Physics-Informed Active Learning for Constructing Robust and Data-Efficient Machine Learning Potentials to Accelerate Quantum Chemical Simulations

An end-to-end active learning protocol based on physics-informed sampling, automatic selection of initial data, and uncertainty quantification enables the construction of robust and data-efficient machine learning potentials to significantly accelerate quantum chemical simulations.