Aditi S. Krishnapriyan

1. The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
   2025/05/13 by Daniel S. Levine, Levine, Daniel S., Muhammed Shuaibi +43 · 6 voices · 35 citations
   Physics and Astronomy · #physics.chem-ph
2. The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
   2024/10/31 by Eric Qu, Aditi S. Krishnapriyan, Qu, Eric +1 · 2 voices · 20 citations
   Computer Science · Materials Science · #Machine Learning in Materials Science #cs.LG
3. Learning differentiable solvers for systems with hard constraints
   2022/07/18 by Geoffrey Négiar, Négiar, Geoffrey, Michael W. Mahoney +3 · 11 citations
   Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications
4. Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
   2025/04/25 by Sanjeev Raja, Martin Šípka, Raja, Sanjeev +9 · 3 voices · 10 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #cs.LG #cond-mat.mtrl-sci #physics.chem-ph #q-bio.BM
5. Topological Descriptors Help Predict Guest Adsorption in Nanoporous\n Materials
   2020/01/16 by Aditi S. Krishnapriyan, Krishnapriyan, Aditi S., Maciej Harańczyk +3 · 3 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · #Algebraic Topology (math.AT) #Bioinformatics and Genomic Networks #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Geochemistry and Geologic Mapping #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #Topological and Geometric Data Analysis
6. Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
   2025/01/15 by Ishan Amin, Amin, Ishan, Sanjeev Raja +4 · 1 voice · 10 citations
   Computer Science · Materials Science · Physics and Astronomy · #Machine Learning in Materials Science #Model Reduction and Neural Networks #cond-mat.mtrl-sci #cs.LG #physics.bio-ph #physics.chem-ph
7. Neural Spectral Methods: Self-supervised learning in the spectral domain
   2023/12/08 by Yiheng Du, Du, Yiheng, Nithin Chalapathi +3 · 5 citations
   Physics and Astronomy · #Model Reduction and Neural Networks
8. Foundation Models for Atomistic Simulation of Chemistry and Materials
   2025/03/13 by Eric Chung‐Yueh Yuan, Yunsheng Liu, Yuan, Eric C. -Y. +25 · 10 citations
   Chemistry · Engineering · Materials Science · #Advanced Materials Characterization Techniques #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Radioactive element chemistry and processing
9. Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
   2023/10/09 by Da Long, Long, Da, Wei Xing +9 · 2 citations
   Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Model Reduction and Neural Networks
10. PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
    2020/10/29 by Nicolas Swenson, Swenson, Nicolas, Aditi S. Krishnapriyan +8 · 1 citation
    Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Algebraic Topology (math.AT) #Bioinformatics and Genomic Networks #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Microbial Natural Products and Biosynthesis #Topological and Geometric Data Analysis
11. Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
    2025/03/11 by Tobias Kreiman, Kreiman, Tobias, Aditi S. Krishnapriyan +1 · 6 citations
    Decision Sciences · Engineering · #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #Reservoir Engineering and Simulation Methods #Simulation Techniques and Applications
12. MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
    2025/09/25 by Yuan Chiang, Tobias Kreiman, Chiang, Yuan +25 · 9 citations
    Computer Science · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Chemical Physics (physics.chem-ph) #Computational Engineering #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Materials Science (cond-mat.mtrl-sci) #and Science (cs.CE)
13. Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
    2024/05/16 by Hongwei Jin, Jin, Hongwei, Prasanna Balaprakash +13 · 1 citation
    Engineering · Materials Science · #FOS: Computer and information sciences #FOS: Electrical engineering #High voltage insulation and dielectric phenomena #Machine Learning (cs.LG) #Power System Reliability and Maintenance #Power Transformer Diagnostics and Insulation #Systems and Control (eess.SY) #electronic engineering #information engineering