KLIFF: A framework to develop physics-based and machine learning interatomic potentials

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.

原子间势（Interatomic Potentials，IPs）是一类降阶模型，用于根据原子的空间位置与种类计算原子系统的势能。原子间势将原子视为经典粒子，无需显式模拟电子，因此计算成本远低于第一性原理方法，可实现更大尺度体系、更长时间跨度的分子模拟。开发原子间势是一个复杂的迭代过程，包含多个核心步骤：构建训练集、设计泛函形式、优化泛函参数、测试模型质量，以及将模型部署至分子模拟软件包。本文介绍了基于KIM的学习集成拟合框架（KLIFF），一款可助力完整原子间势开发流程的软件包。KLIFF可同时支持基于物理的原子间势与机器学习原子间势。该框架采用模块化设计，拟合流程中的各类组件（如原子环境描述符、泛函形式、损失函数、优化器、质量分析器等）可实现无缝协同，为新型原子间势形式的快速设计提供了灵活的开发框架。经训练的原子间势可兼容原子间模型知识库（Knowledgebase of Interatomic Models，KIM）应用程序接口（Application Programming Interface，API），并可直接用于支持KIM的主流材料模拟软件包，包括ASE、DL_POLY、GULP、LAMMPS与QC。KLIFF采用Python编写，计算密集型组件则通过C++实现；其支持数据并行，可兼容共享内存多核桌面设备与高性能分布式内存计算集群。我们通过为硅体系拟合基于物理的斯蒂林格-韦伯势（Stillinger–Weber potential）与机器学习神经网络势，展示了KLIFF的具体使用方法。KLIFF软件包及其文档可通过以下链接公开获取：https://github.com/openkim/kliff。