Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern
Dongxiao Chen1, Philippe Sautet1,2,3
1Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California, 90095, USA.
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Understanding the atomistic structure and fluxionality of Pt nanoparticles under reactive conditions is essential for rational design of effective catalysts, yet their structural complexity presents a great challenge. In this work, we combine grand canonical global optimization methods and machine learning potentials to explore the atomistic landscape of nanometer-sized (1∼2 nm) Pt nanoparticles under a pressure of hydrogen, resulting in a comprehensive library of PtxHy nanoparticles with over one million low-energy metastable structures. We found that hydrogen adsorption drives a size-dependent transformation from an amorphous to a crystalline structure, leading to sharp phase transitions for smaller nanoparticles and smooth transformations for larger ones. This behavior is governed by a competition between distinct core configurations, as well as the formation of rigid and fluxional local domains, where stability is dictated by specific surface motifs at low H coverage and by the crystalline core at high H coverage. By applying this structural library for reactivity modeling of methane dehydrogenation and ethylene hydrogenation, we show a marked discrepancy between the abundance of a surface site and its catalytic contribution, indicating that the active sites are rare, structurally distinct motifs, not the most common sites.
