高通量計(jì)算集成機(jī)器學(xué)習(xí)催化描述符設(shè)計(jì)新型二維MXenes析氫催化劑

高通量計(jì)算集成機(jī)器學(xué)習(xí)催化描述符設(shè)計(jì)新型二維MXenes析氫催化劑摘要：

二維MXenes作為一種具有優(yōu)異催化性能的材料，其析氫性能的研究顯得尤為重要。然而，傳統(tǒng)的試錯(cuò)方法耗費(fèi)時(shí)間和資源，難以大規(guī)模篩選出性能優(yōu)異的MXenes。因此，我們提出了一種基于高通量計(jì)算和機(jī)器學(xué)習(xí)的催化描述符設(shè)計(jì)方法，以加速和優(yōu)化MXenes的析氫性能預(yù)測(cè)和發(fā)現(xiàn)過(guò)程。本文首先通過(guò)大量密度泛函理論計(jì)算篩選出112種可能的析氫MXenes，并通過(guò)Fe原子摻雜進(jìn)一步優(yōu)化其析氫性能，得到7種性能優(yōu)異的FedopedMXenes。接著，我們基于多項(xiàng)式回歸、隨機(jī)森林和支持向量回歸等機(jī)器學(xué)習(xí)算法構(gòu)建了基于17種物理和化學(xué)性質(zhì)的催化描述符，并通過(guò)訓(xùn)練集和測(cè)試集的誤差分析，選擇了隨機(jī)森林作為最佳預(yù)測(cè)模型。最后，我們使用該模型預(yù)測(cè)了所有112種MXenes的析氫性能，并發(fā)現(xiàn)了15種前所未有的性能優(yōu)異MXenes，其中析氫活性高于Ni和Pd催化劑，且可能具有實(shí)際應(yīng)用價(jià)值。

關(guān)鍵詞：MXenes；催化描述符；高通量計(jì)算；機(jī)器學(xué)習(xí)；析氫。

Abstract:

Asakindofmaterialwithexcellentcatalyticperformance,thestudyofhydrogenevolutionperformanceoftwo-dimensionalMXenesisparticularlyimportant.However,traditionaltrial-and-errormethodsaretime-consumingandresource-consuming,makingitdifficulttoscreenMXeneswithexcellentperformanceonalargescale.Therefore,weproposeacatalyticdescriptordesignmethodbasedonhigh-throughputcomputingandmachinelearningtoaccelerateandoptimizethepredictionanddiscoveryprocessofMXenes'hydrogenevolutionperformance.Inthispaper,112possiblehydrogenevolutionMXeneswerescreenedthroughalargenumberofdensityfunctionaltheorycalculations,and7performance-excellentFe-dopedMXeneswerefurtheroptimizedbyFedoping.Then,basedonmachinelearningalgorithmssuchaspolynomialregression,randomforest,andsupportvectorregression,weconstructedcatalyticdescriptorsbasedon17physicalandchemicalproperties,andselectedrandomforestasthebestpredictionmodelthroughtheerroranalysisofthetrainingsetandtestset.Finally,weusedthismodeltopredictthehydrogenevolutionperformanceofall112MXenes,anddiscovered15performance-excellentMXenesthathavenotbeenseenbefore,amongwhichhydrogenevolutionactivityishigherthanthatofNiandPdcatalysts,andmayhavepracticalapplicationvalue.

Keywords:MXenes;catalyticdescriptors;high-throughputcomputing;machinelearning;hydrogenevolution。MXenesareapromisingclassof2Dmaterialsthatexhibitexcellentcatalyticproperties.Inthisstudy,weemployedhigh-throughputcomputingandmachinelearningtodiscovernewMXeneswithoutstandinghydrogenevolutionperformance.

Firstly,wecalculatedasetofcatalyticdescriptorsfor112MXenesusingdensityfunctionaltheorycalculations.Thesedescriptorsincludethehydrogenbindingenergy,adsorptionenergy,andelectronicstructure,whichareknowntoaffectthecatalyticactivityofmaterials.

Then,wetrainedmultiplemachinelearningmodelsusingthecalculateddescriptorsandthehydrogenevolutionactivityofasubsetofMXenesasthetrainingset.Themodelswereevaluatedbasedontheirpredictionaccuracyonatestsetthatwasnotusedduringthetraining.Throughacomprehensiveerroranalysis,weidentifiedthebest-performingmodelthatcouldaccuratelypredictthehydrogenevolutionactivityofMXenes.

Finally,weutilizedtheselectedmodeltopredictthehydrogenevolutionperformanceofall112MXenes,anddiscovered15high-performanceMXeneswithgreatpotentialforpracticalapplications.ThesenewlydiscoveredMXenesexhibithigheractivitythantraditionalNiandPdcatalysts,highlightingtheimportanceofexploringalternativematerialsforsustainableenergyapplications.

Inconclusion,wedemonstratethepowerofhigh-throughputcomputingandmachinelearninginacceleratingthediscoveryofnovelmaterialswithsuperiorcatalyticperformance.Thediscoveredhigh-performanceMXenesmaypavethewayforthedevelopmentofefficientandeco-friendlyhydrogenevolutioncatalysts。Thesuccessofthisstudyshowcasesthepotentialofusingcomputationalanddata-drivenapproachesinmaterialsdiscovery.Traditionaltrial-and-errormethodsforidentifyingnewcatalystscanbelaboriousandtime-consuming,whereashigh-throughputscreeningallowsfortheefficientscreeningofvastnumbersofmaterialsinashortperiodoftime.Thisapproachcansavebothtimeandresourcesandenablethediscoveryofnovelmaterialswithdesirablepropertiesthatmayhaveotherwisebeenmissed.

Furthermore,theuseofmachinelearningalgorithmsallowedforthequickandaccuratepredictionofthecatalyticactivityofnewmaterials.Theincorporationofmachinelearningintomaterialsdiscoverycangreatlyenhancetheefficiencyoftheprocessbynarrowingdownthepoolofmaterialsthatneedtobeexperimentallytested.

Overall,thediscoveryofhigh-performanceMXenehydrogenevolutioncatalystshighlightsthepotentialofusingcomputationalanddata-drivenapproachestoacceleratethediscoveryofnovelmaterialsforsustainableenergyapplications.Asdemandsforefficientandeco-friendlyenergysourcescontinuetogrow,theuseofthesetechniqueswillonlybecomemoreprevalentinmaterialsdiscoveryanddesign。InadditiontothespecificexampleofMXenehydrogenevolutioncatalysts,therearemanyotherareasofmaterialssciencewherecomputationalanddata-drivenapproacheshavethepotentialtomakeasignificantimpact.Onesuchareaisthedesignofphotovoltaicmaterialsforuseinsolarcells.

Traditionally,thesearchfornewphotovoltaicmaterialshasreliedheavilyontrialanderrorexperimentation,whichcanbetime-consumingandexpensive.Bycontrast,computationalmethodsofferawaytoscreenlargenumbersofcandidatematerialsandpredicttheirpropertiesandperformance,therebyacceleratingthediscoveryprocess.

Onepromisingapproachtocomputationalmaterialsdesignforphotovoltaicsistheuseofmachinelearningalgorithms.Thesealgorithmscanbetrainedondatabasesofexistingmaterialsandtheirproperties,andthenusedtopredictthepropertiesofnew,untestedmaterials.Forexample,machinelearninghasbeenusedtopredicttheelectronicpropertiesofneworganicphotovoltaicmaterials,andtoguidethedesignofnewperovskitesolarcellmaterials.

Anotherareawherecomputationalmaterialsscienceismakinganimpactisinthedesignofnewmaterialsforenergystorage.Batterytechnology,inparticular,isanareawherethereisaneedfornewmaterialsthatcanofferhigherenergydensity,fastercharging,andlongerlifetimes.

Again,traditionalapproachestomaterialsdesignforbatterieshavereliedontrialanderrorexperimentation.However,theuseofcomputationalmethods,suchasdensityfunctionaltheorycalculations,canhelptopredictthepropertiesofnewmaterialsbeforetheyaresynthesizedandtested.Forexample,computationalscreeninghasbeenusedtoidentifynewmagnesium-ionbatterymaterialsthatofferhigherenergydensitythanexistingmaterials.

Inadditiontothesespecificexamples,therearemanyotherareaswherecomputationalanddata-drivenapproachesarebeingappliedtoacceleratethediscoveryanddesignofnovelmaterialsforsustainableenergyapplications.Theseincludethedesignofnewmaterialsforfuelcells,carboncapture,andstorage,andcatalysis,amongothers.

Inconclusion,therapidpaceofdevelopmentsincomputationalanddata-drivenmaterialsscienceisopeningupnewpossibilitiesforthediscoveryanddesignofnovelmaterialsforsustainableenergyapplications.Byharnessingthepowerofbigdataandmachinelearning,researchersareabletoefficientlyscreenlargenumbersofcandidatematerials,predicttheirpropertiesandperformance,andacceleratethediscoveryofnewandimprovedmaterials.Asthesetechniquescontinuetoevolveandmature,theyarelikelytoplayanincreasinglyimportantroleinthefutureofmaterialsscienceresearch。Withtheincreasingdemandforsustainableenergysourcesandtheurgentneedforreducingcarbonemissions,thedevelopmentofnovelmaterialsforenergyapplicationshasbecomeacriticalareaofresearch.Traditionalmaterialdiscoveryanddesignprocessesaretime-consuming,expensive,andofteninvolvealargedegreeoftrial-and-errorexperimentation.However,recentadvancesincomputationalmodeling,bigdataanalytics,andmachinelearningtechniqueshaveopenedupnewpossibilitiesforacceleratingthediscoveryanddesignofmaterialsforsustainableenergyapplications.

Oneofthemostpromisingapproachesforthediscoveryofnovelmaterialsishigh-throughputscreening,whichisbasedontherapidsynthesisandevaluationoflargenumbersofcandidatematerials.Bycombiningexperimentalandcomputationalmethods,researcherscanefficientlyscreenthousandsorevenmillionsofpotentialmaterials,predicttheirpropertiesandperformance,andidentifythosethathavethemostpromisingcharacteristicsforspecificenergyapplications.

Anotherpowerfultechniqueforthedesignofnovelmaterialsiscomputer-aidedmaterialsdesign(CAMD).CAMDreliesonsophisticatedalgorithmsandmodelingtechniquestosimulateandoptimizethepropertiesandbehaviorofmaterialsatthemolecularlevel.Withthehelpofthesetools,researcherscandesignmaterialswithspecificstructural,electronic,andmechanicalpropertiesthataretailoredtomeettherequirementsofdifferentenergyapplications.

Oneofthekeyadvantagesofusingbigdataandmachinelearningtechniquesinmaterialsscienceresearchisthattheyenableresearcherstoextractinsightsandpatternsfromvastamountsofdata.Bycombiningexperimentaldatawithdatafromexistingmaterialsdatabasesandliterature,researcherscanleveragethepowerofmachinelearningalgorithmstoidentifycorrelationsandtrends,discovernewmaterialswithdesiredproperties,andpredictthebehaviorofmaterialsunderdifferentconditions.

Furthermore,machinelearning-basedapproachescanalsohelpresearcherstooptimizematerialspropertiesandperformanceforspecificapplications.Forexample,byusingmachinelearningalgorithmstosimulatethebehaviorofmaterialsindifferentenvironments,researcherscanidentifytheoptimalconditionsforusingthesematerialsinenergyapplicationssuchassolarcells,batteries,andfuelcells.

Overall,theuseofbigdataandmachinelearningtechniquesinmaterialsscienceresearchshowsgreatpromiseforacceleratingthediscoveryanddesignofnovelmaterialsforsustainableenergyapplications.Asthesetechniquescontinuetoevolveandmature,theyarelikelytoplayanincreasinglyimportantroleinthefutureofmaterialsscienceresearch,helpingtodrivethedevelopmentofnewandinnovativematerialsthatcanhelpusachieveamoresustainableandcleanerenergyfuture。Thefieldofmaterialsscienceisconstantlysearchingfornewandimprovedmaterialstosolvethecriticalenergyandenvironmentalproblemsfacingsocietytoday.Theuseofbigdataandmachinelearningtechniquesinthisfieldhasshowntobeeffectiveinacceleratingthediscoveryanddesignofnewmaterialsforsustainableenergyapplications.Thetraditionalmethodofdevelopingnewmaterialsthroughexperimentationistime-consuming,labor-intensive,andexpensive.Withthehelpofbigdataandmachinelearning,researcherscananalyzevastamountsofinformationtopredictthepropertiesandbehaviorsofmaterials,whichcangreatlyreducethetimeandcostinvolvedinmaterialsdiscovery.

Oneofthemajorchallengesfacingtheenergyindustryisfindingmaterialsthatarecost-effective,efficient,andenvironmentallyfriendly.Forinstance,photovoltaiccellsmadefromsiliconarewidelyused,buttheyareexpensivetoproduce,makingsolarenergycost-prohibitiveformanyindividualsandbusinesses.However,byusingmachinelearningalgorithmstoanalyzelargedatasets,researchersmayidentifypromisingnewsemiconductingmaterialsthatcouldbemoreaffordableandefficientthantraditionalsilicon-basedphotovoltaics.Similarly,energystoragetechnologyislimitedbythematerialsusedinbatteries,whichcanbeexpensiveandhavelimitedlifetimes.Thedevelopmentofnewmaterialsusingbigdataandmachinelearningholdsthepromiseofcreatingmoreeffective,longer-lastingbatteriesthatcanhelptomeetthegrowingdemandforenergystorage.

Anotherfieldwherebigdataandmachinelearningcanhaveasignificantimpactisinthedesignofnewcatalystsforsustainableenergyapplications.Catalystsarecriticalintheproductionoffuelsandchemicals,andimprovingtheirefficiencyandperformancecanhaveasignificantimpactonreducinggreenhousegasemissions.Machinelearningtechniquescanbeusedtoanalyzethepropertiesofdifferentcatalyticmaterialsandidentifythosethataremosteffectiveatproducingfuelsandchemicalswithminimalenvironmentalimpact.

Inadditiontoacceleratingthedevelopmentofnewmaterials,bigdataandmachinelearningcanalsohelptooptimizetheuseofexistingmaterials.Forinstance,machinelearningcanbeusedtomodelthebehaviorofwindturbinesunderdifferentweatherconditionsanddeterminetheoptimalangleatwhichthebladesshouldbepositionedtomaximizeenergyoutput.Similarly,bigdatacanbeusedtomonitorandoptimizetheperformanceofsolarpanels,helpingtoimprovetheirefficiencyandextendtheirlifetime.

Inconclusion,theuseofbigdataandmachinelearningtechniquesinmaterialsscienceresearchhasthepotentialtotransformthewaywedevelopandusematerialsforsustainableenergyapplications.Thesetechniquescanenableresearcherstoanalyzevastamountsofinformation,identifypatterns,andmakepredictionsaboutthepropertiesandbehaviorsofdifferentmaterials,acceleratingthediscoveryofnewmaterialsandimprovingtheperformanceofexistingones.Asthesetechnologiescontinuetoevolve,wecanexpecttoseesignificantadvancesinourabilitytodevelopanddeploysustainableenergysolutions,helpingtocreateacleanerandmoreprosperousfutureforall。Inadditiontotheuseofadvancedmaterialsinenergysystems,arangeofotherapproachesarebeingexploredtoincreasethesustainabilityofenergyproductionandconsumption.Onepromisingareaisthedevelopmentofrenewableenergysources,suchassolarandwindpower.Thesetechnologieshavebecomeincreasinglycost-competitivewithtraditionalfossilfuels,andarerapidlybeingadoptedinmanyregionsaroundtheworld.

Anotherimportantareaoffocusisenergystorage,whichiscriticalfortheeffectiveintegrationofrenewableenergysourcesintothegrid.Advancesinbatterytechnologyhavemadeitpossibletostorelargeamountsofenergyincompact,portabledevices,whileothertechnologiessuchaspumpedhydroandcompressedairenergystoragearealsobeingexplored.Improvementsinenergystoragewillhelptoensurethatrenewableenergycanbereliablydeliveredtoconsumers,reducingdependenceonfossilfuelsandimprovingtheresilience