A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors

TL;DR

This paper introduces a novel machine learning approach with specialized descriptors and phase correction for accurately predicting nonadiabatic couplings, enabling efficient and precise photochemical simulations.

Contribution

The authors develop NAC-specific descriptors and a phase-correction method, achieving unprecedented accuracy in machine learning of nonadiabatic couplings for photochemical modeling.

Findings

Achieved $R^2$ exceeding 0.99 in NAC prediction.

Enabled ML-driven FSSH simulations with accurate $S_1$ decay.

Reduced error bars by running large ensembles of trajectories.

Abstract

Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH). There is therefore a strong incentive to machine learn NACs for accelerating simulations. However, this is challenging due to NACs' vectorial, double-valued character and the singularity near a conical intersection seam. For the first time, we design NAC-specific descriptors based on our domain expertise and show that they allow learning NACs with never-before-reported accuracy of $R^2$ exceeding 0.99. The key to success is also our new ML phase-correction procedure. We demonstrate the efficiency and robustness of our approach on a prototypical example of fully ML-driven FSSH simulations of fulvene targeting the SA-2-CASSCF(6,6) electronic structure level. This ML-FSSH dynamics leads to an accurate description of $S_1$ decay while reducing error bars by allowing the execution of a large ensemble of trajectories. Our implementations are available in open-source MLatom.