27 citations · 119 across the 12 of their papers we have counts for
3 papers · 1 filter
Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions
Juan Carlos San Vicente Veliz, Julian Arnold, Raymond J. Bemish +1
The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable pr…
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
Julian Arnold, Juan Carlos San Vicente Veliz, Debasish Koner +3
A machine learned (ML) model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented…
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Julian Arnold, Debasish Koner, Silvan Käser +3
Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested.…