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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2022
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| Online Access: | https://hdl.handle.net/1721.1/142510 |