Characterizing possible failure modes in physics-informed neural networks A Krishnapriyan, A Gholami, S Zhe, R Kirby, MW Mahoney Advances in neural information processing systems 34, 26548-26560, 2021 | 792 | 2021 |
Chemical reaction networks and opportunities for machine learning M Wen, EWC Spotte-Smith, SM Blau, MJ McDermott, AS Krishnapriyan, ... Nature Computational Science 3 (1), 12-24, 2023 | 84 | 2023 |
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks AS Krishnapriyan, J Montoya, M Haranczyk, J Hummelshøj, D Morozov Scientific reports 11 (1), 1-11, 2021 | 58 | 2021 |
Topological descriptors help predict guest adsorption in nanoporous materials AS Krishnapriyan, M Haranczyk, D Morozov The Journal of Physical Chemistry C 124 (17), 9360-9368, 2020 | 56 | 2020 |
Numerical optimization of density functional tight binding models: application to molecules containing carbon, hydrogen, nitrogen, and oxygen A Krishnapriyan, P Yang, AMN Niklasson, MJ Cawkwell Journal of Chemical Theory and Computation 13 (12), 6191-6200, 2017 | 42 | 2017 |
On the stereochemical inertness of the auride lone pair: Ab initio studies of AAu (A= K, Rb, Cs) M Miao, J Brgoch, A Krishnapriyan, A Goldman, JA Kurzman, R Seshadri Inorganic Chemistry 52 (14), 8183-8189, 2013 | 40 | 2013 |
Learning continuous models for continuous physics AS Krishnapriyan, AF Queiruga, NB Erichson, MW Mahoney Communications Physics 6 (1), 319, 2023 | 37 | 2023 |
An ecosystem for digital reticular chemistry KM Jablonka, AS Rosen, AS Krishnapriyan, B Smit ACS central science 9 (4), 563-581, 2023 | 36 | 2023 |
Learning differentiable solvers for systems with hard constraints G Négiar, MW Mahoney, AS Krishnapriyan The Eleventh International Conference on Learning Representations, 2023 | 30 | 2023 |
Spectrum of exfoliable 1D van der Waals molecular wires and their electronic properties Y Zhu, DA Rehn, ER Antoniuk, G Cheon, R Freitas, A Krishnapriyan, ... ACS nano 15 (6), 9851-9859, 2021 | 30 | 2021 |
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction N Swenson, AS Krishnapriyan, A Buluc, D Morozov, K Yelick arXiv preprint arXiv:2010.16027, 2020 | 24 | 2020 |
Electronic structure study of the CdS buffer layer in CIGS solar cells by X-ray absorption spectroscopy: Experiment and theory C Schwartz, D Nordlund, TC Weng, D Sokaras, L Mansfield, ... Solar energy materials and solar cells 149, 275-283, 2016 | 23 | 2016 |
First-principles study of band alignments in the p-type hosts BaM2X2 (M= Cu, Ag; X= S, Se) A Krishnapriyan, PT Barton, M Miao, R Seshadri Journal of Physics: Condensed Matter 26 (15), 155802, 2014 | 20 | 2014 |
LATTE N Bock, MJ Cawkwell, JD Coe, A Krishnapriyan, MP Kroonblawd, A Lang, ... 10.5281/zenodo.1297664, 2008 | 20* | 2008 |
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D Long, Z Wang, A Krishnapriyan, R Kirby, S Zhe, M Mahoney International Conference on Machine Learning, 14210-14222, 2022 | 18 | 2022 |
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products S Luo, T Chen, AS Krishnapriyan The Twelfth International Conference on Learning Representations, 2024 | 17 | 2024 |
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation D Reidenbach, AS Krishnapriyan Journal of Chemical Information and Modeling, 2024 | 7 | 2024 |
Neural Spectral Methods: Self-supervised learning in the spectral domain Y Du, N Chalapathi, A Krishnapriyan The Twelfth International Conference on Learning Representations, 2024 | 6 | 2024 |
Topological Regularization via Persistence-Sensitive Optimization A Nigmetov, AS Krishnapriyan, N Sanderson, D Morozov arXiv preprint arXiv:2011.05290, 2020 | 6* | 2020 |
Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators S Raja, I Amin, F Pedregosa, AS Krishnapriyan arXiv preprint arXiv:2402.13984, 2024 | 5 | 2024 |