手机知网 App
24小时专家级知识服务
打 开
有机化工
手机知网首页
文献检索
期刊
工具书
图书
我的知网
充值中心
Interpretable Machine Learning-Assisted High-Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C-N Coordination
College of Physics Science and Technology, Yangzhou University;
State Key Laboratory of Organic Electronics and Information Displays (KLOEID) & Institute of Advanced Materials (IAM), School of Science, Nanjing University of Posts and Telecommunications;
College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology;
School of Physics & Key Laboratory of Quantum Materials and Devices, Ministry of Education, Southeast University
|
Jinxin Sun
Anjie Chen
Junming Guan
Ying Han
Yongjun Liu
Xianghong Niu
Maoshuai He
Li Shi
Jinlan Wang
Xiuyun Zhang
开通知网号
Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design...
机 构:
College of Physics Science and Technology, Yangzhou University;
State Key Laboratory of Organic Electronics and Information Displays (KLOEID) & Institute of Advanced Materials (IAM), School of Science, Nanjing University of Posts and Telecommunications;
College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology;
School of Physics & Key Laboratory of Quantum Materials and Devices, Ministry of Education, Southeast University;
领 域:
有机化工;
自动化技术;
关键词:
electrochemical nitrogen reduction;
feature engineering;
high-throughput screening;
machine learning;
0
19
开通会员更优惠,尊享更多权益
下载PDF版
手机阅读本文
下载APP 手机查看本文
Energy & Environmental Materials
2024年05期
立即查看 >
相似文献
期刊
硕士
博士
会议
报纸
加载中
更多
暂无数据
图书推荐
更多
相关工具书
更多
搜 索