Combining first principles modeling, experimental inputs, and machine learning for nanocatalysts design

Nanocatalysts are of technological and scientific relevance for a large variety of catalytic processes. Due to the diverse geometries and complex structure-activity relationships, computational modeling and machine learning techniques are helpful in order to sample configuration space, incorporate experimental information, and account for co-variations in stability and catalytic activity. We will discuss structural determination of Au and IrO2 nanocatalysts from single and multi-objective global optimization algorithms, using as inputs density functional theory (DFT) calculations [1], a combination of energetic and simulated pair distribution function (PDF) data, and a combination of energetic and activity objectives. DFT data from thousands of Au nanostructures are fitted using a genetic algorithm to a hybrid bond-order potential (HyBOP)[2], which is able to predict structural and energetic properties of Au nanoclusters to bulk. Similarly, genetic algorithm is used to parametrize a variable charge potential for IrO2[3], which is instrumental in the combined multi-objective optimization of stability and activity. [1] A. Kinaci, et al, Sci. Rep. 6, 34974 (2016). [2] B. Narayanan, et al, J. Phys. Chem. C 120, 13787 (2016). [3] F. G. Sen, et al, J. Mater. Chem. A 3, 18970 (2015).