2.1k citations · 6k across the 29 of their papers we have counts for
7 papers · 1 filter
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Yuyang Wu, Jinhui Ye, Shuhao Zhang +3
Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify pote…
Kolmogorov-Arnold Networks in Thermoelectric Materials Design
Marco Fronzi, Michael J. Ford, Kamal Singh Nayal +2
The discovery of high-performance thermoelectric materials requires models that are both accurate and interpretable. Traditional machine learning approaches, while effective at pro…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
Nicholas Casetti, Dylan Anstine, Olexandr Isayev +1
Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving sev…
Applications of Modular Co-Design for De Novo 3D Molecule Generation
Danny Reidenbach, Filipp Nikitin, Olexandr Isayev +1
De novo 3D molecule generation is a pivotal task in drug discovery. However, many recent geometric generative models struggle to produce high-quality 3D structures, even if they ma…
GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
Filipp Nikitin, Ian Dunn, David Ryan Koes +1
Deep generative models have shown significant promise in generating valid 3D molecular structures, with the GEOM-Drugs dataset serving as a key benchmark. However, current evaluati…