CDS&E: Machine learning enabled modelling of dynamic nanoparticle catalysts

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Catalysis enables chemical and fuels manufacturing by promoting efficient energy and resource utilization. As widely practiced in industry, catalysis is an inherently complex process, which has led to heavy reliance on trial-and-error methods for catalyst discovery and optimization. Advances in computational and data science methods are seeing increasing application in catalyst discovery and design. The project extends those methods to a range of catalyst particle sizes that are common in both industrial and environmental applications, yet challenging to simulate because of 1) the large number of atoms which must be modeled, 2) the “massive” combinatorial space of their structural configurations, and 3) dynamical changes in structure that occur in response to changes in reaction conditions. In contrast to previous modeling approaches that treat small particles as static, perfect polyhedrons, the project develops methods that consider the effects of dynamic rearrangements and metastable structures on dehydrogenation and hydrogenolysis reactions as catalyzed by platinum and nickel nanoparticles containing 20 to 200 atoms. To that end, the project employs several novel data science methods to model the vast computational space needed to predict relationships between dynamic catalyst structures and chemical reactivity. Beyond the technical aspect, the project offers educational and outreach activities focused on underrepresented students at the undergraduate and high-school levels.The project is built on the hypothesis that a transition metal nanoparticle, for example based on Pt or Ni, presents several low energy metastable isomers, representing accessible “defect-type” structures, that might fully dominate the catalytic activity of the particle. The main difficulty for atomistic modelling of metallic nanoparticles is the massive combinatorial space of their structural configurations. Those challenges will be addressed by exploiting data science methods leading to generalized models linking the structure and reactivity of nanoparticles in dynamical situations. Data science methods will be used at two places in the project, 1) interatomic potential fitting, and 2) identification of structural motifs on nanoparticle surfaces. The first step involves generation of an accurate inter-atomic potential using Neural Networks based on the investigator’s previous experience modeling small clusters. The various configurations of the surface atoms on the nanoparticle will be explored using basin-hoping algorithms, in a grand canonical approach to handle a variable number of adsorbates. This will allow us to efficiently explore the extremely diverse structures for Pt and Ni particles of ~20-200 atoms, with realistic adsorbate coverage. The obtained large structure database will be used to extract local structure descriptors and learn the statistical distribution of local structural motifs, using pattern recognition algorithms. This distribution of local motifs is key to analyze the catalytic activity. The educational and outreach aspects involve partnering with the UCLA Center for Excellence in Engineering and Diversity (CEED) to involve both high-school and undergraduate URM students in the research. A second initiative features a one-day workshop for high-school teachers on the UCLA campus, targeting Latino schools in central Los Angeles. The program will illustrate how computational chemistry can provide key insights on how catalysts work at the atomic scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.