The world is facing an ever-increasing demand for energy and resources as the scarcity of resources and the pollution of the environment are forcing us to redesign the foundations of global economies. New methods of producing „green“ energy and chemical base materials are in heavy demand. Hydrogen generated from environmentally neutral processes has the potential to provide both: a zero-emission energy carrier and chemical feedstock. However, the processes needed for the „clean“ production of hydrogen are not yet economically viable on a large scale. This project explores a novel way to generate hydrogen by splitting water into its elements, H₂ and O₂.

A key goal of modern energy research is to find efficient ways to achieve this splitting. The process relies on the efficient reduction of water hydrogen and oxidation of water oxygen. It has long been known that electrons solvated in water are the ideal, most direct agents to induce this reduction, but typically generating them has required harsh reaction conditions that have limited this approach. Very recently, however, a relatively mild production process was experimentally achieved using hydrogen-covered nanodiamonds illuminated by light. The process is conceived as follows: (1) The nanodiamond is excited and an electron moves towards the particle‘s surface, which permits (2) the electron to transfer into the interfacial water and (3) to move into the solution, where (4) it eventually reacts. Still we are far from understanding the precise mechanism underlying this effect, which is a key to improving and scaling-up its performance.

To learn more about the electron generating processes, we plan to model the electron transfer and solvation dynamics (described in (1)-(3) above) using coupled multi-scale electron and nuclear dynamics methods. Additionally, we will optimize the reaction paramters through a combination of quantum chemistry and machine learning. Steps (1-2) require intricate quantum electron dynamics (ED) calculations, which can be done only for a small number of molecular conformations. Steps (2-3) rely on electron hopping/transfer rates in conjunction with statistical interface physics and simulations of the molecular dynamics (MD) of the diamond/water interface. Deep learning will be used to approximate results from ED to parametrize MD simulations and create a time-dependent multi-physics description of the full process. This should give us a significantly better understanding of the system. Subsequently, we will use methods of optimal control to find the most efficient electron solvation process, in which the optimal control parameters are surface decoration, UV pulse (intensity, duration, shape), and temperature. Furthermore, the nanodiamonds‘ electronic properties will be optimized for excitation by sunlight through an approach that combines density functional theory (DFT) and supervised machine learning.