Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials. - Aditi S. Krishnapriyan; Maciej Haranczyk; Dmitriy Morozov (CoRR, 2020)

Persistent homology advances interpretable machine learning for nanoporous materials. - Aditi S. Krishnapriyan; Joseph H. Montoya; Jens S. Hummelshøj; Dmitriy Morozov (CoRR, 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)

Characterizing possible failure modes in physics-informed neural networks. - Aditi S. Krishnapriyan; Amir Gholami; Shandian Zhe; Robert M. Kirby; Michael W. Mahoney (NeurIPS, 2021)

AutoIP: A United Framework to Integrate Physics into Gaussian Processes. - Da Long; Zheng Wang 042; Aditi S. Krishnapriyan; Robert M. Kirby; Shandian Zhe; Michael W. Mahoney (ICML, 2022)

Learning continuous models for continuous physics. - Aditi S. Krishnapriyan; Alejandro F. Queiruga; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2022)

Learning differentiable solvers for systems with hard constraints. - Geoffrey Négiar; Michael W. Mahoney; Aditi S. Krishnapriyan (ICLR, 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)

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)

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 (AISTATS, 2024)

Scaling physics-informed hard constraints with mixture-of-experts. - Nithin Chalapathi; Yiheng Du; Aditi S. Krishnapriyan (ICLR, 2024)

Neural Spectral Methods: Self-supervised learning in the spectral domain. - Yiheng Du; Nithin Chalapathi; Aditi S. Krishnapriyan (ICLR, 2024)

Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. - Shengjie Luo; Tianlang Chen; Aditi S. Krishnapriyan (ICLR, 2024)

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. - Eric Qu; Aditi S. Krishnapriyan (NeurIPS, 2024)

Topological regularization via persistence-sensitive optimization. - Arnur Nigmetov; Aditi S. Krishnapriyan; Nicole Sanderson; Dmitriy Morozov (Comput. Geom., 2024)

Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators. - Sanjeev Raja; Ishan Amin; Fabian Pedregosa; 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)

General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design. - Yue Jian; Curtis Wu; Danny Reidenbach; Aditi S. Krishnapriyan (CoRR, 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 001; Gopala Krishna Anumanchipalli (CoRR, 2024)

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. - Ishan Amin; Sanjeev Raja; Aditi S. Krishnapriyan (ICLR, 2025)

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. - Danny Reidenbach; Aditi S. Krishnapriyan (J. Chem. Inf. Model., 2025)

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. - Sanjeev Raja; Ishan Amin; Fabian Pedregosa; Aditi S. Krishnapriyan (Trans. Mach. Learn. Res., 2025)

Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields. - Tobias Kreiman; Aditi S. Krishnapriyan (CoRR, 2025)

Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional. - Sanjeev Raja; Martin Sípka; Michael Psenka; Tobias Kreiman; Michal Pavelka; Aditi S. Krishnapriyan (CoRR, 2025)