ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields

XZ Geng and JN Gu and GW Qin and LW Wang and XY Meng, JOURNAL OF CHEMICAL PHYSICS, 162, 052502 (2025).

DOI: 10.1063/5.0247559

Machine Learning Force Fields (MLFFs) require ongoing improvement and innovation to effectively address challenges across various domains. Developing MLFF models typically involves extensive screening, tuning, and iterative testing. However, existing packages based on a single mature descriptor or model are unsuitable for this process. Therefore, we developed a package named ABFML, based on PyTorch, which aims to promote MLFF innovation by providing developers with a rapid, efficient, and user-friendly tool for constructing, screening, and validating new force field models. Moreover, by leveraging standardized module operations and cutting-edge machine learning frameworks, developers can swiftly establish models. In addition, the platform can seamlessly transition to the graphics processing unit environments, enabling accelerated calculations and large-scale parallel simulations of molecular dynamics. In contrast to traditional from-scratch approaches for MLFF development, ABFML significantly lowers the barriers to developing force field models, thereby expediting innovation and application within the MLFF development domains.

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