Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Spin-crossover (SCO) complexes are
materials that exhibit changes
in the spin state in response to external stimuli, with potential
applications in molecular electronics. It is challenging to know a
priori how to design ligands to achieve the delicate balance of entropic
and enthalpic contributions needed to tailor a transition temperature
close to room temperature. We leverage the SCO complexes from the
previously curated SCO-95 data set [Vennelakanti et al. J.
Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature
(T1/2) prediction using graph-based revised
autocorrelations as features. We perform feature selection using random
forest-ranked recursive feature addition (RF-RFA) to identify the
features essential to model transferability. Of the ML models considered,
the full feature set RF and recursive feature addition RF models perform
best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing
density functional approximations (DFAs) which accurately predict
SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing
DFAs. In addition, we study ML model predictions for a set of 18 SCO
complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the
RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson’s r of
0.82. In contrast, DFA-predicted T1/2 values
have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our
study demonstrates slightly superior performance of ML models in comparison
with some of the best-performing DFAs, and we expect ML models to
improve further as larger data sets of SCO complexes are curated and
become available for model training.