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Machine learning driven high-throughput screening of S and Ncoordinated SACs for eNRR
《Nano Research》2025年第4期633-644,共12页Lintao Xu Yuhong Huang Haiping Lin Xiumei Wei Fei Ma 
supported by National Natural Science Foundation of China(Nos.52271136 and 22373063);the Natural Science Foundation of Shaanxi Province in China(Nos.2021JC-06 and 2019TD-020);Fundamental Research Funds for the Central Universities of China(No.GK202203002).
This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively dist...
关键词：nitrogen reduction reaction(NRR)process machine learning catalytic descriptors SHapley Additive exPlanations(SHAP)analysis 
Atomically precise M-N-C electrocatalysts for oxygen reduction:Effects of inter-site distance,metal-metal interaction,coordination environment,and spin states
《Journal of Energy Chemistry》2025年第2期132-155,I0004,共25页Junfeng Huang Saira Ajmal Anuj Kumar Jianwen Guo Mohammed Mujahid Alam Abdullah G.Al-Sehemi Ghulam Yasin 
supported by the Research Fund for International Scientists(RFIS-Grant numbers:52150410410);National Natural Science Foundation of China;the Deanship of Scientific Research and Graduate Studies at King Khalid University for funding this research work through Large Research Project under the grant number RGP2/121/1445.
Inspired by molecular catalysts,researchers developed atomically precise nitrogen-coordinated single or dual metal sites imbedded in graphitized carbon(M-N-C)to fully utilize metallic sites for 02activation.These cata...
关键词：ELECTROCATALYSIS M-N-C electrocatalysts ORR Activity descriptors Spin states 
Predicting superconducting temperatures with new hierarchical neural network AI model
《Frontiers of physics》2025年第1期165-172,共8页Shaomeng Xu Pu Chen Mingyang Qin Kui Jin X-D.Xiang 
support from the National Key R&D Program of China(Grant No.2022YFB3807700);the Shenzhen Fundamental Research Funding(Nos.JCYJ20220818100612027 and JCYJ20220818100613028);the Major Science and Technology Infrastructure Project of Shenzhen Material Genome Big-Science Facilities Platform.
Superconducting critical temperature is the most attractive material property due to its impact on the applications of electricity transmission,railway transportation,strong magnetic fields for nuclear fusion and medi...
关键词：conventional superconducting critical temperature hierarchical neural network universal descriptors artificial intelligence 
The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries:A perspective
《Electron》2024年第4期30-47,共18页Zehua Wang Li Wang Hao Zhang Hong Xu Xiangming He 
Ministry of Science and Technology of the People's Republic of China,Grant/Award Number:2019YFA0705703;National Natural Science Foundation of China,Grant/Award Numbers:22279070,U21A20170。
Conventional approaches for developing new materials may no longer be adequate to meet the urgent needs of humanity's energy transition.The emergence of machine learning(ML)and artificial intelligence(AI)has led mater...
关键词：artificial intelligence data‐driven DESCRIPTORS lithium batteries machine‐learning 
Data-Driven Design of Single-Atom Electrocatalysts with Intrinsic Descriptors for Carbon Dioxide Reduction Reaction
《Transactions of Tianjin University》2024年第5期459-469,共11页Xiaoyun Lin Shiyu Zhen Xiaohui Wang Lyudmila V.Moskaleva Peng Zhang Zhi-Jian Zhao Jinlong Gong 
the National Key R&D Program of China(No.2022YFE0102000);the National Natural Science Foundation of China(Nos.22121004,U22A20409,22250008,and 22108197);the Haihe Laboratory of Sustainable Chemical Transformations,the Natural Science Foundation of Tianjin City(No.21JCZXJC00060);the Program of Introducing Talents of Discipline to Universities(No.BP0618007);the XPLORER PRIZE for financial support。
The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts(SACs)is crucial for catalyzing the CO_(2)reduction reaction(CO_(2)RR).However,it rem...
关键词：Density functional theory Machine learning CO_(2) reduction reaction ELECTROCATALYSTS High-throughput screening 
Single-atom catalysts supported on graphene/electride heterostructures for the enhanced sulfur reduction reaction in lithium-sulfur batteries
《Journal of Energy Chemistry》2024年第10期738-746,I0015,共10页Siyun Qi Chuanchuan Li Gang Chen Mingwen Zhao 
financially supported by the National Natural Science Foundation of China (No.22209196 and 12247167);Shandong Province through the Taishan Scholar Program;the Technological Innovation Project (MSTIP) (No.2019JZZY010209)。
Single-atom catalysts(SACs)hold great promise in addressing the sluggish kinetics of the sulfur reduction reaction(SRR)in lithium-sulfur(Li-S)batteries for their unique catalytic activity and maximum atom efficiency.W...
关键词：ELECTRIDES DFT calculations Kinetics Universal descriptors Charge transfers 
Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship
《Energy and AI》2024年第3期168-178,共11页Rodolfo S.M.Freitas Xi Jiang 
supported by the UK Physical Sciences Research Council under Grant No.EP/X019551/1.
The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develo...
关键词：Chemical descriptors Quantitative structure-property relationship Machine learning Cetane number Fuel design 
A Comprehensive Descriptor for Understanding High-Shell Heteroatom-Tuned Oxygen Reduction Reaction Activity on Diatomic FeCoN_(6)Sites被引量：1
《Renewables》2024年第4期242-249,共8页Xinyi Li Dongxu Jiao Xiao Zhao 
supported by the Jilin Province Science and Technology Development Project(Grant Numbers:YDZJ202401329ZYTS);the Fundamental Research Funds for the Central Universities.
Diatomic catalysts as a class of emerging non-noble oxygen reduction reaction(ORR)catalysts show superior activity over their single-atom counterparts.However,the strategies to further enhance their performance remain...
关键词：oxygen reduction reaction dual-atom catalysts density functional theory machine learning comprehensive descriptors 
Data-driven discovery of formation ability descriptors for high-entropy rare-earth monosilicates被引量：2
《Journal of Materiomics》2024年第3期738-747,共10页Hong Meng Peng Wei Zhongyu Tang Hulei Yu Yanhui Chu 
support from the National Key Research and Development Program of China(No.2022YFB3708600);the National Natural Science Foundation of China(No.52122204 and 51972116);Guangzhou Basic and Applied Basic Research Foundation(No.202201010632).
Herein we establish formation ability descriptors of high-entropy rare-earth monosilicates(HEREMs)via the data-driven discovery based on the high-throughput solid-state reaction and machine learning(ML)methods.Specifi...
关键词：High-entropy rare-earth monosilicates Formation ability descriptors High-throughput experiments Machine learning 
Deciphering orbital hybridization in heterogeneous catalysis
《Electron》2024年第1期68-94,共27页Xiaoyang Yue Lei Cheng Eszter Baráth Rajenahally V.Jagadeesh Quanjun Xiang 
Sichuan Science and Technology Program,Grant/Award Numbers:2022ZYD0039,2022NSFSC1213,2023NSFSC1069;National Natural Science Foundation of China,Grant/Award Number:22272019。
The catalytic coordinate is essentially the evolving frontier orbital interaction while feeding with catalytic materials and adsorbates under proper reaction conditions.The heterogeneous catalytic reaction mechanism i...
关键词：CATALYSIS catalyst design and prediction DESCRIPTORS INTERACTIONS orbital hybridization 
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