Weuseliquid-phasecrystalgrowthmethod.Thefigureshowsvariousinorganiccrystalsgrowninourlab.Thecrystalsexhibitsurface-developed,high-qualitynatures.Weuseexperimentalgraphsandimagesformachinelearning(upperfigures).Weconvertthesedatatonumericalparametersastrainingdata.Lowerfigureshowspredictionresultsofexperimentsusingmachinelearning.Tetsuya YamadaMaterialsChemistryIn the FutureAfter GraduationDatasciencecananalyzebigdataandpredictthefuture,sothathasgreatpotentialtochangesocialstructure.Ifwecanapplydatasciencetochemicalexperiments,wecanexpecttobreakawayfromexistingresearchmethodsthatrelyonexperienceandintuitionanddevelophigh-performancematerialsinashorttime.Inourlaboratory,wearedevelopinghigh-performancecrystalsofenergy&environmentalmaterialsbyusingdatascience.Westudyexperimentaldatacollection,dataconversion,andmachinelearninganalysisforconstructionoffutureprediction/suggestionsystemfortheexperiments.Forasustainablesociety,itisessentialtodevelophigh-performanceenvironmental&energymaterials.Oneofissuestobesolvedinmaterialsciencewouldbetimecostfordevelopmentofnewmaterials.Byconstructionofprocessinformaticssystemwithhighaccuracy,wewillhavenewtooltosupplyhigh-performancematerials,ondemandforeveryone.Studentscanlearnchemicalsyntheses,characterizations,andalsoapplicationofdatasciencetothematerialchemistries.DatascienceisnowprogressinginJapan.Bygettingtheaboveskills,theycanplayanactivepartinthescientificfieldforbigdatainnearfuture.Assistant Professor・Ph.D. (Science), Graduate school of cience, Hokkaido University (2011).・Present career (2020-)・Current research topics : inorganic material chemistry, crystal growth, and data science.Process Informatics for Energy & Environmental Crystalline Materials
元のページ ../index.html#19