Equivariant Graph Neural Networks for Predicting Spin-Crossover Energy in Transition Metal Complexes

The behavior of spin-crossover materials is influenced by the choice of ligands coordinating a metallic center. Thus, spin-crossover can be tailored chemically to a wide range of application interests [J. Matter. Chem. C 2, 1360 (2014)]. However, the experimental exploration of such a vast combinatorial chemical space is cumbersome and expensive. Machine learning (ML) algorithms offer an alternative that can characterize the relationships between ligands and a property of interest [J. Phys. Chem. Lett. 9, 1064 (2018), Ind. Eng. Chem. Res. 57, 13973 (2018), Chem. Rev. 121, 9927 (2021)]. We describe the implementation of a two-step ML approach based on equivariant graph networks [arXiv:2102.09844v3]. First, an autoencoder is trained on a vast materials dataset. Then, we use the encoded embedding space to predict the adiabatic spin-crossover energy using a different dataset of fifteen hundred metal-organic solids, computed with the r²SCAN meta-generalized gradient density functional approximation [J. Phys. Chem. Lett. 11, 8208 (2020)]. Our efforts elucidate the efficacy of equivariant graph networks to predict novel spin crossover materials. This research has the potential to open new avenues for the efficient exploration of complex chemical spaces.