CAREER: Accurate Electrochemical Barriers Accelerated by Machine-learning

摘要（彼得森;1553365）这项研究将促进电催化的理论理解的进步，加速机器学习工具。 由此产生的理解将有助于开发相关技术，如太阳能燃料设备、电池、燃料电池和电解槽-所有这些都与可再生能源和可持续技术的商业化有关。 该研究还将为美国和肯尼亚农村地区的各级学生提供教育机会，并将通过首席研究员的公开代码“EARO”促进在广泛的研究社区中引入高保真加速原子计算。电子结构理论近年来彻底改变了非均相催化剂设计，然而，由于计算过渡态能垒（其通常决定催化性能）的困难，在电催化中具有更大的挑战。 这项研究将提供第一个系统的研究，这种电位依赖的电催化屏障在一系列的催化材料和吸附的反应物，从而促进新材料和电催化过程的发现。 过渡态计算将通过开发独特的、以原子为中心的机器学习工具来实现，这些工具可以大大加速原子计算，同时匹配计算密集型（通常非常耗时）的“从头算”计算的准确性。 PI的可编程软件将以原子为中心的机器学习模块化，并将用于加速搜索局部最小值，过渡状态和全局最小值的势能表面。 此外，机器学习提供的加速也将有助于引入与电化学环境相关的复杂现象，例如溶剂效应和典型材料的大单元电池。 更广泛地说，该项目将提供信息，PI将使用这些信息作为教学工具，向学生传达反应可视化，从当地高中和布朗大学的本科生和研究生到肯尼亚农村一所新的科学技术大学（JOOUST）的学生。 此外，随着新的共享应用程序的不断开发，将加速与能源和环境相关的广泛应用中的材料发现。

Abstract (Peterson;1553365)The study will promote advances in theoretical understanding of electrocatalysis as accelerated by machine-learning tools. The resulting understanding will aid the development of related technologies such as solar-fuel devices, batteries, fuel cells and electrolyzers - all of relevance to renewable energy and the commercialization of sustainable technologies. The research will also provide educational opportunities to students at various levels in both the U.S. and in rural Kenya, and will facilitate introduction of high-fidelity, accelerated atomistic calculations across a broad research community via the principal investigator's publicly available code, "Amp".Electronic structure theory has revolutionized heterogeneous catalyst design in recent years, however it has had greater challenges in electrocatalysis due to the difficulty of calculating transition state energy barriers (which often dictate catalytic performance). This study will provide the first systematic study of such potential-dependent electrocatalytic barriers across a range of catalytic materials and adsorbed reactants, thus facilitating the discovery of new materials and electrocatalytic processes. The transition-state calculations will be enabled by the development of unique, atom-centered, machine-learning tools that dramatically accelerate atomistic calculations while matching the accuracy of computationally-intensive (and often exceedingly time-consuming) "ab initio" calculations. The PI's Amp software modularizes atom-centered machine learning, and will be used to accelerate the search of potential energy surfaces for local minima, transition states, and global minima. Moreover, the acceleration provided by machine learning will also facilitate the introduction of complicated phenomena associated with the electrochemical environment such as solvent effects and large unit cells of typical materials. More broadly, the project will provide information that will be used by the PI as teaching tools to convey reaction visualization to students ranging from local high school and Brown undergraduate and graduate students to students at a new science and technology university (JOOUST) in rural Kenya. Moreover, continued development of Amp, with new shared applications, will accelerate materials discovery across a broad range of applications related to energy and the environment.