Accelerated Dynamics of Surface Chemical Reactions

1600218 PI：Ramprasad 机构名称：康涅狄格大学标题：表面化学反应的加速动力学表面化学反应在自然现象中无处不在，在多相催化、晶体生长和电化学等多个科学学科中发挥着关键作用。 然而，获得表面化学反应机制和动力学细节的分子水平图像仍然是一项艰巨的任务。目前的调查方法，无论是经验的还是计算的，都只能为我们提供对这个复杂世界的有限看法。实现必要的理解水平的一种方法是通过使用分子动力学（MD）模拟。这些模拟可以在分子水平上直接监测表面反应的进展，作为各种相关条件的函数。然而，在当今的MD能力中仍然存在重大差距：它们要么是快速的（但不是通用的或准确的），例如，基于经验力场的那些，或者它们是通用的和精确的（但不是有效的），例如，基于量子力学（或从头算）方法的那些。拟议的工作将利用一种自适应机器学习方案，既可以加速从头算MD模拟，也可以在运行中创建准确的力场（无需额外成本）。以前用量子力学模拟无法达到的时间尺度可以用这种新的范式来访问（可以想象，毫秒到秒），同时仍然保持量子力学的保真度。从头算MD模拟缓慢的主要原因很大程度上是因为渗透到当今范式中的巨大冗余。能量和力，执行MD模拟所需的成分，被评估为每个被访问的配置，无论一个新的配置是否类似于以前访问的配置。该提议的基本前提是，基于机器学习的方法可以用于消除在局部最小值内以及在等效的多个局部最小值处预测重访或类似状态的原子力和能量所涉及的重大努力，从而消除大量冗余。只有当遇到一个真正新的配置时，才需要从头算方案;否则，廉价的机器学习算法被用来预测原子力和能量。在前一种情况下，学习算法被重新训练以包括新信息，从而使预测方案在运行中自适应。提出的研究的具体目标是：（1）独立力场的发展，只是使用从头算MD模拟过程中积累的数据;和（2）这一发展的重要模型表面科学问题的应用，包括表面吸附原子扩散，表面氧化和表面催化反应。还计划了几项教育举措，包括新课程、讲习班、在线讲座、短期课程和研讨会。

1600218 PI: Ramprasad Institution: University of ConnecticutTitle: Accelerated Dynamics of Surface Chemical ReactionsSurface chemical reactions are ubiquitous in natural phenomena and play a key role in several scientific disciplines, such as heterogeneous catalysis, crystal growth, and electrochemistry. Achieving a molecular-level picture of the mechanisms and dynamical detail of surface chemical reactions though remains a daunting task. Current methods of inquiry, be it empirical or computational, provide us with only a limited view of this complex world. One way of achieving the requisite level of understanding is by the use of molecular dynamics (MD) simulations. These simulations can directly monitor the progression of reactions at surfaces at the molecular level as a function of a variety of relevant conditions. Nevertheless, significant gaps remain in present-day MD capabilities: they are either fast (but not versatile or accurate), e.g., those based on empirical force-fields, or they are versatile and accurate (but not efficient), e.g., those based on quantum mechanical (or ab initio) methods. The proposed work will exploit an adaptive machine learning scheme that can both accelerate ab initio MD simulations as well as create accurate force-fields (at no extra cost) on-the-fly. Timescales previously unreachable using quantum mechanical simulations may be accessed using this new paradigm (conceivably,milliseconds to seconds), while still preserving the fidelity of quantum mechanics.The primary reason ab-initio MD simulations are slow is largely because of enormous redundancies that permeate present-day paradigms. Energies and forces, the ingredients necessary to perform MD simulations, are evaluated for every configuration that is visited, regardless of whether a new configuration is similar to a previously visited configuration. The basic premise underlying this proposal is that a methodology based on machine learning can be used to eliminate the significant effort involved in predicting atomic forces and energies of revisited or similar states within a local minimum, and at equivalent multiple local minima, thus eliminating an enormous amount of redundancies. Only when a truly new configuration is encountered is an ab initio scheme necessary; otherwise, the inexpensive machine learning algorithm is used to predict atomic forces and energies. In the former instance, the learning algorithm is retrained to include the new information, thus making the prediction scheme adaptive on-the-fly. The specific goals of the proposed research are: (1) The development of stand-alone force-fields just using the data accumulated during ab initio MD simulations; and (2) Application of this development to important model surface science problems, including surface adatom diffusion, surface oxidation, and surface catalytic reactions. Several educational initiatives are also planned, including new course offerings, workshops, online lectures, short courses, and symposia.