Aditi S. Krishnapriyan

40 papers A* 8A 1Journal 31

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention. CoRR Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary W. Ulissi
2026	J	`jnl`	From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures. CoRR Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan
2026	J	`jnl`	General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design. J. Chem. Inf. Model. Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan
2025	J	`jnl`	CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. J. Chem. Inf. Model. Danny Reidenbach, Aditi S. Krishnapriyan
2025	J	`jnl`	EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale. CoRR Yiheng Du, Aditi S. Krishnapriyan
2025	J	`jnl`	MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform. CoRR Yuan Chiang, Tobias Kreiman, Christine Zhang, Matthew C. Kuner, Elizabeth Weaver, Ishan Amin, Hyunsoo Park, Yunsung Lim, Jihan Kim, Daryl Chrzan, Aron Walsh, Samuel M. Blau, Mark Asta, Aditi S. Krishnapriyan
2025	J	`jnl`	Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. Trans. Mach. Learn. Res. Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan
2025	A*	`conf`	Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. ICLR Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan
2025	J	`jnl`	Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. CoRR Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan
2025	J	`jnl`	Transformers Discover Molecular Structure Without Graph Priors. CoRR Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan
2025	J	`jnl`	Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields. CoRR Tobias Kreiman, Aditi S. Krishnapriyan
2024	J	`jnl`	Deep Speech Synthesis from Multimodal Articulatory Representations. CoRR Peter Wu, Bohan Yu, Kevin Scheck, Alan W. Black, Aditi S. Krishnapriyan, Irene Y. Chen, Tanja Schultz, Shinji Watanabe, Gopala Krishna Anumanchipalli
2024	A*	`conf`	Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. ICLR Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
2024	J	`jnl`	Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. CoRR Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
2024	A	`conf`	Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2024	J	`jnl`	General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design. CoRR Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan
2024	A*	`conf`	Neural Spectral Methods: Self-supervised learning in the spectral domain. ICLR Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan
2024	J	`jnl`	Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement. CoRR Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent
2024	A*	`conf`	Scaling physics-informed hard constraints with mixture-of-experts. ICLR Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan
2024	J	`jnl`	Scaling physics-informed hard constraints with mixture-of-experts. CoRR Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan
2024	J	`jnl`	Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators. CoRR Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan
2024	A*	`conf`	The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. NeurIPS Eric Qu, Aditi S. Krishnapriyan
2024	J	`jnl`	The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. CoRR Eric Qu, Aditi S. Krishnapriyan
2024	J	`jnl`	Topological regularization via persistence-sensitive optimization. Comput. Geom. Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov
2023	J	`jnl`	Chemical reaction networks and opportunities for machine learning. Nat. Comput. Sci. Mingjian Wen, Evan Walter Clark Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan, Kristin A. Persson
2023	J	`jnl`	CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. CoRR Danny Reidenbach, Aditi S. Krishnapriyan
2023	J	`jnl`	Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2023	J	`jnl`	Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations. CoRR Daniel Rothchild, Andrew S. Rosen, Eric Taw, Connie Robinson, Joseph E. Gonzalez, Aditi S. Krishnapriyan
2023	A*	`conf`	Learning differentiable solvers for systems with hard constraints. ICLR Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
2023	J	`jnl`	Neural Spectral Methods: Self-supervised learning in the spectral domain. CoRR Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan
2022	A*	`conf`	AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2022	J	`jnl`	AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2022	J	`jnl`	Learning continuous models for continuous physics. CoRR Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney
2022	J	`jnl`	Learning differentiable solvers for systems with hard constraints. CoRR Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
2021	A*	`conf`	Characterizing possible failure modes in physics-informed neural networks. NeurIPS Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
2021	J	`jnl`	Characterizing possible failure modes in physics-informed neural networks. CoRR Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
2020	J	`jnl`	PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction. CoRR Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluç, Dmitriy Morozov, Katherine A. Yelick
2020	J	`jnl`	Persistent homology advances interpretable machine learning for nanoporous materials. CoRR Aditi S. Krishnapriyan, Joseph H. Montoya, Jens S. Hummelshøj, Dmitriy Morozov
2020	J	`jnl`	Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials. CoRR Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov
2020	J	`jnl`	Topological Regularization via Persistence-Sensitive Optimization. CoRR Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov