Publications

2026

Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
K. Nelson, T. Kreiman, S. Levine, A. S. Krishnapriyan
arXiv (pre-print)
Link
Benchmarking Machine-Learned Potentials for Water-Splitting Catalysts: Validation on Pt and IrO₂ Surfaces Using OC20 and OMat24
A. Jana, F. Roncoroni, J. Fornaciari, A. S. Krishnapriyan, D. Prendergast, A. Weber, E. J. Crumlin, J. Qian
chemrxiv (pre-print)
Link
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
E. Qu, B. M. Wood, A. S. Krishnapriyan†, Z. W. Ulissi†
International Conference on Machine Learning (ICML), 2026
Link
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
R. Liu, E. Qu, T. Kreiman, S. M. Blau, A. S. Krishnapriyan
International Conference on Machine Learning (ICML), 2026
Link
Parallel Stochastic Gradient-Based Planning for World Models
M. Psenka, M. Rabbat, A. S. Krishnapriyan, Y. LeCun, A. Bar
International Conference on Machine Learning (ICML), 2026
Link
Flow matching for generative modelling in bioinformatics and computational biology
A. Morehead, L. Atanackovic, A. Hegde, Y. Wag, F. Boadu, J. Selvaraj, A. Tong, A. S. Krishnapriyan, J. Cheng
Nature Machine Intelligence, 2026
Link
Understanding and Mitigating Distribution Shifts in Universal Machine Learning Interatomic Potentials
T. Kreiman and A. S. Krishnapriyan
Digital Discovery, 2026
Link
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
Y. Jian, C. Wu, D. Reidenbach, A. S. Krishnapriyan
Journal of Chemical Information and Modeling, 2026
Link

2025

Transformers Discover Molecular Structure without Graph Priors
T. Kreiman, Y. Bai, F. Atieh, E. Weaver, E. Qu, A. S. Krishnapriyan
arXiv (pre-print)
Link
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
D. S. Levine, M. Shuaibi, E. W. C. Spotte-Smith, M. G. Taylor, M. R. Hasyim, K. Michel, I. Batatia, G. Csanyi, M. Dzamba, P. Eastman, N. C. Frey, X. Fu, V. Gharakhanyan, A. S. Krishnapriyan, J. A. Rackers, S. Raja, A. Rizvi, A. S. Rosen, Z. Ulissi, S. Vargas, C. L. Zitnick, S. M. Blau, B. M. Wood
arXiv (pre-print)
Link
Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop
E. Fahsbender, et al.
arXiv (pre-print)
Link
EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
Y. Du and A. S. Krishnapriyan
Neural Information Processing Systems (NeurIPS), 2025
Link
Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform
Y. Chiang, T. Kreiman, C. Zhang, M. Kuner, E. Weaver, I. Amin, H. Park, Y. Lim, J. Kim, D. Chrzan, A. Walsh, S. M. Blau, M. Asta, A. S. Krishnapriyan
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, Spotlight (top 3%), 2025
Link
Foundation Models for Atomistic Simulation of Chemistry and Materials
E. C. Y. Yuan, Y. Liu, J. Chen, P. Zhong, S. Raja, T. Kreiman, S. Vargas, W. Xu, M. Head-Gordon, C. Yang, S. M. Blau†, B. Cheng†, A. S. Krishnapriyan†, T. Head-Gordon†
Nature Reviews Chemistry, 2025
Link
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
S. Raja, M. Sipka, M. Psenka, T. Kreiman, M. Pavelka, A. S. Krishnapriyan
International Conference on Machine Learning (ICML), 2025
Link
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
I. Amin, S. Raja, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), 2025
Link
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
S. Raja, I. Amin, F. Pedregosa, A. S. Krishnapriyan
Transactions on Machine Learning Research (TMLR), 2025
Link

2024

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
E. Qu, A. S. Krishnapriyan
Neural Information Processing Systems (NeurIPS), 2024
Link
Scaling physics-informed hard constraints with mixture-of-experts
N. Chalapathi, Y. Du, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), 2024
Link
Enabling efficient equivariant operations in the Fourier basis via Gaunt Tensor Products
S. Luo, T. Chen, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), Spotlight (top 3%), 2024
Link
Neural Spectral Methods: Self-supervised learning in the spectral domain
Y. Du, N. Chalapathi, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), 2024
Link
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Theory Calculations
D. Rothchild, A. S. Rosen, E. Taw, C. Robinson, J. Gonzalez, A. S. Krishnapriyan
Chemical Science, 2024
Link
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
D. Reidenbach, A. S. Krishnapriyan
Journal of Chemical Information and Modeling, 2024
Link
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
H. Jin, P. Balaprakash, A. Zou, P. Ghysels, A. S. Krishnapriyan, A. Mate, A. Barnes, R. Bent
Electric Power Systems Research, 2024
Link
Deep Speech Synthesis from Multimodal Articulatory Representations
P. Wu, B. Yu, K. Scheck, A. Black, A. S. Krishnapriyan, I. Y. Chen, T. Schultz, S. Watanabe, G. K. Anumanchipalli
Asia Pacific Signal and Information Processing Association Annual Summit (APSIPA ASC), 2024
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
D. Long, W. W. Xing, A. S. Krishnapriyan, R. M. Kirby, S. Zhe, M. W. Mahoney
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Link
Topological regularization via persistence-sensitive optimization
A. Nigmetov*, A. S. Krishnapriyan*, N. Sanderson, D. Morozov
Computational Geometry, 2024
Link
* Equal contribution

2023

Learning differentiable solvers for systems with hard constraints
G. Negiar, M. W. Mahoney, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), 2023
Link
Learning continuous models for continuous physics
A. S. Krishnapriyan, A. Queiruga, N. B. Erichson, M. W. Mahoney
Communications Physics, 2023
Link
Chemical reaction networks and opportunities for machine learning
M. Wen, S. M. Blau, E. W. Spotte-Smith, M. McDermott, A. S. Krishnapriyan, K. Persson
Nature Computational Science, 2023
Link
An ecosystem for digital reticular chemistry
K. Jablonka, A. S. Rosen, A. S. Krishnapriyan, B. Smit
ACS Central Science, 2023
Link