期刊
PHYSICAL REVIEW B
卷 105, 期 16, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.105.165131
关键词
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资金
- European Research Council (ERC) under the European Union [948493]
- European Research Council (ERC) [948493] Funding Source: European Research Council (ERC)
Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties. In this work, the authors propose a scalable equivariant machine learning model based on local atomic environment descriptors for accurate predictions of dielectric and magnetic tensorial properties of different ranks in molecules.
Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks. These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modeling.
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