Unified differentiable learning of electric response
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59304-1 |
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| author | Stefano Falletta Andrea Cepellotti Anders Johansson Chuin Wei Tan Marc L. Descoteaux Albert Musaelian Cameron J. Owen Boris Kozinsky |
| author_facet | Stefano Falletta Andrea Cepellotti Anders Johansson Chuin Wei Tan Marc L. Descoteaux Albert Musaelian Cameron J. Owen Boris Kozinsky |
| author_sort | Stefano Falletta |
| collection | DOAJ |
| description | Abstract 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−SiO2, 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 BaTiO3 and capture the temperature dependence, frequency dependence, and time evolution of the ferroelectric hysteresis, revealing the underlying intrinsic mechanisms of nucleation and growth that govern ferroelectric domain switching. |
| format | Article |
| id | doaj-art-7f71b57d50d249f2ac7a48e44dae4458 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7f71b57d50d249f2ac7a48e44dae44582025-08-20T01:47:32ZengNature PortfolioNature Communications2041-17232025-04-0116111210.1038/s41467-025-59304-1Unified differentiable learning of electric responseStefano Falletta0Andrea Cepellotti1Anders Johansson2Chuin Wei Tan3Marc L. Descoteaux4Albert Musaelian5Cameron J. Owen6Boris Kozinsky7John A. Paulson School of Engineering and Applied Sciences, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityDepartment of Chemistry and Chemical Biology, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityAbstract 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−SiO2, 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 BaTiO3 and capture the temperature dependence, frequency dependence, and time evolution of the ferroelectric hysteresis, revealing the underlying intrinsic mechanisms of nucleation and growth that govern ferroelectric domain switching.https://doi.org/10.1038/s41467-025-59304-1 |
| spellingShingle | Stefano Falletta Andrea Cepellotti Anders Johansson Chuin Wei Tan Marc L. Descoteaux Albert Musaelian Cameron J. Owen Boris Kozinsky Unified differentiable learning of electric response Nature Communications |
| title | Unified differentiable learning of electric response |
| title_full | Unified differentiable learning of electric response |
| title_fullStr | Unified differentiable learning of electric response |
| title_full_unstemmed | Unified differentiable learning of electric response |
| title_short | Unified differentiable learning of electric response |
| title_sort | unified differentiable learning of electric response |
| url | https://doi.org/10.1038/s41467-025-59304-1 |
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