This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models, widely used in atomistic simulations of chemical systems. CEMT demonstrated enhanced flexibility and adaptability by allowing reference points to exist anywhere within the modeled domain and thus, enabling the study of various model architectures. Utilizing Gaussian Multipole (GMP) featurization functions, several models with different reference point sets, including finite difference grid-centered and bond-centered models, were tested to analyze the variance in capabilities intrinsic to models built on distinct reference points. The results underscore the potential of non-atom-centered reference points in force training, revealing variations in prediction accuracy, inference speed and learning efficiency. Finally, a unique connection between CEMT and real-space orbital-free finite element Density Functional Theory (FE-DFT) is established, and the implications include the enhancement of data efficiency and robustness. It allows the leveraging of spatially-resolved energy densities and charge densities from FE-DFT calculations, as well as serving as a pivotal step towards integrating known quantum-mechanical laws into the architecture of ML models.
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
School of Engineering and Applied science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；College of Resources and Environment, Chengdu University of Information Technology（成都信息工程大学资源环境学院）；USC Keck School of Medicine, University of Southern California（南加州大学凯克医学院）；College of Big Data and Intelligent Engineering, Southwest Forestry University（西南林业大学大数据与智能工程学院）；College of Information Sciences and Technology, Donghua University（东华大学信息科学与技术学院）
Operator Forces For Coarse-Grained Molecular Dynamics
Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany（数学与计算机科学系，弗里堡大学，柏林，德国）；Microsoft Research AI for Science, Berlin, Germany（微软研究院人工智能科学部，柏林，德国）；Department of Physics, Rice University, Houston, Texas 77005, USA（物理系，里士大学，休斯顿，德克萨斯州77005，美国）
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）；Suzhou Laboratory（苏州实验室）；Chalmers University of Technology（查尔姆斯理工大学）；Department of Materials Science and Engineering, Westlake University（西湖大学材料科学与工程系）；School of Science, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）理学院）；Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology（南方科技大学材料科学与工程系微/纳孔功能材料深圳市重点实验室）；Department of Materials Science and Engineering, City University of Hong Kong（香港城市大学材料科学与工程系）
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Technische Universität Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Google Research（谷歌研究院）
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
University of Cambridge（剑桥大学）；BASF SE, Group Research（巴斯夫股份公司集团研究）；BASF Chemicals India Pvt. Ltd.（巴斯夫印度化工有限公司）；California Institute of Technology（加州理工学院）
Fast and flexible long-range models for atomistic machine learning
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院 IMX 计算科学与建模实验室）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院 MARVEL 新型材料计算设计与发现国家中心）
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
Institute of Informatics and Telecommunications（信息学与电信研究所）；Institute of Nanoscience and Nanotechnology（纳米科学与纳米技术研究所）；National Centre for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）；SciFY P.N.P.C.（SciFY公共非营利公司）；School of Chemical Engineering（化学工程学院）；National Technical University of Athens（雅典国立技术大学）
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Google Research（谷歌研究院）；Technische Universität Berlin（柏林工业大学）；University of Luxembourg（卢森堡大学）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Institute for Theoretical Chemistry, University of Stuttgart（斯图加特大学理论化学研究所）；Institute for Computational Physics, University of Stuttgart（斯图加特大学计算物理研究所）
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）
