aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields

Abstract

Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and material systems. Here we introduce aims-PAX, an expedited, multi-trajectory active learning framework that streamlines the development of stable and accurate MLFFs. Designed for a wide range of researchers, aims-PAX offers a modular, high-performance workflow that couples diversified sampling with scalable training across CPU and GPU architectures. Integrated with the widely used ab initio code FHI-aims, the framework supports state-of-the-art ML models and dataset generation using general-purpose (or "foundational") force-fields for rapid deployment in diverse systems. We demonstrate the capabilities of aims-PAX in various challenging tasks: creating datasets and models for highly flexible peptides, multiple organic molecules at once, explicitly solvated molecules, and for efficiently handling computationally demanding systems such as the CsPbI$_3$ perovskite. We show that aims-PAX achieves a reduction of up to three orders of magnitude in the number of required reference calculations, automatically selects challenging systems within a given chemical space, facilitates simulation of solvated molecules with more than thousand atoms, while enabling a ten-fold speedup in active-learning time through optimized resource utilization. This positions aims-PAX as a powerful and versatile platform for next-generation atomistic simulations in both academic and industrial settings.