Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution

被引：0

作者：

Zou, Quan ^{[1
]}

Kuzume, Akiyoshi ^{[2
]}

Yoshida, Masataka ^{[1
,2
]}

Imaoka, Takane ^{[1
,2
]}

Yamamoto, Kimihisa ^{[1
,2
]}

机构：

[1] Tokyo Inst Technol, Inst Innovat Res IIR, Lab Chem & Life Sci CLS, Yokohama, Kanagawa 2268503, Japan

[2] Tokyo Inst Technol, ERATO JST Yamamoto Atom Hybrid Project, Yokohama, Kanagawa 2268503, Japan

来源：

CHEMISTRY LETTERS | 2023年 / 52卷 / 10期

关键词：

Subnanoparticles; Hydrogen evolution; Machine learning; CATALYST;

D O I：

10.1246/cl.230310

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Metal and alloy subnanoparticles (SNPs) have been anticipated to be a class of promising catalysts because of their fundamental difference from nanoparticles (NPs). In general, the interaction among the surface and bulk atoms of SNPs is significant due to the higher degree of alloying in SNPs than that in NPs counterparts. This study compared the SNPs and NPs concerning their electrocatalytic activities of hydrogen evolution reaction (HER) to understand the essential difference between alloy SNPs and NPs by using machine learning.

引用

页码：828 / 831

页数：4

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