审查
生化工程
催化作用
机器学习
钥匙(锁)
计算机科学
纳米技术
人工智能
管理科学
数据科学
工程类
化学
材料科学
生物化学
政治学
计算机安全
法学
作者
M. Karthikeyan,Durga Madhab Mahapatra,Abdul Razak,Abdulaziz A.M. Abahussain,Baranitharan Ethiraj,Lakhveer Singh
标识
DOI:10.1080/01614940.2022.2103980
摘要
There have been significant advancements in catalysis today, especially with the aid of artificial intelligence (AI). Understanding the relationship between the descriptors and catalytic performance and underlying principles is crucial for hydrogen evolution reaction (HER) catalysis. As of today, the usage and application of machine learning (ML) are soaring and there has been a phenomenal demand owing to its immense pattern recognition capability and optimization potential. Its applications in computational quantum mechanical modeling have inspired promising development in HER catalyst scrutiny and screening. The present communication reviews the general scheme of ML, data sources, and computational tools for HER catalyst screening and therefore provides a strong basis for its application. Furthermore, the analysis elucidates how ML algorithms are used for the prediction of a) adsorption energies, b) reaction descriptors, c) structure–property relationships, d) catalyst discovery, and e) synthesis conditions and reaction design of HER catalysts. Lastly, key constraints and the need for an integrative platform have been highlighted. Based on literature surveys, recent trends, material advancements, systems understanding, and key challenges in deploying ML tools, future directions with a road map have been provided for an evolved design and development in the case of HER catalysis.
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