高通量計算集成機器學習催化描述符設計新型二維MXenes析氫催化劑摘要：

二維MXenes作為一種具有優異催化性能的材料，其析氫性能的研究顯得尤為重要。然而，傳統的試錯方法耗費時間和資源，難以大規模篩選出性能優異的MXenes。因此，我們提出了一種基于高通量計算和機器學習的催化描述符設計方法，以加速和優化MXenes的析氫性能預測和發現過程。本文首先通過大量密度泛函理論計算篩選出112種可能的析氫MXenes，并通過Fe原子摻雜進一步優化其析氫性能，得到7種性能優異的FedopedMXenes。接著，我們基于多項式回歸、隨機森林和支持向量回歸等機器學習算法構建了基于17種物理和化學性質的催化描述符，并通過訓練集和測試集的誤差分析，選擇了隨機森林作為最佳預測模型。最后，我們使用該模型預測了所有112種MXenes的析氫性能，并發現了15種前所未有的性能優異MXenes，其中析氫活性高于Ni和Pd催化劑，且可能具有實際應用價值。

關鍵詞：MXenes；催化描述符；高通量計算；機器學習；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