How machine learning can accelerate electrocatalysis discovery and optimization
Author:
Affiliation:
1. Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Chimie UMR 5182, Lyon, France
2. Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, 138634, Singapore
Abstract
Funder
Université de Lyon
Agency for Science, Technology and Research
Publisher
Royal Society of Chemistry (RSC)
Subject
Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science
Link
http://pubs.rsc.org/en/content/articlepdf/2023/MH/D2MH01279K
Reference112 articles.
1. Applied Machine Learning for Developing Next‐Generation Functional Materials
2. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
3. Machine Learning in Screening High Performance Electrocatalysts for CO 2 Reduction
4. Theory-guided materials design: two-dimensional MXenes in electro- and photocatalysis
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