Machine Learning for Predicting Electron Transfer Coupling

Abstract

Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10−10 4 times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed.

Decoding chemical information from vibrational spectroscopy data: Local vibrational mode theory

“…33 It has to be noted that in this connection the term electronic coupling refers to the potential energy and should not be confused with the use of the term electronic coupling in electron and/or excitation energy transfer theory. 158,159 The electronic coupling can be eliminated via the Wilson GF-matrix formalism applied to Eq. (1), 33,82,160,161 i.e.…”

Exchange Spin Coupling from Gaussian Process Regression

“…A possible solution to this problem are machine-learning (ML) approaches 31,32 , which have seen an enormous gain in popularity and development in the past few years in the chemistry, materials science and solid-state physics communities , and which may save orders of magnitude in computing time compared to DFT, and often comparable or even better accuracy. ML has proven successful for spin-dependent molecular properties, in particular for spin-state energies in spin crossover complexes 35,36,[54][55][56][57] , magnetic moments and magnetic anisotropy 58,59 , and also for properties closely related 60,61,[61][62][63][64][65][66][67][68] to exchange spin coupling such as charge transfer [69][70][71] and excitation energy transfer 71,72 . The capability of ML for exchange spin coupling has not been explored yet.…”

Reorganization energies and spectral densities for electron transfer problems in charge transport materials

“…A future possibility is to use machine learning for a better ensemble averaging and higher resolution in the spectral density function. 105 The quality of MD simulation results highly depends on the force field used. 96 A polarizable force field should offer a better solution.…”