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

Journal Article · Fri Mar 07 00:00:00 EST 2014 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.4867010· OSTI ID:22255002

Mpourmpakis, Giannis ^[1]; Broderick, Scott; Rajan, Krishna ^[2]; Datta, Somnath ^[3]; Sunkara, Mahendra ^[4]; Menon, Madhu ^[5]

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1. Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15621 (United States)
2. Materials Science and Engineering, Iowa State University, Ames, Iowa 50011 (United States)
3. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202 (United States)
4. Department of Chemical Engineering, University of Louisville, Louisville, Kentucky 40202 (United States)
5. Department of Physics and Astronomy and Center for Computational Sciences, University of Kentucky, Lexington, Kentucky 40506 (United States)

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.

• OSTI ID: 22255002
• Journal Information: Journal of Chemical Physics, Vol. 140, Issue 9; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9606
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC
ORGANIC
PHYSICAL AND ANALYTICAL CHEMISTRY
77 NANOSCIENCE AND NANOTECHNOLOGY
ADSORPTION
BINDING ENERGY
ELECTRONIC STRUCTURE
NANOSTRUCTURES
PARTICLES
SURFACES