Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields
Abstract We introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide. With...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-023-01124-2 |