Modeling and simulating excited-state dynamics of a system in condensed phases: Machine Learning approach

Simulating quantum irreversible dynamics of exciton and electron transfer problems possesses a non-trivial challenge. Because irreversibility of the system dynamics results from quantum thermal activation and dissipation caused by a surrounding environment, it is necessary to include infinite environmental degrees of freedom in the simulation. Because the capability of full quantum dynamics simulation that includes the surrounding molecular degrees of freedom is limited, a practical approach is to employ a system-bath model, in which dynamics of excitons or electrons are described by a system Hamiltonian, while the other degrees of freedom, arising from the environmental molecules, are described by a harmonic oscillator bath (HOB) plus a system-bath interaction. By extending a previous study of a machine-learning approach for molecular liquids [J. Chem. Theory Comput. 2020, 16, 2099], here we construct a system-bath model for exciton and electron transfer problems. We determine both the system and system-bath interaction parameters that include the bath spectral distribution, using the electronic excitation energies obtained from Quantum Mechanics/Molecular Mechanics (QM/MM) simulations as a function of time. Using analytical expressions of optical response functions, we calculate linear and two-dimensional electronic (2DES) spectra for indocarbocyanine dimer in methanol. From these results, we demonstrate the capability of our approach to elucidate the non-equilibrium exciton dynamics of a quantum system in a non-intuitive manner.