Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are
common tools for identifying
strongly correlated electronic structure that makes single-reference
(SR) methods (e.g., density functional theory or DFT) insufficient
for accurate property prediction. However, MR diagnostics typically
require computationally demanding correlated wave function theory
(WFT) calculations, and diagnostics often disagree or fail to predict
MR effects on properties. To overcome these challenges, we introduce
a semi-supervised machine learning (ML) approach with virtual adversarial
training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics
as inputs. In semi-supervised learning, only the most extreme SR or
MR points are labeled, and the remaining point labels are learned.
The resulting VAT model outperforms the alternatives, as quantified
by the distinct property distributions of SR- and MR-classified molecules.
To reduce the cost of generating inputs to the VAT model, we leverage
the VAT model’s robustness to noisy inputs by replacing WFT
MR diagnostics with regression predictions in an MR decision engine
workflow that preserves excellent performance. We demonstrate the
transferability of our approach to larger molecules and those with
distinct chemical composition from the training set. This MR decision
engine demonstrates promise as a low-cost, high-accuracy approach
to the automatic detection of strong correlation for predictive high-throughput
screening.