Data driven computational design of stable oxygen evolution catalysts by DFT and machine learning: Promising electrocatalysts-Reference-Cited by-同舟云学术

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Data driven computational design of stable oxygen evolution catalysts by DFT and machine learning: Promising electrocatalysts

Published:2024-04 Issue: Volume:91 Page:645-655
ISSN:2095-4956
Container-title:Journal of Energy Chemistry
language:en
Short-container-title:Journal of Energy Chemistry

Author:

Park Hwanyeol^ORCID,Kim Yunseok^ORCID,Choi Seulwon^ORCID,Kim Ho Jun

Publisher

Elsevier BV

Reference79 articles.

1. Nanostructured materials for advanced energy conversion and storage devices

2. Metal-Free Catalysts for Oxygen Reduction Reaction

3. Atomic site electrocatalysts for water splitting, oxygen reduction and selective oxidation

4. Oxide-supported Ir nanodendrites with high activity and durability for the oxygen evolution reaction in acid PEM water electrolyzers

5. Oxide‐Supported IrNiO x Core–Shell Particles as Efficient, Cost‐Effective, and Stable Catalysts for Electrochemical Water Splitting

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1. Recent advances of carbon fiber-based self-supported electrocatalysts in oxygen electrocatalysis;Journal of Energy Chemistry;2024-11

2. Recent advances in constructing efficient electrocatalysts for ethanol oxidation reaction;Journal of Alloys and Compounds;2024-10

3. Directly predicting N2 electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features;Journal of Energy Chemistry;2024-10

4. Recent Advances in Revealing the Electrocatalytic Mechanism for Hydrogen Energy Conversion System;Small;2024-07-29

5. From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design;ACS Catalysis;2024-07-24

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