Publications

Research themes (click to filter):

all generative models molecular dynamics fluid dynamics understanding scientific ML benchmarking data
2026
Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
K. Nelson, T. Kreiman, S. Levine, A. S. Krishnapriyan
Neural Information Processing Systems (NeurIPS), 2026
Link
generative modelsmolecular dynamics
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
molecular dynamicsunderstanding scientific ML
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
molecular dynamicsbenchmarkingunderstanding scientific ML
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
generative models
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
D. Goel, N. Chalapathi, S. Raja, A. S. Krishnapriyan
Transactions on Machine Learning Research (TMLR), 2026
Link
understanding scientific MLfluid dynamicsbenchmarkingdata
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
generative modelsmolecular dynamics
Understanding and Mitigating Distribution Shifts in Universal Machine Learning Interatomic Potentials
T. Kreiman and A. S. Krishnapriyan
Digital Discovery, 2026
Link
molecular dynamicsunderstanding scientific MLbenchmarking
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
Discover Chemistry
Link
benchmarkingmolecular dynamics
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
generative modelsmolecular dynamics
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
molecular dynamicsunderstanding scientific ML
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
datamolecular dynamics
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
benchmarkingdata
EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
Y. Du and A. S. Krishnapriyan
Neural Information Processing Systems (NeurIPS), 2025
Link
fluid dynamicsunderstanding scientific ML
MLIP Arena: 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
benchmarkingmolecular dynamicsdata
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
molecular dynamicsdataunderstanding scientific ML
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
generative modelsmolecular dynamics
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
molecular dynamicsunderstanding scientific ML
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
molecular dynamicsunderstanding scientific ML
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
molecular dynamicsunderstanding scientific ML
Scaling physics-informed hard constraints with mixture-of-experts
N. Chalapathi, Y. Du, A. S. Krishnapriyan
International Conference on Learning Representations (ICLR), 2024
Link
understanding scientific MLfluid dynamics
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
molecular dynamicsunderstanding scientific ML
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
understanding scientific MLfluid dynamics
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
generative modelsmolecular dynamics
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
D. Reidenbach, A. S. Krishnapriyan
Journal of Chemical Information and Modeling, 2024
Link
generative modelsmolecular dynamics
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
understanding scientific ML
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
understanding scientific ML
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
understanding scientific ML
Topological regularization via persistence-sensitive optimization
A. Nigmetov*, A. S. Krishnapriyan*, N. Sanderson, D. Morozov
Computational Geometry, 2024
Link
understanding scientific ML
* 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
understanding scientific MLfluid dynamics
Learning continuous models for continuous physics
A. S. Krishnapriyan, A. Queiruga, N. B. Erichson, M. W. Mahoney
Communications Physics, 2023
Link
understanding scientific MLfluid dynamics
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
datamolecular dynamics
An ecosystem for digital reticular chemistry
K. Jablonka, A. S. Rosen, A. S. Krishnapriyan, B. Smit
ACS Central Science, 2023
Link
datamolecular dynamics