本文提出 KLIFF 框架，通过模块化设计统一支持基于物理和机器学习的原子间势开发，并与 KIM API 及主流模拟软件兼容，实现从训练到部署的高效灵活流程。
摘要：
原子间势（IPs）是用于根据原子在空间中的位置和种类计算原子系统势能的降阶模型。IPs 将原子视为经典粒子，不显式建模电子，因此其计算成本远低于第一性原理方法，从而能够在更长时间尺度上对显著更大的体系进行分子模拟。开发一个 IP 是一个复杂的迭代过程，涉及多个步骤：组装训练集、设计函数形式、优化函数参数、测试模型质量以及部署到分子模拟软件包。本文介绍了基于 KIM 的学习集成拟合框架（KLIFF），这是一个促进整个 IP 开发流程的软件包。KLIFF 同时支持基于物理和机器学习的 IPs。它采用模块化方法，使拟合过程中的各种组件（如原子环境描述符、函数形式、损失函数、优化器、质量分析器等）能够彼此无缝协作。这为快速设计新的 IP 形式提供了一个灵活的框架。训练好的 IPs 与原子间模型知识库（KIM）应用程序编程接口（API）兼容，并且可以直接用于与 KIM 兼容的主要材料模拟软件包，包括 ASE、DL_POLY、GULP、LAMMPS 和 QC。KLIFF 使用 Python 编写，计算密集型组件以 C++ 实现。它在数据层面进行并行化，同时支持共享内存多核台式机和高性能分布式内存计算集群。我们通过为硅拟合一个基于物理的 Stillinger--Weber 势和一个机器学习神经网络势来演示 KLIFF 的使用。KLIFF 软件包及其文档可在以下地址公开获取：https://github.com/openkim/kliff。
英文摘要：
Interatomic potentials (IPs) are reduced-order models for calculating the potential energy of a system of atoms given their positions in space and species. IPs treat atoms as classical particles without explicitly modeling electrons and thus are computationally far less expensive than first-principles methods, enabling molecular simulations of significantly larger systems over longer times. Developing an IP is a complex iterative process involving multiple steps: assembling a training set, designing a functional form, optimizing the function parameters, testing model quality, and deployment to molecular simulation packages. This paper introduces the KIM-based learning-integrated fitting framework (KLIFF), a package that facilitates the entire IP development process. KLIFF supports both physics-based and machine learning IPs. It adopts a modular approach whereby various components in the fitting process, such as atomic environment descriptors, functional forms, loss functions, optimizers, quality analyzers, and so on, work seamlessly with each other. This provides a flexible framework for the rapid design of new IP forms. Trained IPs are compatible with the Knowledgebase of Interatomic Models (KIM) application programming interface (API) and can be readily used in major materials simulation packages compatible with KIM, including ASE, DL_POLY, GULP, LAMMPS, and QC. KLIFF is written in Python with computationally intensive components implemented in C++. It is parallelized over data and supports both shared-memory multicore desktop machines and high-performance distributed memory computing clusters. We demonstrate the use of KLIFF by fitting a physics-based Stillinger--Weber potential and a machine learning neural network potential for silicon. The KLIFF package, together with its documentation, is publicly available at: https://github.com/openkim/kliff.
Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）；School of Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程学院）
Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal-Salt Systems for Calcium Electrolysis
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Digital Materials LLC（数字材料有限责任公司）；Institute of High Temperature Electrochemistry, Ural Branch of the Russian Academy of Sciences（俄罗斯科学院乌拉尔分院高温电化学研究所）
Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO$_2$ and UO$_2$
Idaho National Laboratory（爱达荷国家实验室）；Columbia University（哥伦比亚大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；The Pennsylvania State University（宾夕法尼亚州立大学）；The Ohio State University（俄亥俄州立大学）
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
Southern University of Science and Technology（南方科技大学）；Shanghai University（上海大学）；Materials Genome Institute（材料基因组研究院）；Institute of Innovative Materials（创新材料研究院）
Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials
University of British Columbia（不列颠哥伦比亚大学）；Symmetric Group（对称集团）；MatNex；University of Cambridge（剑桥大学）；Max Planck Institute for Polymer Research（马克斯·普朗克高分子研究所）
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）
An Active Learning Interatomic Potential For Defect-Engineered CoCrFeMnNi High-Entropy Alloy
University of Tokyo（东京大学）；Indian Institute of Technology Gandhinagar（甘地纳加尔印度理工学院）；Herbert Gleiter International Institute, Liaoning Academy of Materials（赫伯特·盖勒国际研究所，辽宁材料研究院）；Regional Institute of Education（地区教育学院）
Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond
Ruhr-Universität Bochum（波鸿鲁尔大学）；Federal Institute for Materials Research and Testing（联邦材料研究与测试研究所）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希·席勒大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
Lawrence Berkeley National Laboratory（伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Department of Materials Science and Engineering（材料科学与工程系）
Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric
Fritz-Haber-Institut der Max-Planck-Gesellschaft（马克斯·普朗克学会弗里茨·哈伯研究所）；Department of Chemistry and Biochemistry, University of South Carolina（南卡罗来纳大学化学与生物化学系）；Department of Chemical Engineering, University of California, Davis（加州大学戴维斯分校化学工程系）；Department of Materials, Imperial College London（帝国理工学院材料系）；Department of Chemistry, Imperial College London（帝国理工学院化学系）；Department of Materials Science and Engineering, University of Florida（佛罗里达大学材料科学与工程系）；Department of Chemistry, Korea University（高丽大学化学系）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）
