  The ability to understand and engineer molecular structures relies on having
accurate descriptions of the energy as a function of atomic coordinates. Here
we outline a new paradigm for deriving energy functions of hyperdimensional
molecular systems, which involves generating data for low-dimensional systems
in virtual reality (VR) to then efficiently train atomic neural networks
(ANNs). This generates high quality data for specific areas of interest within
the hyperdimensional space that characterizes a molecule's potential energy
surface (PES). We demonstrate the utility of this approach by gathering data
within VR to train ANNs on chemical reactions involving fewer than 8 heavy
atoms. This strategy enables us to predict the energies of much
higher-dimensional systems, e.g. containing nearly 100 atoms. Training on
datasets containing only 15K geometries, this approach generates mean absolute
errors around 2 kcal/mol. This represents one of the first times that an
ANN-PES for a large reactive radical has been generated using such a small
dataset. Our results suggest VR enables the intelligent curation of
high-quality data, which accelerates the learning process.
