MACHINE-LEARNING

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Machine-Learning-Assisted Compositional Design of Refractory High-Entropy Alloys with Optimal Strength and Ductility
《Engineering》2025年第3期214-223,共10页Cheng Wen Yan Zhang Changxin Wang Haiyou Huang Yuan Wu Turab Lookman Yanjing Su 
financial support of the National Key Research and Development Program of China(2021YFB3802100);the National Natural Science Foundation of China(52203293);the Innovation Centre of Nuclear Materials Fund(ICNM-2022-ZH-02).
Designing refractory high-entropy alloys(RHEAs)for high-temperature(HT)applications is an outstanding challenge given the vast possible composition space,which contains billions of candidates,and the need to optimize ...
关键词：Machine learning Refractory high-entropy alloys Multi-objective optimization Strength-ductility design 
Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices
《Journal of Energy Chemistry》2025年第2期298-323,I0007,共27页Bonghyun Jo Wenning Chen Hyun Suk Jung 
supported by the Ministry of Science and ICT(MSIT)of the Republic of Korea(00302646);supported by the National Research Foundation of Korea grant funded by the Korean Government(MSIT)(NRF-2022R1A4A1019296,1345374646,2022M3J1A1064315).
Perovskite solar cells(PSCs)have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy sources.However,conventional trial-and-error approaches and the vast compositional parame...
关键词：Perovskite solar cell Data-driven machine learning CHARACTERIZATION Perovskite materials 
Schistosomiasis transmission in Zimbabwe:Modelling based on machine learning
《Infectious Disease Modelling》2024年第4期1081-1094,共14页Hong-Mei Li Jin-Xin Zheng Nicholas Midzi Masceline Jenipher Mutsaka-Makuvaza Shan Lv Shang Xia Ying-jun Qian Ning Xiao Robert Berguist Xiao-Nong Zhou 
supported by the program of the Chinese Center for Tropical Diseases Research(No.131031104000160004);the China-Africa Cooperation Project on Schistosomiasis Control and Elimination(2020-C4-0001-2).
Zimbabwe,located in Southern Africa,faces a significant public health challenge due to schistosomiasis.We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population.To this e...
关键词：MACHINE-LEARNING Transmission risk model SCHISTOSOMIASIS Zimbabwe 
Machine-learning-aided Au-based single-atom alloy catalysts discovery for electrochemical NO reduction reaction to NH_(3)
《Rare Metals》2024年第11期5813-5822,共10页Hui-Long Jin Qian-Nan Li Yun-Yan Tian Shuo-Ao Wang Xing Chen Jie-Yu Liu Chang-Hong Wang 
financially supported by the HeBei Natural Science Foundation(Nos.B2022205029 and B2022205013)。
Direct electrochemical conversion of NO to NH_(3)has attracted widespread interest as a green and sustainable strategy for both ammonia synthesis and nitric oxide removal.However,designing efficient catalysts remains ...
关键词：NO reduction reaction Ammonia synthesis Single-atom alloy catalysts Machine learning 
Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease
《Chinese Chemical Letters》2024年第11期266-272,共7页Yao-Hua Gu Yu Chen Qing Li Neng-Bin Xie Xue Xing Jun Xiong Min Hu Tian-Zhou Li Ke-Yu Yuan Yu Liu Tang Tang Fan He Bi-Feng Yuan 
supported by the National Key R&D Program of China(Nos.2022YFC3400700,2022YFA0806600);the Key Research and Development Project of Hubei Province(No.2023BCB094);the Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University(No.JCRCGW-2022-008);the Key Laboratory of Hubei Province(No.2021KFY005)。
Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic dis...
关键词：Widely-targeted metabolomics MACHINE-LEARNING Chronic kidney disease BIOMARKER Mass spectrometry 
Li-ion transport mechanisms in Ge/Cl dual-doped Li_(10)GeP_(2)S_(12) solid electrolytes:Synergistic insights from experimental structural characterization and machine-learning-assisted atomistic modeling
《Carbon Energy》2024年第10期166-178,共13页Yong-Seok Choi Jiwon Jeong Youngin Lee Hyuna Ahn David O.Scanlon Kyung Yoon Chung Jae-Chul Lee 
European Research Council,Grant/Award Number:758345;National Research Foundation of Korea,Grant/Award Numbers:NRF-2021R1A2C2009596,RS-2023-00236572,NRF-2022M3J1A1054151;Engineering and Physical Sciences Research Council,Grant/Award Numbers:EP/R029431,EP/P020194,EP/T022213。
Enhancing the ionic conductivity of sulfide solid electrolytes(SEs)through dual-doping is a well-established approach,yet the atomic-level mechanisms driving these improvements remain elusive.By dual-doping Ge and Cl ...
关键词：cooperative hopping DFT calculations molecular dynamic simulations PADDLE-WHEEL 
Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods
《Defence Technology（防务技术）》2024年第10期105-124,共20页Qingqing Chen Xinyu Zhang Zhiyong Wang Jie Zhang Zhihua Wang 
supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006);the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...
关键词：Data-driven dimensional analysis PENETRATION Semi-infinite metal target Dimensionless numbers Feature selection 
Determinants of saturation magnetic flux density in Fe-based metallic glasses:insights from machine-learning models
《Rare Metals》2024年第10期5256-5267,共12页Jie Xiong Bo-Wen Bai Hao-Ran Jiang Angeles Faus-Golfe 
financially supported by Shanghai Pujiang Program(No.23PJ1403500);GuangDong Basic and Applied Basic Research Foundation(No.2023A1515110901);Shenzhen Pengcheng Peacock Project(No.NA11409004);the National Natural Science Foundation of China(Nos.U22B2064 and 51105102);and Shanghai Rising-Star Program Yangfan Project(No.23YF1411900)。
Fe-based metallic glasses have garnered significant attention due to their low coercivity force and core loss.Enhancing the saturation magnetic flux density(Bs)of Fe-based metallic glasses is crucial for their industr...
关键词：Saturation magnetic flux density Fe-based metallic glasses Machine learning Symbolic regression 
White-box machine-learning models for accurate interfacial tension prediction in hydrogen-brine mixtures
《Clean Energy》2024年第5期252-264,共13页Qichao Lv Jinglei Xue Xiaochen Li Farzaneh Rezaei Aydin Larestani Saeid Norouzi-Apourvari Hadi Abdollahi Abdolhossein Hemmati-Sarapardeh 
The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources.One intrinsic challenge with renewable energy sour...
关键词：underground hydrogen storage interfacial tension cushion gas correlation gene expression programming group method of data handling 
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 
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