  High-throughput computational screening has emerged as a critical component
of materials discovery. Direct density functional theory (DFT) simulation of
inorganic materials and molecular transition metal complexes is often used to
describe subtle trends in inorganic bonding and spin-state ordering, but these
calculations are computationally costly and properties are sensitive to the
exchange-correlation functional employed. To begin to overcome these
challenges, we trained artificial neural networks (ANNs) to predict
quantum-mechanically-derived properties, including spin-state ordering,
sensitivity to Hartree-Fock exchange, and spin- state specific bond lengths in
transition metal complexes. Our ANN is trained on a small set of
inorganic-chemistry-appropriate empirical inputs that are both maximally
transferable and do not require precise three-dimensional structural
information for prediction. Using these descriptors, our ANN predicts
spin-state splittings of single-site transition metal complexes (i.e., Cr-Ni)
at arbitrary amounts of Hartree-Fock exchange to within 3 kcal/mol accuracy of
DFT calculations. Our exchange-sensitivity ANN enables improved predictions on
a diverse test set of experimentally-characterized transition metal complexes
by extrapolation from semi-local DFT to hybrid DFT. The ANN also outperforms
other machine learning models (i.e., support vector regression and kernel ridge
regression), demonstrating particularly improved performance in
transferability, as measured by prediction errors on the diverse test set. We
establish the value of new uncertainty quantification tools to estimate ANN
prediction uncertainty in computational chemistry, and we provide additional
heuristics for identification of when a compound of interest is likely to be
poorly predicted by the ANN.
