Accessing the performance of CC2 for excited state dynamics: a benchmark study with pyrazine

TL;DR

This study evaluates RI-CC2's effectiveness in modeling ultrafast internal conversion in pyrazine, using advanced computational methods and machine learning to reproduce experimental decay times and identify key vibrational modes.

Contribution

It implements analytical gradients and nonadiabatic couplings for RI-CC2 in Q-Chem, and employs neural networks for accelerated on-the-fly dynamics, providing a valuable dataset for future ML developments.

Findings

RI-CC2 accurately reproduces experimental decay times of 22-26 fs.

Identifies key vibrational modes driving internal conversion.

Provides a high-quality dataset for machine learning applications.

Abstract

In this work, we access the performance of RI-CC2 for ultrafast internal conversion using pyrazine as a benchmark system. We implement analytical gradients and nonadiabatic coupling vectors for RI-CC2 in the Q-Chem package and employ them in two complementary approaches: a reduced-dimensionality vibronic coupling (VC) model and full-dimensional ab initio on-the-fly trajectory surface hopping simulations. To accelerate the on-the-fly dynamics, we employ a diabatic artificial neural network model trained on RI-CC2 data. Both the VC model and the full-dimensional dynamics reveal that the dark $A_\text{1u}$ state actively participates in the internal conversion process. RI-CC2 identifies the $Q_\text{9a}$ and $Q_\text{8a}$ vibrational modes as key drivers of the coherent population transfer between the $A_\text{1u}$ and $B_\text{3u}$. The on-the-fly dynamics reproduce the experimental $B_\text{2u}$ population decay time of 26 fs, consistent with the measured value of $22\pm3$ fs. The high-quality dataset of energies, forces, and nonadiabatic couplings generated here provides a valuable resource for future machine-learning developments, while the stochastic variant sRI-CC2 promises to extend such dynamics to larger molecular systems.