Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866913959917912064 |
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| author | Schmiedmayer, Bernhard Wolffs, J. W. de Wijs, Gilles A. Kentgens, Arno P. M. Lahnsteiner, Jonathan Kresse, Georg |
| author_facet | Schmiedmayer, Bernhard Wolffs, J. W. de Wijs, Gilles A. Kentgens, Arno P. M. Lahnsteiner, Jonathan Kresse, Georg |
| contents | We present a strategy combining machine learning and first-principles calculations to achieve highly accurate nuclear quadrupolar coupling constant predictions. Our approach employs two distinct machine-learning frameworks: a machine-learned force field to generate molecular dynamics trajectories and a second model for electric field gradients that preserves rotational and translational symmetries. By incorporating thermostat-driven molecular dynamics sampling, we enable the prediction of quadrupolar coupling constants in highly disordered materials at finite temperatures. We validate our method by predicting the tetragonal-to-cubic phase transition temperature of the organic-inorganic halide perovskite MAPbI$_3$, obtaining results that closely match experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition Schmiedmayer, Bernhard Wolffs, J. W. de Wijs, Gilles A. Kentgens, Arno P. M. Lahnsteiner, Jonathan Kresse, Georg Materials Science We present a strategy combining machine learning and first-principles calculations to achieve highly accurate nuclear quadrupolar coupling constant predictions. Our approach employs two distinct machine-learning frameworks: a machine-learned force field to generate molecular dynamics trajectories and a second model for electric field gradients that preserves rotational and translational symmetries. By incorporating thermostat-driven molecular dynamics sampling, we enable the prediction of quadrupolar coupling constants in highly disordered materials at finite temperatures. We validate our method by predicting the tetragonal-to-cubic phase transition temperature of the organic-inorganic halide perovskite MAPbI$_3$, obtaining results that closely match experimental data. |
| title | Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition |
| topic | Materials Science |
| url | https://arxiv.org/abs/2507.19435 |