CAREER: Accurate Electrochemical Barriers Accelerated by Machine-learning

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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.