Differentiable Molecular Simulations for Control and Learning. - Wujie Wang; Simon Axelrod; Rafael Gómez-Bombarelli (CoRR, 2020)

GEOM: Energy-annotated molecular conformations for property prediction and molecular generation. - Simon Axelrod; Rafael Gómez-Bombarelli (CoRR, 2020)

Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential. - Simon Axelrod; Eugene Shakhnovich; Rafael Gómez-Bombarelli (CoRR, 2021)

Energy-aware neural architecture selection and hyperparameter optimization. - Nathan C. Frey; Dan Zhao 007; Simon Axelrod; Michael Jones 001; David Bestor; Vijay Gadepally; Rafael Gómez-Bombarelli; Siddharth Samsi (IPDPS Workshops, 2022)

Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential. - Simon Axelrod; Eugene Shakhnovich; Rafael Gómez-Bombarelli (CoRR, 2022)

Learned Force Fields Are Ready For Ground State Catalyst Discovery. - Michael Schaarschmidt; Morgane Riviere; Alex M. Ganose; James S. Spencer; Alexander L. Gaunt; James Kirkpatrick; Simon Axelrod; Peter W. Battaglia; Jonathan Godwin (CoRR, 2022)

Natural Quantum Monte Carlo Computation of Excited States. - David Pfau; Simon Axelrod; Halvard Sutterud; Ingrid von Glehn; James S. Spencer (CoRR, 2023)

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling. - Simon Axelrod; Eugene Shakhnovich; Rafael Gómez-Bombarelli (J. Chem. Inf. Model., 2023)

Molecular machine learning with conformer ensembles. - Simon Axelrod; Rafael Gómez-Bombarelli (Mach. Learn. Sci. Technol., 2023)

Neural scaling of deep chemical models. - Nathan C. Frey; Ryan Soklaski; Simon Axelrod; Siddharth Samsi; Rafael Gómez-Bombarelli; Connor W. Coley; Vijay Gadepally (Nat. Mac. Intell., 2023)