Machine Learning Prediction for Fe(II) Spin-Crossover Complex in the Same Spin State Using Geometrical and Topological Descriptors

-SSD. Using this data set, we built machine learning models to distinguish between SCO-undergoing and non-SCO complexes crystallized in the same spin state (either HS or LS). The classification results showed that the key factors for predicting SCO activity differed between the two spin states: in the HS regime, local geometric distortions, such as Fe-N bond elongation and octahedral deformation, were important, whereas in the LS regime, ligand-derived chemical and steric factors were important. Overall, the many-body tensor representation as a descriptor set achieved high prediction accuracy in both spin states. The influence of environmental factors, such as solvents and counterions, on SCO classification was inconsistent across representations.