Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

ArXi:2605.13788v1 Announce Type: new Active learning for machine-learning interatomic potentials (MLIPs) must address several challenges to be practical: scaling to large candidate pools, leveraging energy-force supervision, and maintaining robustness when candidate pools are biased relative to the target distribution. In this work, we jointly address these challenges. We first