  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 reactions. These investigations
took us days instead of weeks of pure quantum chemical calculations on a
high-performance computing cluster. The code in MLatom and tutorials are
available at https://github.com/dralgroup/mlatom.
