Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy
Abstract While machine learning excels in simulating material thermal properties, its application to order-disorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces, forces, and vibrational properties in the pres...
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| Main Authors: | Andrea Corradini, Giovanni Marini, Matteo Calandra |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01614-5 |
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