Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles

被引：35

作者：

Andriotis, Antonis N. ^{[1
]}

Mpourmpakis, Giannis ^{[2
]}

Broderick, Scott ^{[3
]}

Rajan, Krishna ^{[3
]}

Datta, Somnath ^{[4
]}

Sunkara, Mahendra ^{[5
]}

Menon, Madhu ^{[6
,7
]}

机构：

[1] FORTH, Inst Elect Struct & Laser, Iraklion 71110, Crete, Greece

[2] Univ Pittsburgh, Dept Chem Engn, Pittsburgh, PA 15621 USA

[3] Iowa State Univ, Ames, IA 50011 USA

[4] Univ Louisville, Dept Bioinformat & Biostat, Louisville, KY 40202 USA

[5] Univ Louisville, Dept Chem Engn, Louisville, KY 40202 USA

[6] Univ Kentucky, Dept Phys & Astron, Lexington, KY 40506 USA

[7] Univ Kentucky, Ctr Computat Sci, Lexington, KY 40506 USA

来源：

JOURNAL OF CHEMICAL PHYSICS | 2014年 / 140卷 / 09期

基金：

美国国家科学基金会;

关键词：

ELECTRONIC-STRUCTURE; ADSORPTION BEHAVIOR; METAL; REACTIVITY; DESIGN; IDENTIFICATION; DECOMPOSITION; TRANSITION; STRAIN;

D O I：

10.1063/1.4867010

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

A data driven discovery strategy based on statistical learning principles is used to discover new correlations between electronic structure and catalytic activity of metal surfaces. From the quantitative formulations derived from this informatics based model, a high throughput computational framework for predicting binding energy as a function of surface chemistry and adsorption configuration that bypasses the need for repeated electronic structure calculations has been developed. (C) 2014 AIP Publishing LLC.

引用

页数：8

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