2019 Bodossaki Award Lecture in the field of CHEMISTRY
Nanomaterials such as metal nanoparticles, find tremendous applications in numerous and diverse areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of nanoparticle properties are yet to be elucidated and any discoveries have relied on trial and error experimentation in the lab. Focusing on catalysis, it is currently unknown how nanoparticle stability and surface adsorption (property central to catalysis) changes as a function of nanoparticle size, shape and metal composition. In this work, we blend first-principles calculations with computational modeling and machine learning to develop models able to capture thermodynamic stability, mixing properties and adsorption behavior of bimetallic nanoparticles of any morphology (size/shape) and metal composition. Our models are universal, rapid and able to capture a large number of computational and experimental data. Importantly, they take into consideration atomic level chemical bonding information on metal nanoparticles, revealing how different surface sites contribute to the overall property response (e.g. catalytic) under realistic experimental conditions. Overall, we demonstrate a methodology to accelerate the discovery of nanomaterials by rapidly screening bimetallic nanoparticles with targeted application performance.
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