ALF: Open-Source Active Learning Framework for Atomistic Modeling

Machine learning interatomic potentials (MLIPs) have surged in popularity over the last two decades, with many model architectures now openly available. As data-driven models, MLIPs critically depend on high-fidelity (i.e., physically accurate) training data produced by electronic structure calculations. However, assembling large and chemically diverse datasets can be a complex and time-consuming endeavor, often requiring the manual selection of representative atomic configurations and the execution of hundreds to millions of electronic structure simulations. To address this challenge, we introduce the Active Learning Framework (ALF), an open-source Python package designed to streamline the design and deployment of MLIP training datasets on High Performance Computing resources. ALF automatically selects new configurations from undersampled regions of the potential energy surface where the MLIP exhibits high uncertainty, schedules electronic structure calculations across available computational resources, and retrains MLIPs on the fly, thereby reducing manual intervention and limiting human bias. As a demonstration, we applied ALF to generate an actively learned dataset for molten salt mixtures consisting of F, Li, Na, Be, and K atoms. An MLIP trained on this data was then employed to predict melting point, viscosity, density, radial distribution function, and specific heat, which are computationally resource-intensive to evaluate via first-principles molecular dynamics. These results were subsequently validated against experimental data. Collectively, these findings illustrate ALF's effectiveness in compiling datasets that capture essential chemical and structural regimes, thereby virtually eliminating manual curation.