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

Antonis N Andriotis; Giannis Mpourmpakis; Scott Broderick; Krishna Rajan; Somnath Datta; Mahendra Sunkara; Madhu Menon

doi:10.1063/1.4867010

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

J Chem Phys. 2014 Mar 7;140(9):094705. doi: 10.1063/1.4867010.

Authors

Antonis N Andriotis¹, Giannis Mpourmpakis², Scott Broderick³, Krishna Rajan³, Somnath Datta⁴, Mahendra Sunkara⁵, Madhu Menon⁶

Affiliations

¹ Institute of Electronic Structure and Laser, FORTH, P.O. Box 1527, 71110 Heraklio, Crete, Greece.
² Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15621, USA.
³ Materials Science and Engineering, Iowa State University, Ames, Iowa 50011, USA.
⁴ Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, USA.
⁵ Department of Chemical Engineering, University of Louisville, Louisville, Kentucky 40202, USA.
⁶ Department of Physics and Astronomy and Center for Computational Sciences, University of Kentucky, Lexington, Kentucky 40506, USA.

PMID: 24606374
DOI: 10.1063/1.4867010

Abstract

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Catalysis
Informatics*
Metal Nanoparticles / chemistry*
Quantum Theory*
Structure-Activity Relationship
Surface Properties