Machine learning potentials and excited-state molecular dynamics

Artificial neural networks can learn the relationship between the nuclear geometry of a molecule and the corresponding potential energy and, in this way, serve as a highly accurate and extremely fast tool for predicting potential energy surfaces [1]. As an application, the simulation of an organic reaction with such a machine learning algorithm is presented [2]. Furthermore, with the right neural network setup, the chemical locality is inherently exploited, where the local region of a large molecule is only weakly influenced by the atoms that are far from the region of interest [3]. As a result, a vast amount of computational time can be saved when generating the so-called reference data from which the neural network learns the shape of the potential energy surfaces. Such improvements will enable us to perform on-the-fly dynamics calculations of large systems and long time scales in the future.
Currently, we still use highly accurate but time-consuming ab initio calculations in semiclassical molecular dynamics. Here, we study molecules in their excited electronic states by the surface hopping method, where the nuclei move classically on potentials computed by quantum mechanics. We have modified the surface hopping approach to incorporate not only kinetic dynamical couplings but also any other arbitrary coupling in our so-called SHARC (surface hopping including arbitrary couplings) method [4, 5]. The corresponding code is also publicly available [6]. Especially spin-orbit couplings can now be treated in on-the-fly simulations. The method is applied to a variety of systems, showing e.g. that intersystem crossing can take place on a femtosecond timescale even in organic molecules with only relatively small spin-orbit couplings (see [5] and references therein).

References
[1] J. Behler, J. Phys.: Condens. Matter, 26 (2014) 183001.
[2] M. Gastegger, P. Marquetand J. Chem. Theory Comput., 11 (2015) 2187-2198.
[3] M. Gastegger, C. Kauffmann, J. Behler, P. Marquetand, J. Chem. Phys., 144 (2016) 194110.
[4] M. Richter, P. Marquetand, J. González-Vázquez, I. Sola, L. González, J. Chem. Theory, Comput., 7 (2011) 1253-1258.
[5] S. Mai, P. Marquetand, L. González, Int. J. Quantum Chem., 115 (2015) 1215-1231.
[6] S. Mai, M. Richter, M. Ruckenbauer, M. Oppel, P. Marquetand, L. González, SHARC, Program Package for Non-Adiabatic Dynamics, sharc-md.org (2014).