Single-site metal-doped amorphous materials are widely used in heterogeneous catalytic processes, particularly in industrial applications. Unfortunately, the rational design of these materials is hindered by the irregularity of the support, which prevents a detailed experimental characterization of the active sites. Thus, theoretical simulations are fundamental for such analysis and the development of better catalysts. However, thousands or millions of structurally different active sites should in principle be sampled to estimate effective rate constants that are directly comparable to experiment, making a brute-force approach impractical. To address this issue, we have developed an algorithm that automatically generates metal-doped amorphous silica clusters, which will be used in electronic structure calculations. The clusters are carved from slabs of amorphous silica, also automatically created from molecular dynamics simulations, and then doped. This cluster generation algorithm is combined with machine learning to develop training sets on-the-fly to find active sites with low activation energies. This combination of algorithms may be tested on Nb(IV) doped amorphous silica, for which the mechanism of the ethylene epoxidation reaction is known.

Automated generation of metal-doped amorphous silica clusters

2019 Fall National Meeting

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