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

氢键（H-键）的形成和质子运输在许多生物和技术相关的过程中发挥着关键作用。酶中的氢键网络在加速化学反应速率方面至关重要，而高质子迁移率对水化学和燃料电池的发展具有重要意义。然而，复杂体系中质子位置和质子转移机理的精确信息很难通过实验获得。此外，小质量的氢原子还产生了一些不可忽视的核量子效应，如隧穿、零点运动和量子离域等。虽然从头计算的计算机模拟，明确考虑的量子性质的电子和原子核给重要的洞察力的氢键的性质，他们的高计算成本严重限制了他们的application.The拟议的项目的目的是开发一个计算框架，允许详细调查质子运输和氢键在复杂的凝聚系统的准确和有效的量子模拟。通过将有效的机器学习势与加速量子模拟收敛的方法相结合，传统从头算方法的计算负担将减少两个数量级以上，而不会损失准确性。这种新的方法将被应用于调查的量子动力学的浓酸溶液和研究的H-键网络的酶HIV天冬氨酸蛋白酶，抗HIV药物的重要目标的活性位点。除了这些应用程序，所提出的方法代表了一个通用的框架，加速量子模拟，这将适用于大范围的系统中，轻原子的存在需要明确包括原子核的量子性质。

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.