(661e) Modeling Supported Sub-Nanometer Cluster Catalysts Via Multiscale Computations and Machine Learning

Many industrial reactions are catalyzed by supported sub-nanometer metal clusters.¹ However, unlike ordered catalysts, like zeolites, supported clusters cannot be precisely characterized by spectroscopic methods, which provide spatiotemporally averaged information. Furthermore, supported clusters have large configurational spaces, which cannot be reliably sampled using expensive ab initio computational methods. Consequently, their structure and structure-property relationships remain poorly understood. Prior computational work using on-lattice models has been applied to discover stable supported metal clusters.² Here we extend the procedure to include off-lattice configurations. We develop actively trained machine learning potentials to discover stable clusters on oxide supports with global optimization methods. We show that off-lattice models can discover configurations which are not accessible to lattice models. Our work also suggests that support reconstructions and the diversity of supported clusters can be detected spectroscopically. Our framework can be easily generalized to any metal/support combination.

References

1. Liu, L.; Corma, A., Metal Catalysts for Heterogeneous Catalysis: From Single Atoms to Nanoclusters and Nanoparticles. Chemical Reviews 2018, 118 (10), 4981-5079.
2. Wang, Y.; Su, Y.-Q.; Hensen, E. J. M.; Vlachos, D. G., Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. ACS Nano 2020, 14 (10), 13995-14007.