  The development of accurate and transferable machine learning (ML) potentials
for predicting molecular energetics is a challenging task. The process of data
generation to train such ML potentials is a task neither well understood nor
researched in detail. In this work, we present a fully automated approach for
the generation of datasets with the intent of training universal ML potentials.
It is based on the concept of active learning (AL) via Query by Committee
(QBC), which uses the disagreement between an ensemble of ML potentials to
infer the reliability of the ensemble's prediction. QBC allows the presented AL
algorithm to automatically sample regions of chemical space where the ML
potential fails to accurately predict the potential energy. AL improves the
overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test
cases by mitigating human biases in deciding what new training data to use. AL
also reduces the training set size to a fraction of the data required when
using naive random sampling techniques. To provide validation of our AL
approach we develop the COMP6 benchmark (publicly available on GitHub), which
contains a diverse set of organic molecules. Through the AL process, it is
shown that the AL-based potentials perform as well as the ANI-1 potential on
COMP6 with only 10% of the data, and vastly outperforms ANI-1 with 25% the
amount of data. Finally, we show that our proposed AL technique develops a
universal ANI potential (ANI-1x) that provides accurate energy and force
predictions on the entire COMP6 benchmark. This universal ML potential achieves
a level of accuracy on par with the best ML potentials for single molecule or
materials, while remaining applicable to the general class of organic molecules
comprised of the elements CHNO.
