Physics-informed active learning for accelerating quantum chemical simulations

arXiv:2404.11811v1 Announce Type: cross
Abstract: Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, …

abstract active learning arxiv cs.ai cs.lg machine machine learning physics physics.chem-ph physics-informed quantum robustness simulations the end type