Unified Differentiable Learning of Electric Response
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909219623534592 |
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| author | Falletta, Stefano Cepellotti, Andrea Johansson, Anders Tan, Chuin Wei Musaelian, Albert Owen, Cameron J. Kozinsky, Boris |
| author_facet | Falletta, Stefano Cepellotti, Andrea Johansson, Anders Tan, Chuin Wei Musaelian, Albert Owen, Cameron J. Kozinsky, Boris |
| contents | Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws. Through application to $α$-SiO$_2$, we demonstrate that our approach can be used for predicting vibrational and dielectric properties of materials, and for conducting large-scale dynamics under arbitrary electric fields at unprecedented accuracy and scale. We apply our method to ferroelectric BaTiO$_3$ and capture the temperature-dependence and time evolution of hysteresis, revealing the underlying microscopic mechanisms of nucleation and growth that govern ferroelectric domain switching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17207 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unified Differentiable Learning of Electric Response Falletta, Stefano Cepellotti, Andrea Johansson, Anders Tan, Chuin Wei Musaelian, Albert Owen, Cameron J. Kozinsky, Boris Materials Science Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws. Through application to $α$-SiO$_2$, we demonstrate that our approach can be used for predicting vibrational and dielectric properties of materials, and for conducting large-scale dynamics under arbitrary electric fields at unprecedented accuracy and scale. We apply our method to ferroelectric BaTiO$_3$ and capture the temperature-dependence and time evolution of hysteresis, revealing the underlying microscopic mechanisms of nucleation and growth that govern ferroelectric domain switching. |
| title | Unified Differentiable Learning of Electric Response |
| topic | Materials Science |
| url | https://arxiv.org/abs/2403.17207 |