Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA

Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the intermolecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. In particular, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus, this possesses unique advantages in many applications.

Automated summary

This study introduces an efficient machine learning approach for calculating electronic couplings between quasi-diabatic molecular orbitals (MOs) in flexible molecular aggregates without the need for nonadiabatic coupling calculations. By selecting rigid molecule MOs as references, tracking and phase correcting quasi-diabatic MOs in molecular dynamics, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and intermolecular electronic couplings. The method can independently calculate electronic couplings between different quasi-diabatic MOs, providing unique advantages for various applications.