Elemental augmentation of machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) bridge the gap between the accuracy of ab initio methods and the computational efficiency needed for large-scale simulations. However, custom-trained MLIPs are often limited to specific materials and lack flexibility for incorporating additional elemen...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Computational Materials Today |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S295046352500002X |
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