Machine learning of electronic structure and atomistic properties from the external potential
Research Laboratory of Electronics, Massachusetts Institute of Technology（麻省理工学院电子研究实验室）；Université Marie et Louis Pasteur, CNRS, LmB (UMR 6623)（玛丽·路易·巴斯德大学，法国国家科学研究中心，LmB（联合研究单位6623））
Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most existing approaches learn a direct map from molecular geometries, typically represented as graphs or encoded local environments, to molecular properties or use ML as a surrogate for electronic structure theory by targeting quantities such as Fock or density matrices expressed in an atomic orbital (AO) basis.
Inspired by the Hohenberg-Kohn theorem, in this work, we propose an operator-centered framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input. From this operator, we construct hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors. At the same time, the matrix-valued nature of the external potential provides a natural connection to equivariant message-passing neural networks. In particular, we show that successive products of the external potential provide a scalable route to equivariant message passing and enable an efficient description of long-range effects. We demonstrate that this approach can be used to model molecular properties, such as energies and dipole moments, from the external potential, or learn effective operator-to-operator maps, including mappings to the Fock matrix and the reduced density matrix from which multiple molecular observables can be simultaneously derived.
Electrochemical Electron Transfer: Key Concepts, Theories, and Parameterization via Atomistic Simulations
University of Science and Technology of China（中国科学技术大学）；Forschungszentrum Jülich GmbH（于利希研究中心有限公司）；University of Jyväskylä（约恩苏大学）；RWTH Aachen University（亚琛工业大学）
Learning Electron Density with Virtual Atoms for Transferable Atomistic Representations
AI for Science Institute（人工智能科学研究院）；DP Technology（智谱AI）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）；National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics（计算物理国家重点实验室，应用物理与计算数学研究所）；HEDPS, CAPT, College of Engineering, Peking University（北京大学工学院，高能量密度物理教育部重点实验室，复杂系统先进计算方法（CAPT））
Characterization and quantification of the role of coherence in ultrafast quantum biological experiments using quantum master equations, atomistic simulations, and quantum process tomography
Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)
