Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

© 2020 Elsevier Inc. Through autonomous data acquisition and machine learning, we demonstrate that our neural-network-based reactive force fields allow us to study the dynamical effects of several pericyclic reactions and to predict solvent effects on periselectivity. Our method is over 2,000 times...

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Bibliographic Details
Main Authors: Ang, Shi Jun, Wang, Wujie, Schwalbe-Koda, Daniel, Axelrod, Simon, Gómez-Bombarelli, Rafael
Other Authors: Massachusetts Institute of Technology. Department of Materials Science and Engineering
Format: Article
Language:English
Published: Elsevier BV 2022
Online Access:https://hdl.handle.net/1721.1/142510

Internet

https://hdl.handle.net/1721.1/142510

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