Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies , shorttitle =
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Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.