A Data-Driven Approach for the Guided Regulation of Exposed Facets in Nanoparticles

Nanomaterials with high-index facets have desirable properties but are often challenging to synthesize. One way to realize such structures is by incorporating guest metal or metalloid atoms that can stabilize high-index facets by influencing surface energies. However, the effect of different guest atoms can vary significantly, and the vast parameter set (possible combinations of host nanoparticles and guest species) makes a trial-and-error experimental approach to explore every combination impractical. Herein, we report a data-driven approach incorporating high-throughput density functional theory calculations to assess surface energies of low- and high-index facets of nanoparticles (9 transition metals) with surfaces modified by 13 guest atoms. Machine learning techniques are then employed to understand the critical features leading to energetically favored high-index facet formation in the context of tetrahexahedron (THH). The predictions are validated by chemical synthesis, demonstrating the efficacy of this approach in accelerating the synthesis of THH materials with exposed {210} facets.