between 1.9 +/- 0.7 and 7.4 +/- 2.1 nm in diameter, depending on the calculation method, form upon patching to the surface. Coulter blocking and nanoprecipitation are ruled out, and we instead propose a mechanism of stochastic current fluctuations arising from transitions between vapor and liquid phases, consistent with similar observations and theory from statistical mechanics literature. Interestingly, we find that [Ru(bpy)(3)](2+), a common probe molecule employed in nanopore research, physisorbs inside these hydrophobic nanopores blocking all ionic current flow at concentrations higher than 1 X 10(-4) M, despite the considerably larger pore diameter relative to the molecule. Patch clamp methods are promising for the study of stochastic current fluctuations and other transport phenomenon in synthetic nanopore systems.
@article{ISI:000319649100015,title={Stochastic Pore Blocking and Gating in PDMS-Glass Nanopores from Vapor-Liquid Phase Transitions},author={Shimizu, Steven and Ellison, Mark and Aziz, Kimberly and Wang, Qing Hua and Ulissi, Zachary W. and Gunther, Zachary and Bellisario, Darin and Strano, Michael},journal={Journal of Physical Chemistry C},year={2013},month=may,volume={117},number={19},pages={9641--9651},doi={10.1021/jp312659m},url={https://doi.org/10.1021/jp312659m},issn={1932-7447},unique-id={ISI:000319649100015}}
Biochemistry, Nanotechnology
Control of nano and microchemical systems
Zachary W. Ulissi, Michael S. Strano, and Richard D. Braatz
Many advances in the development of nano and microchemical systems have occurred in the last decade. These systems have significant associated identification and control challenges, including high state dimensionality, limitations in real-time measurements and manipulated variables, and significant uncertainties described by non-Gaussian distributions. Some strategies for addressing these challenges are summarized, which include exploiting structure within the stochastic Master equations that describe molecular interactions, manipulating molecular bonds at system boundaries, and manipulating molecules and nanoscale objects through magnetic and electric fields. The strategies are illustrated in a variety of applications that include the estimation of nucleation kinetics of protein and pharmaceutical crystals within fluidic devices, the estimation of concentration fields using DNA-wrapped single-walled carbon nanotube-based sensor arrays, the simultaneous control of nanoscale geometry and electrical activation during thermal annealing in a semiconductor material, and the control of nanostructure formation on surfaces. Promising directions for research and technology development are identified for the next decade. (C) 2012 Elsevier Ltd. All rights reserved.
@article{ISI:000314993000014,title={Control of nano and microchemical systems},author={Ulissi, Zachary W. and Strano, Michael S. and Braatz, Richard D.},journal={Computers \& Chemical Engineering},year={2013},month=apr,volume={51},number={SI},pages={149-156},doi={10.1016/j.compchemeng.2012.07.004},url={https://doi.org/10.1016/j.compchemeng.2012.07.004},issn={0098-1354},unique-id={ISI:000314993000014}}
Nanotechnology
Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes
Wonjoon Choi, Zachary W Ulissi, Steven FE Shimizu, and 3 more authors
@article{choi2013diameter,title={Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes},author={Choi, Wonjoon and Ulissi, Zachary W and Shimizu, Steven FE and Bellisario, Darin O and Ellison, Mark D and Strano, Michael S},journal={Nature Communications},year={2013},volume={4},pages={2397},publisher={Nature Publishing Group},doi={10.1038/ncomms3397},url={https://doi.org/10.1038/ncomms3397},}
Understanding molecular recognition is of fundamental importance in applications such as therapeutics, chemical catalysis and sensor design. The most common recognition motifs involve biological macromolecules such as antibodies and aptamers. The key to biorecognition consists of a unique three-dimensional structure formed by a folded and constrained bioheteropolymer that creates a binding pocket, or an interface, able to recognize a specific molecule. Here, we show that synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymer-nanotube recognition complexes for riboflavin, L-thyroxine and oestradiol. In each case, the recognition was predicted using a two-dimensional thermodynamic model of surface interactions in which the dissociation constants can be tuned by perturbing the chemical structure of the heteropolymer. Moreover, these complexes can be used as new types of spatiotemporal sensors based on modulation of the carbon nanotube photoemission in the near-infrared, as we show by tracking riboflavin diffusion in murine macrophages.
@article{ISI:000327943400026,title={Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes},author={Zhang, Jingqing and Landry, Markita P. and Barone, Paul W. and Kim, Jong-Ho and Lin, Shangchao and Ulissi, Zachary W. and Lin, Dahua and Mu, Bin and Boghossian, Ardemis A. and Hilmer, Andrew J. and Rwei, Alina and Hinckley, Allison C. and Kruss, Sebastian and Shandell, Mia A. and Nair, Nitish and Blake, Steven and Sen, Fatih and Sen, Selda and Croy, Robert G. and Li, Deyu and Yum, Kyungsuk and Ahn, Jin-Ho and Jin, Hong and Heller, Daniel A. and Essigmann, John M. and Blankschtein, Daniel and Strano, Michael S.},journal={Nature Nanotechnology},year={2013},month=dec,volume={8},number={12},pages={959--968},doi={10.1038/NNANO.2013.236},url={https://doi.org/10.1038/NNANO.2013.236},issn={1748-3387},unique-id={ISI:000327943400026}}
2012
AI/ML Models, Catalysis
Modelling and development of photoelectrochemical reactor for H-2 production
C. Carver, Zachary W. Ulissi, C. K. Ong, and 3 more authors
International Journal of Hydrogen Energy, Feb 2012
Photoelectrolysis of aqueous solutions, using one or more semiconducting electrodes in a photoelectrochemical reactor, is a potentially attractive process for hydrogen production because of its prospectively high energy efficiency, simplicity and potentially low cost. The design requirements and preliminary results of modelling a photoelectrochemical (PEC) reactor are described. Potential and current density distributions, due to ohmic potential losses in thin (non-photo) anodes on poorly conducting fluoride-doped tin oxide coated glass substrates, were modelled. The predicted current densities decayed rapidly from the terminals at the edges, towards the centre of a 0.1 x 0.1 m(2) anode, so limiting scale-up with such substrates. Spatial distributions of dissolved oxygen concentrations were also modelled, aiming to define operating conditions that would avoid forming bubbles, which reflect light specularly decreasing photon absorption efficiencies of photoelectrodes. The implications for the future optimization of the reactor are discussed. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
@article{ISI:000301157300094,title={Modelling and development of photoelectrochemical reactor for H-2 production},author={Carver, C. and Ulissi, Zachary W. and Ong, C. K. and Dennison, S. and Kelsall, G. H. and Hellgardt, K.},journal={International Journal of Hydrogen Energy},year={2012},month=feb,volume={37},number={3},pages={2911--2923},doi={10.1016/j.ijhydene.2011.07.012},url={https://doi.org/10.1016/j.ijhydene.2011.07.012},issn={0360-3199},unique-id={ISI:000301157300094}}
Single-molecule fluorescent microscopy allows semiconducting single-walled carbon nanotubes (SWCNTs) to detect the adsorption and desorption of single adsorbate molecules as a stochastic modulation of emission intensity. In this study, we identify and assign the signature of the complex decomposition and reaction pathways of riboflavin in the presence of the free radical scavenger Trolox using DNA-wrapped SWCNT sensors dispersed onto an aminopropyltriethoxysilane (APTES) coated surface. SWCNT emission is quenched by riboflavin-induced reactive oxygen species (ROS), but increases upon the adsorption of Trolox, which functions as a reductive brightening agent. Riboflavin has two parallel reaction pathways, a Trolox oxidizer and a photosensitizer for singlet oxygen and superoxide generation. The resulting reaction network can be detected in real time in the vicinity of a single SWCNT and can be completely described using elementary reactions and kinetic rate constants measured independently. The reaction mechanism results in an oscillatory fluorescence response from each SWCNT, allowing for the simultaneous detection of multiple reactants. A series-parallel kinetic model is shown to describe the critical points of these oscillations, with partition coefficients on the order of 10(6) - 10(4) for the reactive oxygen and excited state species. These results highlight the potential for SWCNTs to characterize complex reaction networks at the nanometer scale.
@article{ISI:000312563600024,title={Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy},author={Sen, Fatih and Boghossian, Ardemis A. and Sen, Selda and Ulissi, Zachary W. and Zhang, Jingqing and Strano, Michael S.},journal={ACS Nano},year={2012},month=dec,volume={6},number={12},pages={10632--10645},doi={10.1021/nn303716n},url={https://doi.org/10.1021/nn303716n},issn={1936-0851},unique-id={ISI:000312563600024}}
Nanotechnology
Systems nanotechnology: Identification, estimation, and control of nanoscale systems
Zachary W Ulissi, Mark C Molaro, Michael S Strano, and 1 more author
Technological advancements in synthesizing devices at the nanoscale has produced a variety of analogues to classical systems elements, such as sensors, actuators, and control systems. We review recent experimental literature on nanoscale devices that have been produced to have useful systems functionality, as well as efforts to employ these devices in identification, estimation, and control. The integration of these components is also discussed.
@article{ulissi2012systems_v_tt,title={Systems nanotechnology: Identification, estimation, and control of nanoscale systems},author={Ulissi, Zachary W and Molaro, Mark C and Strano, Michael S and Braatz, Richard D},journal={2012 American Control Conference (ACC)},year={2012},pages={1-7},publisher={IEEE},doi={10.1109/acc.2012.6314855},url={https://doi.org/10.1109/acc.2012.6314855},}
2011
Nanotechnology
The chemical dynamics of nanosensors capable of single-molecule detection
Ardemis A. Boghossian, Jingqing Zhang, François T. Le Floch-Yin, and 8 more authors
@article{doi:10.1063/1.3606496,title={The chemical dynamics of nanosensors capable of single-molecule detection},author={Boghossian, Ardemis A. and Zhang, Jingqing and Le Floch-Yin, François T. and Ulissi, Zachary W. and Bojo, Peter and Han, Jae-Hee and Kim, Jong-Ho and Arkalgud, Jyoti R. and Reuel, Nigel F. and Braatz, Richard D. and Strano, Michael S.},journal={The Journal of Chemical Physics},year={2011},volume={135},number={8},pages={084124},doi={10.1063/1.3606496},url={https://doi.org/10.1063/1.3606496},}
AI/ML Models, Catalysis
Effect of multiscale model uncertainty on identification of optimal catalyst properties
Zachary W. Ulissi, Vinay Prasad, and Dionisios Vlachos
Computer-based catalyst design has been a long standing dream of the chemistry community for replacing tedious and expensive experimental trial-and-error. While first-principle kinetic modeling emerges as a powerful tool for catalyst selection, it has mainly been limited to using a single catalyst descriptor, simplified chemical kinetic models, and assumptions that question the predictive capability of computational results in the absence of addressing the effect of error in kinetic parameters. Here, we introduce a new framework to address the effect of model uncertainty on optimal catalyst property identification. The framework is applied to the ammonia decomposition reaction for CO-free H(2) production for fuel cells. It is shown that a range of materials, rather than a single material, should be experimentally screened. Among kinetic model parameters, the often neglected adsorbate-adsorbate interactions can have a profound effect on catalyst selection. The importance of lateral interactions is confirmed with recent experimental data. (C) 2011 Elsevier Inc. All rights reserved.
@article{ISI:000293422100015,title={Effect of multiscale model uncertainty on identification of optimal catalyst properties},author={Ulissi, Zachary W. and Prasad, Vinay and Vlachos, Dionisios},journal={Journal of Catalysis},year={2011},month=jul,volume={281},number={2},pages={339--344},doi={10.1016/j.jcat.2011.05.019},url={https://doi.org/10.1016/j.jcat.2011.05.019},issn={0021-9517},unique-id={ISI:000293422100015}}
Nanotechnology
Carbon Nanotubes as Molecular Conduits: Advances and Challenges for Transport through Isolated Sub-2 nm Pores
Zachary W. Ulissi, Steven Shimizu, Chang Young Lee, and 1 more author
Devices that explore transport through the narrowest diameter single-walled carbon nanotubes (SWCNTs) have only recently been enabled by advances in SWCNT synthesis methods and experimental design. These devices hold promise as next-generation sensors, platforms for water desalination, proton conduction, energy storage, and to directly probe molecular transport under significant geometric confinement In this Perspective, we first describe this new generation of devices and then highlight two important concepts that have emerged from recent work. First, the most reliable way to identify transport is to borrow techniques from the biological and silicon nanopore communities and analyze the discrete stochastic events caused by molecules blocking the SWCNT channel. Second, it is nearly impossible to isolate mass transport within a SWCNT without a substantial suppression of leakage transport and around the SWCNT. To highlight this, we discuss experiments showing water transport along the exterior of SWCNTs. Finally, we describe some further innovations to these devices in the near future that will allow for a more complete understanding of confined molecular transport.
@article{ISI:000297195600011,title={Carbon Nanotubes as Molecular Conduits: Advances and Challenges for Transport through Isolated Sub-2~{nm} Pores},author={Ulissi, Zachary W. and Shimizu, Steven and Lee, Chang Young and Strano, Michael S.},journal={Journal of Physical Chemistry Letters},year={2011},month=nov,volume={2},number={22},pages={2892--2896},doi={10.1021/jz201136c},url={https://doi.org/10.1021/jz201136c},issn={1948-7185},researcherid-numbers={Lee, Chang Young/E-3793-2010},unique-id={ISI:000297195600011}}
In recent work, we have shown that d(AT)(15) DNA-wrapped single-walled carbon nanotubes (SWNTs) are able to detect the adsorption and desorption of single molecules of nitric oxide (NO) from the surface by quenching of the near-infrared fluorescence (Zhang et al. J. Am. Chem. Soc. 2011, 133, 567-581). A central question is how to estimate the local concentration from stochastic dynamics for these types of sensors. Herein, we employ an exact solution to the birth-death Markov model to estimate the local analyte concentration from the stochastic dynamics. Conditions are derived for the intrinsic variance displayed by identical sensor elements, and the homogeneity of the environment is assessed by comparing experimental sensor-to-sensor variance with this limit. We find that d(AT)(15) DNA-wrapped SWNTs demonstrate variances that are close to the idealized limit at relatively high NO concentrations (19.4 mu M). At 780 nM, the sensor-to-sensor variance is approximately double the idealized value, indicating marginal variation in the SWNT array. An NO adsorption coefficient of 2.6 x 10(-4) [mu M(-1)] is identified, and we outline how to predict the local analyte concentration from the sensor dynamics.
@article{ISI:000293191800009,title={Applicability of Birth-Death {Markov} Modeling for Single-Molecule Counting Using Single-Walled Carbon Nanotube Fluorescent Sensor Arrays},author={Ulissi, Zachary W. and Zhang, Jingqing and Boghossian, Ardemis A. and Reuel, Nigel F. and Shimizu, Steven F. E. and Braatz, Richard D. and Strano, Michael S.},journal={Journal of Physical Chemistry Letters},year={2011},month=jul,volume={2},number={14},pages={1690--1694},doi={10.1021/jz200572b},url={https://doi.org/10.1021/jz200572b},issn={1948-7185},unique-id={ISI:000293191800009}}
2010
AI/ML Models, Catalysis
High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia
Vinay Prasad, Ayman Karim, Zachary W. Ulissi, and 2 more authors
A novel approach for design of experiments (DOE) is outlined that combines high throughput multiscale modeling, sensitivity analysis, and information extraction from massive computational data using informatics tools. This approach is implemented by conducting experiments of ammonia decomposition on a Ru/gamma-Al(2)O(3) catalyst in a fixed bed reactor. It is shown that a relatively small number of experiments chosen from this new DOE approach can enable refinement of microkinetic models and render them predictive over the (large) experimentally important parameter space. Microkinetic models are subsequently used for process and product design. Specifically, a membrane fixed bed reactor is simulated and is shown to outperform the conventional fixed bed reactor at intermediate temperatures for hydrogen production. Also, the attributes of the best catalyst for ammonia decomposition are identified as a function of processing conditions. It is shown that for NH(3) decomposition, processing conditions do not significantly affect the best catalyst choice. In contrast, fundamental physicochemical phenomena, such as adsorbate adsorbate interactions, can have a profound effect on catalyst discovery. (C) 2009 Elsevier Ltd. All rights reserved.
@article{ISI:000276206700038,title={High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia},author={Prasad, Vinay and Karim, Ayman and Ulissi, Zachary W. and Zagrobelny, Megan and Vlachos, Dionisios},journal={Chemical Engineering Science},year={2010},month=jan,volume={65},number={1, SI},pages={240--246},doi={10.1016/j.ces.2009.05.054},url={https://doi.org/10.1016/j.ces.2009.05.054},issn={0009-2509},orcid-numbers={Karim, Ayman/0000-0001-7449-542X},researcherid-numbers={Karim, Ayman/G-6176-2012},unique-id={ISI:000276206700038}}
2008
Molecules, Biochemistry
Compact Polarization Camera with Liquid-Crystal Retarder for Patterning of Biological Textures
Alexander P Sviridov, Zachary Ulissi, Victor Chernomordik, and 3 more authors
The designed camera allows illumination with polarized light and consequently capturing two orthogonally polarized images using liquid crystal retarder and polarizer. Real time mapping of polarization degree and correlation coefficient were built into image processing.
@article{sviridov2008compact_u-x6,title={Compact Polarization Camera with Liquid-Crystal Retarder for Patterning of Biological Textures},author={Sviridov, Alexander P and Ulissi, Zachary and Chernomordik, Victor and Hassan, Moinuddin and Boccara, Albert C and Gandjbakhche, Amir},journal={Biomedical Optics},year={2008},pages={BTuF48},publisher={Optica Publishing Group},doi={10.1364/biomed.2008.btuf48},url={https://doi.org/10.1364/biomed.2008.btuf48},}
2006
Biochemistry
Visualization of biological texture using correlation coefficient images
Alexander P Sviridov, Zachary W. Ulissi, Victor V Chernomordik, and 2 more authors
@article{sviridov2006visualization,title={Visualization of biological texture using correlation coefficient images},author={Sviridov, Alexander P and Ulissi, Zachary W. and Chernomordik, Victor V and Hassan, Moinuddin and Gandjbakhche, Amir H},journal={Journal of Biomedical Optics},year={2006},volume={11},number={6},pages={060504},publisher={International Society for Optics and Photonics},doi={10.1117/1.2400248},url={https://doi.org/10.1117/1.2400248},}
