原创力文档人工智能论文20220806 2022PhD Northwestern Accelerating Materials Study and Discovery through Machine Learning.pdfVIP
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NORTHWESTERNUNIVERSITY
AcceleratingMaterialsStudyandDiscoverythroughMachineLearning
ADISSERTATION
SUBMITTEDTOTHEGRADUATESCHOOL
INPARTIALFULFILLMENTOFTHEREQUIREMENTS
forthedegree
DOCTOROFPHILOSOPHY
FieldofMaterialsScienceandEngineering
By
CheolWooPark
EVANSTON,ILLINOIS
March2022
2
Abstract
Despitetheunprecedentedpaceatwhichtechnologyisadvancingnowadays,itswider
useandapplicationareoftenlimitedbytheavailabilityoflow-cost,high-performancematerials.
Thedevelopmentofbettermaterialsisoftencostlyandtime-consumingduetothesheersizeof
thesearchspaceofanewmaterial.Recently,data-drivenmaterialsdesignthroughmachine
learningtechniqueshavegainedmuchinterestforitspotentialtoacceleratethematerials
discoveryandcharacterizationbyordersofmagnitudecomparedtoconventionalcomputational
methodssuchasdensityfunctionaltheory(DFT).Thisthesisisfocusedonthedevelopmentof
machinelearningalgorithmsfortwoobjectives.Thefirstobjectiveistoacceleratethediscovery
ofnewstablematerialsthroughhigh-throughputstudiesbyaccuratelyscreeningforpotential
stablecompounds.Towardsthisend,wedevelopedtheiCGCNNandvi-iCGCNNwhichutilize
crystalgraphrepresentationsofinorganicmaterialsthatcanbeusedasinputforneuralnetworks.
Weshowausecaseofthevi-iCGCNNinpredictingalternativesuperhardmaterials.Thesecond
objectiveistoacceleratematerialcharacterizationthroughmoleculardynamicsimulations.To
achievethis,we
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