Accurate predictions of interatomic energies and forces are essential for high quality molecular dynamic simulations (MD). Machine learning algorithms can be used to overcome limitations of classical MD by predicting ab initio quality energies and forces. SE(3)-equivariant neural network allow reasoning over spatial relationships and exploiting the rotational and translational symmetries. One such algorithm is the SE(3)-Transformer, which we adapt for the ANI-1x dataset. Our early experimental results indicate through ablation studies that deeper networks - with additional SE(3)-Transformer layers - could reach necessary accuracies to allow effective integration with MD. However, faster implementations of the SE(3)-Transformer will be required, such as the recently published accelerated version by Milesi.
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）
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
University of Cambridge（剑桥大学）；Université Paris-Saclay（巴黎萨克雷大学）；Harvard University（哈佛大学）；Ruhr-Universität Bochum（波鸿鲁尔大学）；University of British Columbia（不列颠哥伦比亚大学）；Robert Bosch LLC Research and Technology Center（罗伯特博世有限责任公司研究与技术中心）
An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
The Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
High-performance training and inference for deep equivariant interatomic potentials
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保罗森工程与应用科学学院）；Center for Computational Science and Engineering, Massachusetts Institute of Technology（麻省理工学院计算科学与工程中心）；Department of Materials Science and Engineering, Massachusetts Institute of Technology（麻省理工学院材料科学与工程系）；Center for the Environment, Harvard University（哈佛大学环境中心）；Research Laboratory of Electronics, Massachusetts Institute of Technology（麻省理工学院电子研究实验室）；Sandia National Laboratories（桑尼亚国家实验室）；Robert Bosch LLC Research and Technology Center（罗伯特·博世 LLC 研究与技术中心）；Mirian Technologies Inc.（Mirian 技术公司）
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Peking University（北京大学）；AI for Science Institute（人工智能科学研究院）；Tsinghua University（清华大学）；HEDPS, CAPT, College of Engineering, Peking University（北京大学工学院理论力学与计算力学系、高超声速空气动力学国家重点实验室）；DP Technology（深势科技）；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（中国科学院应用物理与计算数学研究所）
University of Luxembourg（卢森堡大学）；Technische Universität Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data – BIFOLD（柏林学习与数据基础研究所（BIFOLD））；Max Planck Institute for Informatics（马克斯·普朗克 informatics 研究所）；Korea University（高丽大学）
MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance
Center for Advanced Systems Understanding（高级系统理解中心）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯多夫亥姆霍兹中心）；University of Wrocław（弗罗茨瓦夫大学）；Faculty of Physics, Warsaw University of Technology（华沙理工大学物理学院）
HIP: Hessian Interatomic Potentials without derivatives
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Technical University of Denmark（丹麦技术大学）；CAPeX Pioneer Center for Accelerating P2X Materials Discovery（CAPeX加速P2X材料发现先锋中心）；Acceleration Consortium（加速联盟）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院）；NVIDIA（英伟达）
ECENet: An Edge Cluster Expansion Line-Graph Neural Network
Kenneth S. Pitzer Theory Center and Department of Chemistry（肯尼斯·S·皮策理论中心与化学系）；Chemical Sciences Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室化学科学部）；Departments of Bioengineering and Chemical and Biomolecular Engineering University of California, Berkeley（加州大学伯克利分校生物工程和化学与分子生物工程系）
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）；Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室计算机、计算与统计科学部）；Earth and Environmental Sciences Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室地球与环境科学部）
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Yusuf Hamied Department of Chemistry, University of Cambridge（剑桥大学）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学）；Department of Materials Science and Engineering, Massachusetts Institute of Technology（麻省理工学院）；Department of Molecular Science and Software Engineering, University of California, Berkeley（加州大学伯克利分校）；Sandia National Laboratories（桑迪亚国家实验室）
