A data-driven approach for the guided regulation of exposed facets in nanoparticles

Zihao Ye; Bo Shen; Dohun Kang; Jiahong Shen; Jin Huang; Zhe Wang; Liliang Huang; Christopher M. Wolverton; Chad A. Mirkin

doi:10.1038/s44160-024-00561-1

A data-driven approach for the guided regulation of exposed facets in nanoparticles

Zihao Ye, Bo Shen, Dohun Kang, Jiahong Shen, Jin Huang, Zhe Wang, Liliang Huang, Christopher M. Wolverton^*, Chad A. Mirkin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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 substantially, 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. Here 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 used to understand the critical features leading to energetically favoured high-index facet formation in the context of tetrahexahedron. The predictions are validated by chemical synthesis, demonstrating the efficacy of this approach in accelerating the synthesis of tetrahexahedron materials with exposed {210} facets. (Figure presented.)

Original language	English (US)
Pages (from-to)	922-929
Number of pages	8
Journal	Nature Synthesis
Volume	3
Issue number	7
DOIs	https://doi.org/10.1038/s44160-024-00561-1
State	Published - Jul 2024

Funding

We thank T. Sengupta (Northwestern University) for professional editorial advice. Research was sponsored by the Army Research Office under grants W911NF-23-1-0141 and W911NF-23-1-0285, the Toyota Research Institute, Inc., and the Sherman Fairchild Foundation, Inc. D.K. acknowledges funding from the International Institute for Nanotechnology. Z.Y. and D.K. acknowledge partial support from the Predictive Science and Engineering Design (PSED) programme at Northwestern University. J.S. acknowledges support from the MRSEC programme (DMR-1720139) at the Materials Research Center of Northwestern University. This work made use of the EPIC and BioCryo facilities of Northwestern University\u2019s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the International Institute for Nanotechnology and Northwestern\u2019s MRSEC programme (NSF DMR-1720139). We acknowledge the computational resources provided by the Quest high-performance computing facility at Northwestern University.

ASJC Scopus subject areas

Chemistry (miscellaneous)
Inorganic Chemistry
Organic Chemistry
Materials Chemistry

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abstract = "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 substantially, 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. Here 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 used to understand the critical features leading to energetically favoured high-index facet formation in the context of tetrahexahedron. The predictions are validated by chemical synthesis, demonstrating the efficacy of this approach in accelerating the synthesis of tetrahexahedron materials with exposed {210} facets. (Figure presented.)",

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AU - Kang, Dohun

AU - Shen, Jiahong

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AU - Wang, Zhe

AU - Huang, Liliang

AU - Wolverton, Christopher M.

AU - Mirkin, Chad A.

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A data-driven approach for the guided regulation of exposed facets in nanoparticles

Abstract

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