While machine learning approaches have been successfully used to represent interatomic potentials, their speed has typically lagged behind conventional formalisms. This is often due to the complexity of the structural fingerprints used to describe the local atomic environment and the large cutoff radii and neighbor lists used in the calculation of these fingerprints. Even recent machine learned methods are at least 10 times slower than traditional formalisms. An implementation of a rapid artificial neural network (RANN) style potential in the LAMMPS molecular dynamics package is presented here which utilizes angular screening to reduce computational complexity without reducing accuracy. For the smallest neural network architectures, this formalism rivals the modified embedded atom method (MEAM) for speed and accuracy, while the networks approximately one third as fast as MEAM were capable of reproducing the training database with chemical accuracy. The numerical accuracy of the LAMMPS implementation is assessed by verifying conservation of energy and agreement between calculated forces and pressures and the observed derivatives of the energy as well as by assessing the stability of the potential in dynamic simulation. The potential style is tested using a force field for magnesium and the computational efficiency for a variety of architectures is compared to a traditional potential models as well as alternative ANN formalisms. The predictive accuracy is found to rival that of slower methods.
Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential
Seoul National University（首尔大学）；Samsung Display Company Ltd.（三星显示有限公司）；Korea Institute for Advanced Study（韩国高等科学研究院）；Incheon National University（仁川国立大学）
Mechanical properties of V-4Ti-4Cr alloy from molecular dynamics with a neural-network potential
Joint Institute for High Temperatures（高温联合研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Institute for High Pressure Physics（高压物理研究所）；Joint Institute for Nuclear Research（苏联核子联合研究所）
Long-Range Machine Learning Interatomic Potentials for Defect Energetics in SrTiO$_3$
University of Warwick（华威大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Vienna（维也纳大学）；Georg-August University, Göttingen（哥廷根乔治-奥古斯特大学）；Max-Planck-Institute for Multidisciplinary Sciences（马克斯·普朗克多学科科学研究所）
Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
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）
kALDo 2.0: Scalable Thermal Transport from First Principles and Machine Learning Potentials
University of California Davis（加州大学戴维斯分校）；PSI Center for Scientific Computing Theory and Data（瑞士保罗谢勒研究所科学计算理论与数据中心）；Massachusetts Institute of Technology（麻省理工学院）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL)（国家新材料计算设计与发现中心（MARVEL））
Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院）；Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University（上海交通大学张江高等研究院）；Materials Genome Initiative Center, Shanghai Jiao Tong University（上海交通大学材料基因组计划中心）
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；University of California Berkeley（加州大学伯克利分校）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
A practical guide to machine learning interatomic potentials -- Status and future
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Harvard University（哈佛大学）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Utrecht University（乌得勒支大学）；Globus（格洛布斯公司）；Argonne National Laboratory（阿贡国家实验室）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Institute of Standards and Technology（美国国家标准与技术研究院）；University of Cambridge（剑桥大学）；Google DeepMind（谷歌DeepMind）
FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
Northeastern University（东北大学）；Department of Mathematics（数学系）；Khoury College of Computer Sciences（计算机科学学院）；Department of Mechanical and Industrial Engineering（机械与工业工程系）
"One defect, one potential" strategy for accurate machine learning prediction of defect phonons
College of Integrated Circuits and Micro-Nano Electronics, and Key Laboratory of Computational Physical Sciences (MOE), Fudan University（复旦大学集成电路与微纳电子学院、计算物理科学教育部重点实验室）
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Bohai University（渤海大学）；The Chinese University of Hong Kong（香港中文大学）；Chalmers University of Technology（查尔姆斯理工大学）；Shenzhen Technology University（深圳职业技术大学）；Shenzhen University（深圳大学）；Xinyu University（新余学院）；University of Science and Technology Beijing（北京科技大学）
Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
Massachusetts Institute of Technology（麻省理工学院）；Schwarzman College of Computing（计算学院）；Center for Computational Science and Engineering（计算科学与工程中心）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）
Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
Key Laboratory of Material Simulation Methods \& Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, China；Research Center for Crystal Materials, State Key Laboratory of Functional Materials；Devices for Special Environmental Conditions, Xinjiang Key Laboratory of Functional Crystal Materials, Xinjiang Technical Institute of Physics；Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi 830011, China；State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China；Department of Chemistry, University College London, Gordon Street, London, WC1H 0AJ, UK；International Center of Future Science, Jilin University, Changchun 130012, China；Key Laboratory of Physics；Technology for Advanced Batteries of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, China
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
organization= Department of Materials Science；Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= J. Mike Walker '66 Department of Mechanical Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= Wm Michael Barnes '64 Department of Industrial；Systems Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA
