Understanding the Nature of Proton Transport and Hydrogen Bond Networks in Complex Environments by Accelerated Quantum Simulations

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Hydrogen bond (H-bond) formation and proton transport play a key role in numerous biologically and technologically relevant processes. H-bond networks in enzymes are critical in accelerating the rate of chemical reactions while the high proton mobility has important implications for aqueous chemistry and the development of fuel cells. However, accurate information on proton location and proton transfer mechanism in complex systems is difficult to obtain by experiment. Moreover, the small hydrogen mass gives rise to nuclear quantum effects such as tunneling, zero-point motion and quantum delocalization which can often not be neglected. While ab initio computer simulations which explicitly consider the quantum nature of the electrons and the nuclei give important insights into the properties of H-bonds, their high computational cost severely limits their application.The aim of the proposed project is to develop a computational framework which allows for a detailed investigation of proton transport and H-bonds in complex condensed systems by accurate and efficient quantum simulations. By combining efficient machine learning-potentials with methods to accelerate the convergence of quantum simulations, the computational burden of conventional ab initio methods will be reduced by more than two orders of magnitude without loss in accuracy. This novel approach will be applied to investigate the quantum dynamics of concentrated acid solutions and to study the H-bond network in the active site of the enzyme HIV aspartic protease, an important target for anti HIV-drugs. Beyond these applications, the proposed method represents a general framework for accelerating quantum simulations which will be applicable to a large range of systems in which the presence of light atoms requires to explicitly include the quantum nature of the nuclei.