Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their a...
| Published in: | Nature Communications |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41467-025-59543-2 |