Machine learning guides the discovery of high-performance HEA catalysts

催化作用 计算机科学 化学 有机化学
作者
Jike Wang,Min Wei,Junyu Zhang
出处
期刊:IntechOpen eBooks [IntechOpen]
标识
DOI:10.5772/intechopen.1004118
摘要

High performance catalysts are crucial to generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of catalytic processes are key for improving the effectiveness and activities. HEAs typically have at least four principal elements, this atomic structure gives them unique properties that have applications and excellent performance in a variety of fields including catalysis. The complexity of HEAs makes challenge for computational researchers, providing promising opportunities for the application of machine learning. Recent advances in data science have great potential to accelerate catalyst research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive and critical review of machine learning techniques used in HEA catalysis research is provided. Sources of HEA catalyst data and current approaches to represent these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of catalyst models evaluated. Illustrations of how machine learning models are applied to novel HEA catalysts discovery and used to reveal catalytic reaction mechanisms are provided.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
量子星尘发布了新的文献求助10
1秒前
1秒前
3秒前
罗拉发布了新的文献求助10
3秒前
4秒前
4秒前
鱼e完成签到,获得积分10
6秒前
6秒前
7秒前
7秒前
song发布了新的文献求助10
9秒前
ding应助时尚初之采纳,获得10
9秒前
罗拉完成签到,获得积分10
9秒前
9秒前
10秒前
yun尘世完成签到,获得积分10
11秒前
11秒前
自信南霜完成签到,获得积分10
11秒前
tingting9完成签到,获得积分10
14秒前
14秒前
15秒前
卡布奇诺完成签到,获得积分10
15秒前
13223456发布了新的文献求助10
15秒前
青山落日秋月春风完成签到,获得积分10
17秒前
18秒前
19秒前
19秒前
小马甲应助动听的雅绿采纳,获得30
20秒前
1177发布了新的文献求助10
22秒前
22秒前
喜喵喵完成签到,获得积分10
23秒前
23秒前
23秒前
23秒前
11关注了科研通微信公众号
24秒前
123456完成签到,获得积分10
25秒前
时尚初之发布了新的文献求助10
25秒前
ddd完成签到,获得积分10
26秒前
喜喵喵发布了新的文献求助10
28秒前
无情的函发布了新的文献求助10
28秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989242
求助须知:如何正确求助?哪些是违规求助? 3531393
关于积分的说明 11253753
捐赠科研通 3270010
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136