27 citations · 119 across the 12 of their papers we have counts for
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cond-mat.dis-nn2020★ 27 cited
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda +1
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method…
physics.chem-ph2020★ 20 cited
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.…