Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

Abstract

Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN) based on diabatic states to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3,100 hypothetical molecules, and identify novel species with extremely high predicted quantum yields. The model predictions are confirmed using high accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• A spin-adapted Density Matrix Renormalization Group algorithm for quantum chemistry
• Equivariant message passing for the prediction of tensorial properties and molecular spectra
• Nonadiabatic ab initio molecular dynamics including spin-orbit coupling and laser fields
• Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

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• Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations
• Optical line shapes of color centers in solids from classical autocorrelation functions