Discovery of the molecular candidates for applications in drug targets,
biomolecular systems, catalysts, photovoltaics, organic electronics, and
batteries, necessitates development of machine learning algorithms capable of
rapid exploration of the chemical spaces targeting the desired functionalities.
Here we introduce a novel approach for the active learning over the chemical
spaces based on hypothesis learning. We construct the hypotheses on the
possible relationships between structures and functionalities of interest based
on a small subset of data and introduce them as (probabilistic) mean functions
for the Gaussian process. This approach combines the elements from the symbolic
regression methods such as SISSO and active learning into a single framework.
Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly
to datasets from both domains of molecular and solid-state materials sciences.