Light-triggered processes, which are ubiquitous in nature and technology, are inherently quantum.  Phenomena such as photovoltaic effect, charge migration, and proton-coupled electron transfer require quantum mechanical description. Understanding and optimizing these processes is the key to novel technologies such as optogenetics, photopharmacology, and photoresponsive materials. However, an accurate description of these processes in (supra)molecular systems is still a demanding task due to: i) the need of high-level electronic structure calculations; (ii) coupled electron-nuclear dynamics; (iii) the importance of the environment.<br/>Recent years have seen a continuous growth of the direct dynamics approaches, e.g. semiclassical trajectory surface hopping or on-the-fly quantum dynamics within variational multi-configuration Gaussian, to study coupled electron-nuclear dynamics of photo-active molecules of moderate size. Particularly troublesome for trajectory-based methods is the huge amount of electronic structure calculations that need to be performed during the simulation. In this work, we present an innovative solution, i.e. both semiclassical and quantum direct dynamics formulations are combined with a database, within which the amount of electronic structure calculations is drastically reduced, by employing machine-learning algorithms and methods borrowed from the realm of artificial intelligence. On-the-fly direct dynamics can be further embedded into a quantum mechanics/molecular mechanics, for the explicit inclusion of the environment. The degree of sophistication, which can be achieved by such an implementation will be illustrated with a test system.

Databased accelerated on-the-fly hybrid quantum/classical dynamics

ACS Spring 2022

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