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, uncertainty quantification, and convergence monitoring.
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.