Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Machine learning
the electronic structure of open shell transition
metal complexes presents unique challenges, including robust and automated
data set generation. Here, we introduce tools that simplify data acquisition
from density functional theory (DFT) and validation of trained machine
learning models using the molSimplify automatic design (mAD) workflow.
We demonstrate this workflow by training and comparing the performance
of LASSO, kernel ridge regression (KRR), and artificial neural network
(ANN) models using heuristic, topological revised autocorrelation
(RAC) descriptors we have recently introduced for machine learning
inorganic chemistry. On a series of open shell transition metal complexes,
we evaluate set aside test errors of these models for predicting the
HOMO level and HOMO–LUMO gap. The best performing models are
ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on
the HOMO level and HOMO–LUMO gap, respectively. Poor performing
KRR models using the full 153-feature RAC set are improved to nearly
the same performance as the ANNs when trained on down-selected subsets
of 20–30 features. Analysis of the essential descriptors for
HOMO level and HOMO–LUMO gap prediction as well as comparison
to subsets previously obtained for other properties reveal the paramount
importance of nonlocal, steric properties in determining frontier
molecular orbital energetics. We demonstrate our model performance
on diverse complexes and in the discovery of molecules with target
HOMO–LUMO gaps from a large 15,000 molecule design space in
minutes rather than days that full DFT evaluation would require.