Computing linear optical spectra in the presence of nonadiabatic effects on Graphics Processing Units using molecular dynamics and tensor-network approaches

TL;DR

This paper compares two GPU-accelerated methods for computing linear optical spectra with nonadiabatic effects in condensed phases, using molecular dynamics and tensor-network techniques, applied to model systems and pyrazine.

Contribution

It introduces and evaluates two open-source strategies for spectral calculations that incorporate nonadiabatic effects with GPU acceleration, combining Gaussian Non-Condon Theory and vibronic coupling approaches.

Findings

Both methods efficiently compute spectra with nonadiabatic effects.

Tensor-network approaches enable exact quantum dynamics calculations.

GPU implementation allows rapid analysis of complex systems.

Abstract

We compare two recently developed strategies, implemented in open source software packages, for computing linear optical spectra in condensed phase environments in the presence of nonadiabatic effects. Both approaches rely on computing excitation energy and transition dipole fluctuations along molecular dynamics (MD) trajectories, treating molecular and environmental degrees of freedom on the same footing. Spectra are then generated in two ways: In the recently developed Gaussian Non-Condon Theory (GNCT), the linear response functions are computed in terms of independent adiabatic excited states, with non-Condon effects described through spectral densities of transition dipole fluctuations. For strongly coupled excited states, we instead parameterize a linear vibronic coupling (LVC) Hamiltonian directly from spectral densities of energy fluctuations and diabatic couplings computed along the MD trajectory. The optical spectrum is then calculated using powerful, numerically exact tensor-network approaches. Both the electronic structure calculations to sample system fluctuations and the quantum dynamics simulations using tensor-network methods are carried out on graphics processing units (GPUs), enabling rapid calculations on complex condensed phase systems. We assess the performance of the approaches using model systems in the presence of a conical intersection (CI), and the pyrazine molecule in different solvent environments.