Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression

Abstract

The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme-oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe 2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems.

Mean Value Ensemble Hubbard-U Correction for Spin-Crossover Molecules

“…Example systems include metal–organic complexes that switch not only the spin-state but also their electronic state and coordination structure. Those concurrent changes make such molecules particularly attractive for electronic devices in memory, display, and sensing applications. , Therefore, an obvious goal for theoretical and computational materials studies is to search and screen for promising SCO candidate molecules and their aggregates. − …”