Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions
Julian Arnold(University of Basel), Markus Meuwly(University of Basel), Debasish Koner(Indian Institute of Technology Hyderabad), Raymond J. Bemish(United States Air Force Research Laboratory), Silvan Käser(University of Basel), Narendra Singh(Stanford University)
Cited by 20
Related Papers
Spontaneous Generation of Aryl Carbocations from Phenols in Aqueous Microdroplets: Aromatic S<sub>N</sub>1 Reactions at the Air–Water Interface
|Journal of the American Chemical Society|2023|76
Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation
|The Journal of Chemical Physics|2019|61
Long-range versus short-range effects in cold molecular ion-neutral collisions
|RePEc: Research Papers in Economics|0|57
Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone
|Journal of Chemical Theory and Computation|2020|55
Reactive molecular dynamics: From small molecules to proteins
|Wiley Interdisciplinary Reviews Computational Molecular Science|2018|54