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 modelling.
Scalar machine learning of tensorial quantities -- Born effective charges from monopole models
University of Vienna（维也纳大学）；Center for Computational Materials Science（计算材料科学中心）；Vienna Doctoral School in Physics（维也纳物理博士生院）；VASP Software GmbH（VASP软件有限公司）
Physics-Based Factorized Machine Learning for Predicting Ionic Dielectric Tensors
Institute for Materials Research, Tohoku University（东北大学物质材料研究机构）；Graduate School of Engineering, Tohoku University（东北大学大学院工学研究科）；Organization for Advanced Studies, Tohoku University（东北大学先进教育研究机构）
Learning local equivariant representations for quantum operators
AI for Science Institute（北京智源人工智能研究院）；McGill University（麦吉尔大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Birla Institute of Technology & Science, Pilani-Dubai Campus（毕拉理工与科学学院 Pilani 迪拜校区）；DP Technology（深势科技）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
Department of Mechanical Engineering, Texas Tech University, Lubbock, Texas 79409, USA（德克萨斯理工大学机械工程系）；Samsung Advanced Institute of Technology, Samsung Electronics, Suwon 16678, Republic of Korea（三星电子首尔先进技术研究所）
Predicting ionic conductivity in solids from the machine-learned potential energy landscape
National University of Singapore（新加坡国立大学）；Institute for Functional Intelligent Materials（功能智能材料研究所）；Centre for Advanced 2D Materials（先进二维材料中心）；HSE University（俄罗斯高等经济大学）；Faculty of Computer Science（计算机科学学院）；Yale-NUS College（耶鲁-新加坡大学学院）；Department of Materials Science Engineering（材料科学与工程系）；Department of Physics（物理系）；Constructor Knowledge Labs；Constructor University
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
Department of Computer Science and Engineering（计算机科学与工程系）；Chalmers University of Technology（楚姆勒技术大学）；University of Gothenburg（哥德堡大学）；Department of Chemistry and Chemical Engineering（化学与化工系）；Technology Research（技术研究部）；Intel Corporation（英特尔公司）；Chief Technology Office（首席技术办公室）；EMD Electronics（EMD电子公司）；Lila Sciences（Lila科学）；M Ventures（M资本）
