The emergence of artificial intelligence has profoundly impacted computational chemistry, particularly through machine-learned potentials (MLPs), which offer a balance of accuracy and efficiency in calculating atomic energies and forces to be used in molecular dynamics simulations. These MLPs have significantly advanced molecular dynamics simulations across various applications, including large-scale simulations of materials, interfaces, and chemical reactions. Despite these advances, the construction of training datasets - a critical component for the accuracy of MLPs - has not received proportional attention. This is particularly critical for chemical reactivity which depends on rare barrier-crossing events. Here we address this gap by introducing ArcaNN, a comprehensive framework designed for generating training datasets for reactive MLPs. ArcaNN employs a concurrent learning approach combined with advanced sampling techniques to ensure accurate representation of high-energy geometries. The framework integrates automated processes for iterative training, exploration, new configuration selection, and energy and force labeling, while ensuring reproducibility and documentation. We demonstrate ArcaNN's capabilities through a paradigm nucleophilic substitution reaction in solution, showcasing its effectiveness, the uniformly low error of the resulting MLP everywhere along the chemical reaction coordinate, and its potential for broad applications in reactive molecular dynamics. We also provide guidelines on how to assess the quality of a NNP for a reactive system.
Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing
Knowing when to trust machine-learned interatomic potentials
Department of Chemistry, Mellon College of Science（化学系，梅隆科学学院）；Department of Materials Science and Engineering（材料科学与工程系）；Ray and Stephanie Lane Computational Biology Department, School of Computer Science（Ray和Stephanie Lane计算生物学部门，计算机科学学院）
Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Kakao Corporation（Kakao公司）；LG Chem（LG化学）；Seoul National University（首尔国立大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Seoul National University of Science and Technology（首尔国立科技大学）
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
Department of Chemistry, UC Berkeley, California 94720, United States（化学系，伯克利大学，加利福尼亚州，94720，美国）；Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States（地球数字材料研究所，伯克利大学，加利福尼亚州，94720，美国）；The Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria（奥地利科学与技术研究所）
Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论 division）；Department of Chemical Engineering & Materials Science, Michigan State University（密歇根州立大学化学工程与材料科学系）；NVIDIA Corp.（NVIDIA 公司）；Computer and Artificial Intelligence Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室计算机与人工智能 division）；Center for Nonlinear Studies, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室非线性研究 center）；Department of Physics and Astronomy, Michigan State University（密歇根州立大学物理与天文学系）
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；CAEP Software Center for High Performance Numerical Simulation（中国工程物理研究院高性能数值模拟软件中心）；Princeton University（普林斯顿大学）；Peking University（北京大学）；Beijing International Center for Mathematical Research（北京国际数学研究中心）；Beijing Institute of Big Data Research（北京大数据研究院）
High-quality, high-information datasets for universal atomistic machine learning
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；MPI for the Structure and Dynamics of Matter（马普物质结构与动力学研究所）；University of Cambridge（剑桥大学）；BASF SE（巴斯夫股份公司）
