publications (catalysis/ml)
publications by categories in reversed chronological order. generated by jekyll-scholar.
2023
- Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptionsApplied Catalysis B: Environmental, Jan 2023
- The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide ElectrocatalystsACS Catalysis, Jan 2023
- AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentialsnpj Computational Materials, Jan 2023
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- WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction ConditionsJournal of Chemical Information and Modeling, Jan 2023PMID: 37017312
- Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data SetJournal of Chemical Information and Modeling, Jan 2023PMID: 38049389
2022
- FINETUNA: Fine-tuning Accelerated Molecular SimulationsMachine Learning: Science and Technology, Sep 2022
- Screening of bimetallic electrocatalysts for water purification with machine learningThe Journal of Chemical Physics, Sep 2022
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- Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversionCatal. Sci. Technol., Jul 2022
2021
- Open Catalyst 2020 (OC20) Dataset and Community ChallengesACS Catalysis, Apr 2021
- Computational catalyst discovery: Active classification through myopic multiscale samplingThe Journal of Chemical Physics, Apr 2021
- Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloyMachine Learning: Science and Technology, Apr 2021
2020
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- Practical Deep-Learning Representation for Fast Heterogeneous Catalyst ScreeningThe Journal of Physical Chemistry Letters, Mar 2020
- Enabling robust offline active learning for machine learning potentials using simple physics-based priorsMachine Learning: Science and Technology, Dec 2020
- Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)Physical Review Letters, Dec 2020
- An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy StoragearXiv preprint arXiv:2010.09435, Dec 2020
2019
- Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural NetworksJournal of Chemical Information and Modeling, Dec 2019
2018
- Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolutionNature Catalysis, Sep 2018
2017
- To address surface reaction network complexity using scaling relations machine learning and DFT calculationsNature Communications, Mar 2017
- Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 ReductionACS Catalysis, Oct 2017
2016
- Automated Discovery and Construction of Surface Phase Diagrams using Machine LearningThe Journal of Physical Chemistry Letters, Oct 2016