First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reaction-Reference-Cited by-同舟云学术

First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reaction

Published:2018 Issue:38 Volume:20 Page:24539-24544
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Kang Joonhee¹²³⁴^ORCID,Noh Seung Hyo¹²³⁴,Hwang Jeemin¹²³⁴,Chun Hoje¹²³⁴,Kim Hansung¹²³⁴^ORCID,Han Byungchan¹²³⁴^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering

2. Yonsei University

3. Seoul 03722

4. Korea

Abstract

An elegant machine-learning-based algorithm was applied to study the thermo-electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR).

Funder

Korea Institute of Energy Technology Evaluation and Planning

Ministry of Science, ICT and Future Planning

Ministry of Trade, Industry and Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2018/CP/C8CP03801E

Reference40 articles.

1. Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles

2. Carbon-Supported Pt-Based Alloy Electrocatalysts for the Oxygen Reduction Reaction in Polymer Electrolyte Membrane Fuel Cells: Particle Size, Shape, and Composition Manipulation and Their Impact to Activity

3. First-Principles Study on the Thermal Stability of LiNiO2 Materials Coated by Amorphous Al2O3 with Atomic Layer Thickness

4. First-Principles Design of Graphene-Based Active Catalysts for Oxygen Reduction and Evolution Reactions in the Aprotic Li–O2 Battery

5. Oxygen-Deficient Zirconia (ZrO2−x): A New Material for Solar Light Absorption

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