publications (ML/GNN)
publications by categories in reversed chronological order. generated by jekyll-scholar.
2023
- Beyond independent error assumptions in large GNN atomistic modelsThe Journal of Chemical Physics, 2023
- AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantificationJournal of Open Source Software, 2023
- Chemical Properties from Graph Neural Network-Predicted Electron DensitiesarXiv preprint arXiv:2309.04811, 2023
- From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionarXiv preprint arXiv:2310.16802, 2023
- The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air CapturearXiv preprint arXiv:2311.00341, 2023
- Fine-Tuned Language Models Generate Stable Inorganic Materials as TextAI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023
- Generalization of Graph-Based Active Learning Relaxation Strategies Across MaterialsarXiv preprint arXiv:2311.01987, 2023
2022
- How Do Graph Networks Generalize to Large and Diverse Molecular Systems?arXiv preprint arXiv:2204.02782, 2022
- Transfer learning using attentions across atomic systems with graph neural networks (TAAG)The Journal of Chemical Physics, 2022
- Spherical Channels for Modeling Atomic InteractionsNeurIPS, Dec 2022
2021
- Rotation Invariant Graph Neural Networks using Spin ConvolutionsarXiv preprint arXiv:2106.09575, Dec 2021
2020
- Methods for comparing uncertainty quantifications for material property predictionsMachine Learning: Science and Technology, May 2020
2019
- Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of CatalystsThe Journal of Physical Chemistry Letters, Jul 2019