Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
Abstract We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01636-z |
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