Physics-informed active learning for accelerating quantum chemical
  simulations
Yi-Fan Hou Lina Zhang Quanhao Zhang Fuchun Ge Pavlo O. Dral
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, automatic selection of initial data, and uncertainty
quantification. The versatility of this protocol is shown in our implementation
of quasi-classical molecular dynamics for simulating vibrational spectra,
conformer search of a key biochemical molecule, and time-resolved mechanism of
the Diels-Alder reaction. These investigations took us days instead of weeks of
pure quantum chemical calculations on a high-performance computing cluster.