Active Learning for Interatomic Potentials: A Review of Uncertainty-Driven Data Efficiency

Abstract

Developing a machine learning interatomic potential (MLIP) that is accurate for the specific conditions under which it will be applied, rather than simply for the predefined configurations chosen by the model developer, is a significant challenge in atomistic simulation. Active learning (AL) addresses this challenge by closing the loop between candidate generation, data selection, reference calculation, and model updating. Here, we survey more than seventy studies, with an emphasis on work from 2020 to 2026, to understand how active learning has evolved from a niche, on-the-fly training technique for query-by-committee and Gaussian process force fields into a diverse family of data-efficient strategies. he reviewed examples include on-the-fly molecular dynamics, active data selection for fine-tuning pretrained models, kernel- and representation-based acquisition using frozen pretrained models, and reaction-aware sampling for chemical systems far from equilibrium. We explore the use of active learning in elemental and alloy mechanics, hydrogen and ion diffusion, ferroelectrics and effective Hamiltonians of functional materials, reactive and photochemical dynamics, high-entropy and compositionally disordered materials, as well as generative and autonomous material discovery pipelines. Drawing on a growing body of critical, methodologically self-aware literature, we also show that this achievement carries systematic caveats. Uncertainty estimates are not interchangeable across model architectures and are frequently the least reliable exactly where they matter most. We close by discussing the outlook for standardized, uncertainty-calibrated, and observable-targeted active-learning workflows in the foundation-model era.