Aditi S. Krishnapriyan

According to our database, Aditi S. Krishnapriyan authored at least 21 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians.

Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan

CoRR, January, 2025

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation.

Danny Reidenbach, Aditi S. Krishnapriyan

J. Chem. Inf. Model., 2025

2024

Deep Speech Synthesis from Multimodal Articulatory Representations.

Peter Wu, Bohan Yu, Kevin Scheck, Alan W. Black, Aditi S. Krishnapriyan, Irene Y. Chen, Tanja Schultz, Shinji Watanabe, Gopala Krishna Anumanchipalli

CoRR, 2024

General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design.

Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan

CoRR, 2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement.

Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent

CoRR, 2024

Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators.

Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan

CoRR, 2024

Topological regularization via persistence-sensitive optimization.

Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov

Comput. Geom., 2024

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.

Eric Qu, Aditi S. Krishnapriyan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products.

Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Neural Spectral Methods: Self-supervised learning in the spectral domain.

Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling physics-informed hard constraints with mixture-of-experts.

Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels.

Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Chemical reaction networks and opportunities for machine learning.

Mingjian Wen, Evan Walter Clark Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan, Kristin A. Persson

Nat. Comput. Sci., 2023

Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations.

Daniel Rothchild, Andrew S. Rosen, Eric Taw, Connie Robinson, Joseph E. Gonzalez, Aditi S. Krishnapriyan

CoRR, 2023

Learning differentiable solvers for systems with hard constraints.

Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning continuous models for continuous physics.

Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney

CoRR, 2022

AutoIP: A United Framework to Integrate Physics into Gaussian Processes.

Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney

Proceedings of the International Conference on Machine Learning, 2022

2021

Characterizing possible failure modes in physics-informed neural networks.

Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction.

Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluç, Dmitriy Morozov, Katherine A. Yelick

CoRR, 2020

Persistent homology advances interpretable machine learning for nanoporous materials.

Aditi S. Krishnapriyan, Joseph H. Montoya, Jens S. Hummelshøj, Dmitriy Morozov

CoRR, 2020

Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials.

Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov

CoRR, 2020