Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules. Recently, numerous papers on machine learning (ML) of molecular properties have also been published. ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability. Here, we propose an approach that combines the strong sides of DFT and ML, namely, physicality and low computational cost. By generalizing the famous Hohenberg-Kohn theorems, we derive general equations for exact electron densities and energies that can naturally guide applications of ML in Quantum Chemistry. Based on these equations, we build a deep neural network that can compute electron densities and energies of a wide range of organic molecules not only much faster, but also closer to exact physical values than current versions of DFT. In particular, we reached a mean absolute error in energies of molecules with up to eight non-hydrogen atoms as low as 0.9 kcal/mol relative to CCSD(T) values, noticeably lower than those of DFT (down to ~3 kcal/mol on the same set of molecules) and ML (down to ~1.5 kcal/mol) methods. A simultaneous improvement in the accuracy of predictions of electron densities and energies suggests that the proposed approach describes the physics of molecules better than DFT functionals developed by "human learning" earlier. Thus, physics-based ML offers exciting opportunities for modeling, with high-theory-level quantum chemical accuracy, of much larger molecular systems than currently possible.
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models
Laboratory of Artificial Chemical Intelligence（人工智能化学智能实验室）；Institute of Chemical Sciences and Engineering（化学科学与工程研究所）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Science Done on a Machine by a Machine: AI Agents in Computational Chemistry
State Key Laboratory of Physical Chemistry of Solid Surfaces（固体表面物理化学国家重点实验室）；College of Chemistry（化学学院）；Xiamen University（厦门大学）；School of Physics, Anhui University（安徽大学物理学院）
Ab-initio quantum chemistry with neural-network wavefunctions
FU Berlin（柏林自由大学）；DeepMind（深度思维）；University of Zurich（苏黎世大学）；IBM Quantum（IBM量子）；IBM Research Zurich（IBM苏黎世研究院）；Thomas J. Watson Research Center（托马斯·J·沃森研究中心）；Imperial College London（帝国理工学院）；EPFL（洛桑联邦理工学院）；Rice University（莱斯大学）；Microsoft Research Cambridge（微软剑桥研究院）
Scientific intuition inspired by machine learning generated hypotheses
University of Toronto（多伦多大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；National Research Council of Canada（加拿大国家研究委员会）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院）
SELFIES and the future of molecular string representations
Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）；Fordham University（福特汉姆大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Syngenta Jealott’s Hill International Research Centre（先正达吉洛特山国际研究中心）；Imperial College London（帝国理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Toronto（多伦多大学）；IBM Research Europe（IBM欧洲研究院）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of São Paulo（圣保罗大学）；University of Vienna（维也纳大学）
