Application of Machine Learning in the Design of Cathode Materials and Electrolytes for High-Performance Lithium Batteries

Therapid application ofbig dataand artificialintelligence" and thedeep intersection ofmachine learning # ML$ and chemistry disciplines have inspired more promising developmentapproaches for the integration ofML technology with battery materials" especially in thematerialdesign ofbattery" performance prediction" structureoptimization" and so on. Theapplication ofML can effectively acceleratetheselection processofbattery materialsand predicttheperformanceoflithium batteries# LBs$ " consequently driving the developmentofLBs.Thisreview briefly introducesthebasicideaofMLand severalimportantMLalgorithmsin thefield ofLBs" then theerrorperformanceand analysisofthetraditionalsimulation calculation method and ML method are discussed" thereby increasing understanding of ML methods by LBs experts. Secondly" the application ofML in thepracticaldevelopmentofbattery materials" including cathodematerials" electrolytes" multi-scalesimulation ofmaterialsand high-throughputexperiments#HTE$ " isemphatically introduced to draw outtheideasand meansofapplying ML methodsin thefield ofbatteries.Finally" therecentworksofML in lithium batteriesaresummarized and theirapplication prospectsareforeseen.Itishoped thatthisreview willshed lighton theapplication ofML in thedevelopmentofLBsand promotethedevelopmentofadvanced LBs.