High-throughput screening of high energy density LiMn_(1-x)Fe_(x)PO_(4)via active learning  

作　　者：Qingyun Hu Wei Wang Junyuan Lu He Zhu Qi Liu Yang Ren Hong Wang Jian Hui 

机构地区：[1]School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [2]Zhang Jiang Institute for Advanced Study,Shanghai Jiao Tong University,Shanghai 201210,China [3]Department of Physics,City University of Hong Kong,Hong Kong 999077,China [4]Herbert Gleiter Institute of Nanoscience,School of Materials Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China [5]Shenzhen Research Institute,City University of Hong Kong,Shenzhen 518057,China

出　　处：《Chinese Chemical Letters》2025年第2期476-480,共5页中国化学快报(英文版)

基　　金：supported by the National Key Research and Development Program of China(No.2021YFB3702102);support from the“Initiation Program for New Teachers”(No.AF0500207);Shanghai Jiao Tong University;support from the Changsha Science and Technology Plan International and Regional Cooperation Project(No.kh2304002)。

摘　　要：Lithium-ion batteries(LiBs)with high energy density have gained significant popularity in smart grids and portable electronics.LiMn_(1-x)Fe_(x)PO_(4)(LMFP)is considered a leading candidate for the cathode,with the potential to combine the low cost of Li Fe PO_(4)(LFP)with the high theoretical energy density of LiMnPO_(4)(LMP).However,quantitative investigation of the intricate coupling between the Fe/Mn ratio and the resulting energy density is challenging due to the parametric complexity.It is crucial to develop a universal approach for the rapid construction of multi-parameter mapping.In this work,we propose an active learning-guided high-throughput workflow for quantitatively predicting the Fe/Mn ratio and the energy density mapping of LMFP.An optimal composition(LiMn_(0.66)Fe_(0.34)PO_(4))was effectively screened from 81 cathode materials via only 5 samples.Model-guided electrochemical analysis revealed a nonlinear relationship between the Fe/Mn ratio and electrochemical properties,including ion mobility and impedance,elucidating the quantitative chemical composition-energy density map of LMFP.The results demonstrated the efficacy of the method in high-throughput screening of LiBs cathode materials.

关 键 词：High-throughput screening Machine learning Cathode material Performance optimization Quantitative map 

分 类 号：TM912[电气工程—电力电子与电力传动] TQ131.11[化学工程—无机化工]