Exploring Challenging C2+ Products During CO2 Reduction via Machine Learning Acceleration (Adv. Energy Mater. 16/2025)
IF 24.4
1区 材料科学
Q1 CHEMISTRY, PHYSICAL
引用次数: 0
Abstract
CO2 Reduction
In article number 2500177, Mingzi Sun and Bolong Huang have applied the first-principles machine learning method to unravel the reaction mechanisms of challenging C2+ products during the CO2 reduction reaction on graphdiyne-supported atomic catalysts, which supply insights into improving the selectivity of designed catalysts.
通过机器学习加速探索二氧化碳减排过程中具有挑战性的C2+产品（能源材料，16/2025）
在第2500177篇文章中，孙明子和黄博龙应用第一性原理机器学习方法，揭示了石墨烯负载的原子催化剂在CO2还原反应过程中具有挑战性的C2+产物的反应机理，为提高所设计催化剂的选择性提供了见解。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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来源期刊
Advanced Energy Materials
CHEMISTRY, PHYSICAL-ENERGY & FUELS
CiteScore
41.90
自引率
4.00%
发文量
889
审稿时长
1.4 months
期刊介绍：
Established in 2011, Advanced Energy Materials is an international, interdisciplinary, English-language journal that focuses on materials used in energy harvesting, conversion, and storage. It is regarded as a top-quality journal alongside Advanced Materials, Advanced Functional Materials, and Small.
With a 2022 Impact Factor of 27.8, Advanced Energy Materials is considered a prime source for the best energy-related research. The journal covers a wide range of topics in energy-related research, including organic and inorganic photovoltaics, batteries and supercapacitors, fuel cells, hydrogen generation and storage, thermoelectrics, water splitting and photocatalysis, solar fuels and thermosolar power, magnetocalorics, and piezoelectronics.
The readership of Advanced Energy Materials includes materials scientists, chemists, physicists, and engineers in both academia and industry. The journal is indexed in various databases and collections, such as Advanced Technologies & Aerospace Database, FIZ Karlsruhe, INSPEC (IET), Science Citation Index Expanded, Technology Collection, and Web of Science, among others.
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