Simulations of molecular photodynamics in long timescales

Abstract

Nonadiabatic dynamics simulations in the long timescale (much longer than 10 ps) are the next challenge in computational photochemistry. This paper delimits the scope of what we expect from methods to run such simulations: they should work in full nuclear dimensionality, be general enough to tackle any type of molecule and not require unrealistic computational resources. We examine the main methodological challenges we should venture to advance the field, including the computational costs of the electronic structure calculations, stability of the integration methods, accuracy of the nonadiabatic dynamics algorithms and software optimization. Based on simulations designed to shed light on each of these issues, we show how machine learning may be a crucial element for long time-scale dynamics, either as a surrogate for electronic structure calculations or aiding the parameterization of model Hamiltonians. We show that conventional methods for integrating classical equations should be adequate to extended simulations up to 1 ns and that surface hopping agrees semiquantitatively with wave packet propagation in the weak-coupling regime. We also describe our optimization of the Newton-X program to reduce computational overheads in data processing and storage. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.

Keywords: computational chemistry; dynamics simulations; excited states; nonadiabatic phenomena; photochemistry; theoretical chemistry.

Figure 1.

Performance of the KREG model for predicting the ground- and excited-state energies of an entire MD trajectory (test set) of protonated 7-azaindole. (Online version in colour.)

Figure 2.

Assessment of KREG model uncertainty for predicting ground- and excited-state energies of an entire surface hopping trajectory of protonated 7-azaindole. Each boxplot contains the distribution of RMSE values of energy predictions for one entire trajectory (test set) out of 50 independent MD simulations. The model was trained on 5000 molecular geometries randomly sampled from 49 trajectories after separating one trajectory for testing. This process was repeated until each one of the 50 MD trajectories were evaluated. The white dots in the boxplots represent the mean value of the RMSE distribution, while black dots indicate the outliers. (Online version in colour.)

Figure 3.

Distribution of total energy deviation as a function of time for different integration set-ups. Each dataset shows the mean (central curve) plus and minus one s.d. (shaded area) for points collected in intervals of 50 ps. Each set-up is characterized by the time step (0.01 or 0.5 fs), and random fluctuation (RF) added to the forces, 10⁻⁷ or 10⁻⁵ Hartree/Bohr. (Online version in colour.)

Figure 4.

The diabatic population decay of the excited state for different dynamics methodologies are shown in solid lines, and the corresponding fitted curves are presented in dashed lines. (Online version in colour.)