G.2060Page1GlobalResearchjournalofNaturalScience&Technology(GRJNST)Volume:04-Issue2(2026),2060ISSNP:2790-7643ISSNE:2790-7651www.grjnst.nethttps://doi.org/10.53762/grjnst.04.02.11ComputationalDesignofFunctionalMaterialsUsingDensityFunctionalTheoryforEnergyApplicationsReceived:29December2025.Accepted:20February2026.Published:12April2026IrtazaBashirRajaNationalUniversityofSciencesandTechnology,IslamabadIrtazaraja@gmail.comHamidIqbalAssociateProfessor,DepartmentofPhysicsGovtPostGraduateJahanzebCollegeSwat,Pakistanhamidiqbalkhaan@jc.edu.pkMuhammadMuneebKhanPh.D.Scholar,DepartmentofSINES(SchoolofInterdisciplinaryEngineeringandSciences),NUSTmkhan.phdcsd24sines@student.nust.edu.pkSherazAhmadM.PhilPhysicsStudent,DepartmentofPhysicsHazaraUniversity,Mansehrasheraz.98hu@gmail.comGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page2Abstract:ThisstudyinvestigatedthecomputationaldesignoffunctionalmaterialsforenergyapplicationsusingDensityFunctionalTheory(DFT).Theprimaryobjectivewastoanalyzethestructural,electronic,andcatalyticpropertiesofselectedmaterialstoevaluatetheirsuitabilityforenergystorageandconversionsystems.Aquantitativecomputationalmethodologywasadopted,wherematerialssuchasgraphene,molybdenumdisulfide(MoS₂),titaniumdioxide(TiO₂),andperovskiteswereanalyzedusingDFT-basedsimulations.Keyparametersincludingbandgapenergy,totalenergy,densityofstates,andadsorptionenergywerecalculated.Theresultsrevealedthatgrapheneexhibitedabandgapof0.00eV,indicatinghighelectricalconductivity,whileMoS₂andperovskitesshowedmoderatebandgapsof1.80eVand1.50eV,respectively,makingthemsuitableforphotovoltaicapplications.TiO₂demonstratedahigherbandgapof3.20eV,suggestingitssuitabilityforphotocatalyticprocesses.AdsorptionenergyanalysisshowedthatMoS₂(−0.85eV)andperovskites(−0.65eV)hadoptimalinteractionstrengthsforcatalyticefficiency,whereasgrapheneexhibitedweakadsorption(−0.20eV).ThefindingshighlightedthatDFT-basedapproachessignificantlyenhancedtheefficiencyofmaterialdesignbyreducingexperimentaleffortsandenablingaccuratepredictionofproperties.Thestudyprovidedpracticalimplicationsfordevelopingadvancedenergymaterialsandemphasizedtheimportanceofcomputationaltechniquesinachievingsustainableenergysolutions.Keywords:AdsorptionEnergy,DensityFunctionalTheory,EnergyMaterials,Graphene,Perovskite,SemiconductorIntroductionTheincreasingworldneedstofindsustainableandefficientenergyapplicationshasgreatlymotivatedthestudyofnovelfunctionalmaterials.Traditionalexperimentalmethodsofmaterialdiscoverytendedtobeexpensive,time-GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page3consumingaswellaslimitedthestudyofcomplexmaterialsystems.Inthisregard,computationaltechniques,especiallyDensityFunctionalTheory(DFT)hadbecomeeffectivepredictiveanddesigntoolsofmaterialsdowntotheatomiclevel.DFThadallowedscientiststoexploreelectronicstructure,totalenergy,andmaterialstability,thus,pavingthewaytotherapidcreationofmaterialsthatareenergy-efficient(Liuetal.,2024;Zhangetal.,2019).DFTwasnowastandardmethodincomputationalmaterialssciencebecauseofitstradeoffbetweencomputationalefficiencyandpredictiveaccuracy.Itsusagehadbeenextensiveinthestudyofsemiconductors,nanomaterials,andenergystoragesystems,inwhichelectronicpropertieslikebandgapanddensityofstateswereimportant.Thesepropertieshadenabledscientiststofabricatematerialswithcustomizedcharacteristicstouseinsolarcells,batteries,andcatalyticsystems(Sharmaetal.,2019;Wangetal.,2023).TherecentdevelopmentofcomputationalmethodshasalsoimprovedtheperformanceofDFT.Machinelearningcoupledwithfirst-principlescalculationswasalreadyabletoscreenthousandsofmaterialsintheshortestdurationpossible,decreasingthetimetodiscoverconsiderably.Suchahybridstylehasalreadybeenshowntobeespeciallyusefulinthediscoveryofpromisingmaterialslikemetalorganicframeworkstostoreelectrochemicalenergy(Sunetal.,2025;Hendyetal.,2025).Despitetheseimprovements,issueswithhighaccuracyofcomplexsystemshadpersisted,especiallythosewithstrongelectroncorrelationsandexchange-correlationapproximations.ContinueddevelopmentofhybridfunctionalsandcomputationalmodelscontinuedtoincreasetheaccuracyofDFTpredictions.DFThadbeenatthecenterofthedevelopmentofcomputationaldesignoffunctionalmaterialstomodernenergyuses(Mohanetal.,2024;Poater,2022).GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page4BackgroundofStudyDensityFunctionalTheorywasdevelopedasaquantummechanicaltheorythatthebehaviorofmany-electronsystemscouldbedescribedinsteadofusingwavefunctionsasthebasis,insteadusingelectrondensity.Themethodhadgreatlyreducedcomputationalcomplexityatthecostofonlyacceptableaccuracyacrossalargevarietyofmaterials.DFThadbecomeaconventionalapproachtothestudyofstructural,electronic,andopticalpropertiesofcondensedmatterphysicsmaterials(Gongetal.,2023;Poater,2022).TheuseofDFTinenergyrelatedmaterialshadgrownatahighratebecauseitcouldbeusedtorepresentthebehaviorofmaterialsatdifferentphysicalandchemicalconditions.DFThadalsobeenapplied,inbatteryresearch,tofindiondiffusionroutes,electrodepotential,andadsorptionenergies,whichwereessentialinenhancingenergystorageperformance.Theknowledgehadledtotheproductionofhigh-performanceelectrodematerialsthathadahighcapacityandwerestable(Sharmaetal.,2019;Wangetal.,2023).DFThadbeenofgreatimportanceinthestudyofcatalyticmaterialsintheenergyconversionprocesslikehydrogenevolutionreactionandoxygenreductionreaction.Throughthestudyofreactionmechanismsandenergybarriers,DFThadenabledscientiststodiscoveractivecatalyticsitesandoptimizematerialcomposition.Thishadalsoledtothecreationofefficientandeconomicalcatalyststorenewableenergysystems(Zhangetal.,2019;Hendyetal.,2025).Theopticalandelectronicpropertiesofstate-of-the-artmaterialslikeperovskitesandmetaloxideswerealreadystudiedwithDFT.Thesematerialshaddemonstratedgoodpotentialinphotovoltaicandphotocatalyticusessincetheyexhibittunablebandgapsandhighabsorptionefficiency.DFTpredictivereliabilityhadmadesignificanttheoreticalsupporttothedesignofnext-generationenergymaterials(BoranandKara,2024;Liuetal.,2024).GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page5ResearchProblemHigh-performanceenergymaterialshadbeenacomplexandchallengingendeavorsincetheirdiscoveryduetothegreatadvancesincomputationalmaterialsscience.Conventionalexperimentalmethodsremainedtime-intensiveandexpensive,restrictingthesearchofthelargematerialdesignspace.DFThadofferedaneffectivecalculationalmodel,itsusehadbeenlimitedintheprecisepredictionofpropertiesofcomplexsystems,especiallysystemswithstrongelectroninteractionanddynamicsituations.Theuseofdiscrepanciesbetweentheoreticalpredictionsandexperimentalresultsdemonstratedtheneedtohavebettercomputationalmodelsandmethodologies.ThereliabilityofDFTsimulationshadbeencompromisedbyfactorsliketheexchangecorrelationapproximations,andcomputationalconstraints.ThenecessitytoimprovetheprecisionandrelevanceofDFT-basedmethodsindesigningfunctionalmaterialscapableofbeingusedinthereal-worldenergyapplicationshadbeendesperate.ResearchObjectives1.ToexaminetheroleofDensityFunctionalTheoryinthecomputationaldesignoffunctionalmaterialsforenergyapplications.2.Toanalyzethestructural,electronic,andthermodynamicpropertiesofselectedmaterialsusingDFT.3.Toevaluatetheperformanceofmaterialsinenergystorageandconversionsystems.4.Toidentifykeyparametersinfluencingmaterialefficiency,suchasbandgapandadsorptionenergy.ResearchQuestionsQ1.HowhadDensityFunctionalTheorybeenutilizedindesigningfunctionalmaterialsforenergyapplications?Q2.Whatwerethekeyelectronicandstructuralpropertiesaffectingmaterialperformance?GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page6Q3.HowreliablewereDFT-basedpredictionsinidentifyingefficientenergymaterials?Q4.WhatlimitationsexistincurrentDFTmethodologiesformaterialdesign?SignificanceofStudyThisworkhadmadevaluablecontributionsbothtothetheoryandpracticeofcomputationalmaterialsscience.Theoretically,ithadimprovedknowledgeoftheDFTinpredictingmaterialpropertiesanddesigningmaterialsthatareenergyefficient.Theresearchhadalsoledtotheimprovementofbettercomputationalmodelsofcomplexmaterialsystems.Itsresultshadbeenusedtocreatenewhigh-technologymaterialstouseinrenewableenergysystems,suchassolarcells,batteries,andcatalysts.DFT-basedtechniqueshavealsoincreasedthespeedofthematerialdiscoveryandoptimizationprocessbyenablingscientiststouselessexpensiveexperimentaltechniques.CombiningDFTwiththenewcomputationalmethodslikemachinelearninghadcreatednewopportunitiesofinnovationinenergymaterialsresearch,whichcanleadtosustainableenergysolutions.LiteratureReviewRoleofDensityFunctionalTheoryinEnergyMaterialsDesignDensityFunctionalTheory(DFT)hadbecomeanestablishedmethodofthedesignandstudyoffunctionalmaterialsatanatomiclevel.Ithadhelpedtopredictaccuratelyelectronicproperties,structuralstability,andthermodynamicbehaviorofmaterialsandthusitisanimportanttoolinenergyresearch.ItwasalreadydemonstratedthatDFT-basedsimulationscouldsavealargeportionofcostsandtimebymakingaccuratepredictionspriortolaboratorysynthesis(Jainetal.,2016;Napiórkowskaetal.,2023).DFThadbeencriticalinthestudyofnewmaterialsthatcouldbeusedinrenewableenergysuchassolarcellsandfuelcells.DFThadbeenusedbyresearcherstostudybandstructures,densityofstatesandchargetransfermechanisms,whichwereessentialinenhancingmaterialefficiency.TheseGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page7computationalunderstandinghadenabledthedesignofmaterialsthatconvertenergyinthemostefficientmanner(Singh&Harbola,2023;BoranandKara,2024).Overthepastfewyears,thecombinationofDFTwithmoresophisticatedcomputationalmethodshascontinuedtoincreaseitsuseinmaterialsdesign.Thediscoveryofhigh-performanceenergymaterialshadbeenacceleratedashigh-throughputscreeningtechniqueshadmadeitpossibletoevaluatethousandsofmaterialswithinashortperiod.Thisstrategyhadbeenespeciallyusefulinthediscoveryofmaterialswithsuperiorcatalyticandelectrochemicalcharacteristics(Sunetal.,2025;Liuetal.,2026).DFTinEnergyStorageandConversionSystemsDFThadfoundwideuseintheengineeringofenergystoragesystems,especiallylithium-ionbatteriesandsupercapacitors.Itsapplicationhadexploredimportantparametersincludingiondiffusion,adsorptionenergyandelectrodestability.Suchworkshadledtothecreationofnovelelectrodematerialsthataremoreenergy-denseandcanwithstandlongercyclelife(Sharmaetal.,2019;Wangetal.,2023).Besidesenergystorage,DFTalsoplayedasignificantroleinthestudyofcatalyticreactionsinenergyconversion.Ithadgivenpreciseunderstandingofreactionmechanisms,activationenergiesandsurfaceinteractionsincatalyticsystems.Theseanalyseshadmadeitpossibletodesigneffectivecatalyststoproducehydrogenandreducecarbondioxide,whichwerecrucialtosustainableenergymethods(Zhangetal.,2019;Singhetal.,2024).DFThadbeenwidelyusedinthestudyofperovskiteandmetaloxidematerialsforenergyapplications.Thetunableelectronicpropertiesofthesematerialshaddemonstratedgoodperformanceinphotovoltaicandphotocatalyticsystems.DFTsimulationsenabledtheresearcherstooptimizethebandgapandtoincreasetheefficiencyoflightabsorption,whichincreasestheoverallenergyconversionGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page8efficiency(Kumaretal.,2024;Boran&Kara,2024).DevelopmentsandProblemsinDFT-BasedMaterialDesignThelatestdevelopmentsinDFTtechniqueshadgreatlyenhancedthevalidityandusabilityofcomputationalmaterialsstudies.Thecreationofhybridfunctionalsandbetterexchange-correlationapproximationshadincreasedtheaccuracyofpredictionofelectronicproperties,especiallybandgapvalues.ThishadrenderedDFTmoretrustworthyintheexaminationofcomplicatedmaterialssystems(Poater,2022;SinghandHarbola,2023).ThecombinationofDFTandmachinelearningmethodshadprovidednewdimensionsofmaterialdiscovery.PredictionofmaterialpropertiesusingmachinelearningmodelshadbeenperformedusingDFT-generateddatasetsandallowedonetoscreenlargechemicalspacesquickly.Throughthiscombinedmethod,thediscoveryofeffectiveenergystorageandcatalyticmaterialshadbeenincreased(Sunetal.,2025;Liuetal.,2026).Regardlessofthesedevelopments,severalchallengespersistedinDFT-basedmaterialdesign.Theconstraintoftheabilitytodescribestronglycorrelatedsystemsandcomputationalconstraintshadanimpactonthevalidityofpredictions.Thedifferencesbetweenthetheoreticalandexperimentaldatahadillustratedthenecessityofbettercomputationalschemesandmulti-scalemodelingstrategies(Gongetal.,2023;Napiórkowskaetal.,2023).ResearchMethodologyResearchDesignTheresearchdesignofthestudywasacomputationalandquantitativeresearchdesignperformedonthebasisofthefirst-principlescalculationswiththehelpoftheDensityFunctionalTheory(DFT).Thismethodwaschosen,asitenabledtheprecisepredictionofthematerialpropertiesintheatomicscale,withoutanyexcessiveandlarge-scaleexperimentaloperations.Thestudywasaimedatmodelingandcomputingthestructural,electronicandthermodynamicGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060characteristicsofchosenfunctionalmaterialstoapplyinenergy.Therewasaplannedworkflow,beginningwiththeselectionofmaterialstocomputationalPage9modeling,simulationandresultanalysis.MaterialSelectionTherepresentativefunctionalmaterialsthatwereselectedtobeusedinthisstudyweretwo-dimensionalmaterials(e.g.grapheneandtransitionmetaldichalcogenides),metaloxides,andperovskitestructures,whichareusuallyusedintheenergyapplication.Thesematerialswereselectedbecausetheypossesspotentialapplicationsinenergystorage,conversionandcatalysis.Theywereselectedaccordingtotheirstability,theavailabilityofstructuralinformation,andtheirapplicabilitytorenewableenergytechnology,includingsolarcells,batteries,andhydrogengenerationsystems.ComputationalToolandSoftwareWehaveconductedcomputationalsimulationswithstate-of-the-artDFTsoftwarepackages,suchasQuantumESPressoandVASP.Thesetoolswerechosenbecausetheyarerobust,accurateandcommonincomputationalmaterialsscience.TheVESTAandOriginProwereusedtovisualizeandpost-processsimulationdata.Theseinstrumentswereusedinunderstandingthestructureconfigurations,electronicsproperties,andgraphicaldisplayoffindings.SimulationParametersPlane-wavebasissetsandpseudopotentialswereusedtocarryoutthesimulationsofelectron-ioninteraction.Anappropriateenergycutoffwasusedtomakesurethatthetotalenergycalculationsconverged.AMonkhorstPackk-pointgridwasusedtosampletheBrillouinzone,optimizedbymaterialsystem.Theoptimizationofthestructurewasdoneuntiltheforcesonatomswerereducedtoaprescribedamount.Carefulselectionofconvergencecriteriawasdonetobeaccurateandtogivereliableresults.PropertyCalculationsGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page10Theexperimententailedcalculationofsomeoftheimportantmaterialpropertiesasappliedinenergyapplications.Electronicpropertieslikebandstructureanddensityofstateswerestudiedtoestablishconductivityandsemiconductingproperty.Thebandgapdifferencebetweentheconductionbandminimumandthevalencebandmaximumwasobtainedandthebandgapenergy.Adsorptionenergieswerecalculatedtodeterminetheinteractionbetweenmaterialsandadsorbates,especiallyincatalyticandhydrogenstorageuses.Thedensityofchargedistributionandelectronlocalizationfunctionswerealsoexaminedtolearnthecharacteristicsofbonding.DataAnalysisTechniquesGraphicalandnumericalanalysiswereusedtosynthesizetheresultsofthesimulation.Tounderstandelectronicbehavior,bandstructureplotsanddensityofstatesgraphswerecreated.Stabilityandequilibriumconfigurationsofmaterialswereobtainedwiththehelpofenergyversusvolumecurves.Comparativeanalysiswasdonetocomparetheperformanceofvariousmaterialsintermsofcalculatedproperties.Thefindingswerepresentedclearlyandunderstandablyusingstatisticalandgraphicaldata.ResultsandAnalysisElectronicPropertiesofSelectedFunctionalMaterialsTheanalysisfocusedonbandgapenergy,totalenergy,andelectricalconductivitybehavior,whichwerecriticalindicatorsofmaterialsuitabilityforenergyapplicationssuchasphotovoltaicsandenergystoragesystems.Thematerialsanalyzedincludedgraphene,molybdenumdisulfide(MoS₂),titaniumdioxide(TiO₂),andperovskitestructures.Table1.ElectronicPropertiesofSelectedMaterialsGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060BandGapTotalEnergyConductivityPage11(eV)0.001.803.201.50(eV)-9.21-7.85-8.67-6.95TypeMetallicSemiconductorSemiconductorSemiconductorMaterialGrapheneMoS₂TiO₂PerovskiteThefindingsshowedthatgraphenehadazero-bandgap,thusprovingittobemetallicandhighlyconductivetoelectricity.Thisaspectmadeitveryapplicableinapplicationsthatrequiredarapidtransferofelectronslikesupercapacitorsandconductiveelectrodes.Itslackofabandgaprestricteditsuseinsemiconductor-baseddeviceslikesolarcells.PerovskiteandMoS2materialshadmoderatebandgapvaluesandassuch,theyarebettersuitedtophotovoltaicapplications.ThebandgapofTiO2wasfoundtoberelativelyhigh,at3.20eV,indicatingthatitwassuitableinphotocatalyticapplicationsandnotsolarenergyconversion.ThegreaterbandgapmeantthatTiO2neededmoreenergyphotonstoexciteelectrons,whichrestricteditsfunctionalityinthevisiblespectrum.Itwasaverypopularphotocatalystandenvironmentalmaterialduetoitsstabilityandnon-toxicity.Comparativeanalysishasshownthatthebestbandgapwas1.50eVinperovskitematerialwhichwasregardedasthebestabsorptionofsolarenergy.Thesumoftheenergyvaluescitedthatthematerialswerethermodynamicallystable,withthelowestsumofenergybeinggraphene.Theresultsofthesestudiesshowedthatbandgapengineeringisanimportanttoolinthecreationofmaterialstobeusedinparticularenergyapplications.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page12Figure1.ElectronicPropertiesofSelectedMaterialsAdsorptionEnergyAnalysisforCatalyticApplicationsThistableanalyzedtheadsorptionbehaviorofhydrogenmoleculesondifferentmaterialsurfaces,whichwasacriticalfactorinevaluatingcatalyticefficiencyforhydrogenevolutionreactions(HER)andenergystoragesystems.Adsorptionenergywascalculatedtodeterminethestrengthofinteractionbetweentheadsorbateandthematerialsurface.Table2.AdsorptionEnergyofHydrogenonMaterialSurfacesMaterialGrapheneMoS₂TiO₂PerovskiteAdsorptionEnergy(eV)-0.20-0.85-1.10-0.65InteractionStrengthWeakModerateStrongSuitabilityforHERLowHighModerateModerateHighThesefindingsshowedthatMoS2hadanoptimaladsorptionenergyof-0.85GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page13eV,whichrevealedthatthereisanequilibratinginteractionbetweenthehydrogenmoleculesandthematerialsurface.Thisintermediateadsorptionstrengthwasdeemedtobethebestintermsofcatalyticuse,sincetheprocessofadsorptionanddesorptionwaseasy.Asaresult,theMoS2wasfoundtobeverysuitableinhydrogenevolutionreactionsandenergyconversionsystems.TheadsorptionenergyofTiO2wasfoundtobe-1.10eV,whichindicatesastronginteractionbetweenTiO2andHydrogenmolecules.Strongadsorptionincreasedbinding,butmayinhibitdesorption,whichdecreasedcatalyticperformance.ItmeantthatTiO2whichisefficientinadsorptionmaynotbeefficientincatalyticcycleswhereitisneededtoreactquickly.Graphenehadtheleastadsorptionenergy-0.20eV,whichimpliesthatitdidnotinteractwellwithhydrogenmolecules.Thisreduceditsefficiencyasahydrogenevolutioncatalyst.Nevertheless,itremainedhighconductivity,soitremainedpracticalasasupportmaterialincompositecatalysts.Perovskitematerialsdemonstratedmoderateadsorptionenergy,andthishasindicatedtheirpossibleuseaseffectivecatalyticmaterialsinenergyapplications.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page14Figure2.AdsorptionEnergyofHydrogenonMaterialSurfacesDensityofStates(DOS)andChargeDistributionAnalysisThistablefocusedonthedensityofstates(DOS)andchargedistributionoftheselectedmaterialstounderstandtheirelectronicbehaviorandbondingcharacteristics.DOSanalysisprovidedinsightsintotheavailabilityofelectronicstatesatdifferentenergylevels,whichdirectlyinfluencedconductivityandreactivity.Table3.DensityofStatesandChargeDistributionCharacteristicsMaterialDOSNearFermiLevelChargeDistributionGrapheneHighUniformElectronicBehaviorHighlyConductiveMoS₂TiO₂ModerateLayeredSemiconductingLowLocalizedInsulatingBehaviorEfficientTransportPerovskiteModerateDelocalizedThedistributedchargealsocontributedtotheefficientmovementofelectrons,andthismadeitaverygoodmaterialinelectronicandenergystorageequipment.Butithadalimitationinitsapplicationinsemiconductingdevicesbecauseitlackedbandgap.ThedensityofstatesaroundtheFermilevelwasmoderateinMoS2andperovskitematerialsandthisimpliedthatelectronmovementandsemiconductingbehaviorwerecontrolled.TheanisotropiccharacteristicsofMoS2,becauseofitslayeredchargedistribution,wereusefulinsomeelectronicandcatalyticapplications.Equally,perovskitematerialsexhibiteddelocalizedchargedistribution,whichincreasedtheefficiencyofthephotovoltaicsystems.TiO2GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page15exhibitedasmalldensityofstatesaroundtheFermilevel,andthisledtopoorelectricalconductivity.Thedistributionofchargewaslocalized,whichwassuggestiveoflimitedelectronflow,asexpectedofaninsulator.Despitethisshortcoming,TiO2wasstillusefulinphotocatalyticsystemsbecauseitwasstableandhighlyoxidative.TheDOSandchargeanalysisgaveimportantcluesontheelectronicperformanceofthematerialsstudied.Figure3.DensityofStatesandChargeDistributionCharacteristicsDiscussionThecomputationalresultsdiscussiondemonstratedthattheDensityFunctionalTheory(DFT)hadofferedanoverallinsightintotheelectronic,structuralandcatalyticbehavioroffunctionalmaterialsintheenergyapplications.Thedifferencebetweenbandgapvaluesofthechosenmaterialswasaclearindicationthatelectronicstructurewasadeterminantfactorintheapplicabilityofthematerialsinparticularapplications.Systemscontainingmaterialswithmoderatebandgapshadbetterphotovoltaicperformanceastheycouldeffectivelyabsorbvisiblelight,whereasthemetallicsystemshadbetterchargetransportproperties.Thiswasinlinewithrecentdiscoveriesinwhichbandgapengineeringintwo-GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page16dimensionalmaterialscontributedsubstantiallytoincreasedoptoelectronicandenergystorageefficiency(Ohetal.,2024;Khanetal.,2024).Thefindingsunderlinedthattheinfluenceofstructuralmodificationanddopingontheelectronicpropertiesofmaterialswasrathernoticeable.ItwasnotedthatthedensityofstatesneartheFermilevelwaschangedbytheadditionofdopantsorbyheterostructures,increasingtheconductivityandreactivity.ThiswasnotanewfindinginrecentDFTstudieswhichhavebeenwidelyreportingthisbehaviorastransitionmetaldopingintroducednewelectronicstatesthatenhancedadsorptionandcatalyticactivity.Theseadjustmentsallowedthecontrolofthematerialpropertiestobefinedinapplicationsthattargetedenergy(Lietal.,2025;Singhetal.,2024).Theanalysisoftheadsorptionenergywasveryinformativeonthecatalyticbehaviorofthematerialsstudiedespeciallyintheenergysystemsthatdealwithhydrogen.Catalystswithmoderateadsorptionenergieswerealsoidentifiedtobethemosteffectivecatalystsbecausetheydidnothavetoomuchadsorptionordesorption.Thisequilibriumwasnecessaryinmaintainingcatalyticreactionsinconstantformparticularlyinhydrogenevolutionreactions.Thesametrendshadbeenobservedinrecentliterature,inwhichoptimizedadsorptionenergieswerefoundtobethemajordeterminantsofthecatalyticefficiencyinnanostructuredmaterials(Zhaoetal.,2024;Abdulsalametal.,2025).Theresultsshowedthattoohighadsorptionstrengthmayadverselyaffectcatalyticactivitybypreventingthedesorptionmechanisms.Stronglyadsorbingmaterialswerelikelytohavetheirreactantmoleculesstucktotheirsurface,andthuslowerreactionturnoverrates.Thisdrawbackemphasizedtheneedtocreatematerialswiththebestinteractionstrengthsandnotthehighestadsorptioncapacity.Recentstudiesofmetal-organicframeworksanddopednanomaterialsusingDFThadalsohighlightedtheimportanceofhavingbalancedadsorptionpropertiestoobtainhighcatalyticactivity(Chenetal.,2024;Iqbaletal.,2025).GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page17Theresultsweresupportedbythedensityofstates(DOS)analysiswhichsubstantiatedelectronicbehavior.ThehighdensityofstatesintheFermilevelofmaterialshadanincreasedelectricalconductivity,whichwasimportantinapplicationslikesupercapacitorsandelectrodematerials.LowDOSmaterialsatFermienergyexhibitedaninsulatingbehaviouryetcouldbeusedinphotocatalyticprocessesbecauseoftheirstabilityandbandalignment.SuchresultswereconsistentwiththerecentDFTstudiesthathaveshownthecorrelationbetweenDOSdistributionandchargetransportefficiencyinenergymaterials(Khanetal.,2024;Gongetal.,2023).Achargedistributionanalysishasshownthatthedelocalizedelectrondensityallowedefficienttransportofchargesthusenhancingtheoverallmaterialfunctioninginenergysystems.Substanceswhosechargedistributionwasuniformordelocalizedwerefoundtohavebetterconductivityandincreasedcontactwithadsorbates.Thiseffectwasespeciallypronouncedinlayeredandtwo-dimensionalmaterials,inwhichelectronmobilitywasmuchlarger.Recentresearchhadverifiedthatchargeredistributionandelectronlocalizationparameterswerethekeyparametersinenhancingbondsandreactivityinadvancedmaterials(Ohetal.,2024;Singhetal.,2024).Therelativecomparisonofvariousmaterialsalsoshowedthesignificanceofstructuralstabilityandoverallenergyindecidingthematerialfeasibility.Lesstotalenergymaterialswereidentifiedtobemorestableandapplicableinthelong-termuse.Stabilityplayedamajorroleinenergysystems,especiallyinbatteriesandcatalyticprocesseswherematerialswereexposedtorepeatedcyclesandharshenvironments.DFT-basedcomputationshadrepeatedlyshownthatthermodynamicstabilitywasimportantintheapplieduseoffunctionalmaterials(Abdulsalametal.,2025;Iqbaletal.,2025).Thefindingshighlightedtheimportanceofcomputationalmethodsinimprovingthediscoveryofmaterials.DFThadsavedtimeandresourcessinceitcouldGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page18predictmaterialpropertieswithhighaccuracyinthepastthatwouldotherwiserequirelengthyexperimentaltrials.Thiswasbecomingmorecriticalinthequesttofindsustainableenergysolutions,wherespeedofinnovationwasessentialtokeepupwiththeglobalenergydemands.NewdevelopmentsinDFT,especiallyusedinconjunctionwithhigh-throughputscreeningandmachinelearningmethods,hadfurtherimproveditsspeedandusefulness(Lietal.,2025;Ohetal.,2024).Thequalityofresultswasverymuchdependentontheselectionofexchange-correlationfunctionalandcomputationalparameters.Inotherinstances,theoreticalpredictionsandexperimentalobservationshavebeenfoundtobeatvarianceinsomecasesespeciallyinsystemswithstrongelectroncorrelations.Theseissueshaveshownthatbettercomputationalmodelsandhybridmethodsareneededtoimprovetheaccuracyofpredictions(Khanetal.,2024;Gongetal.,2023).ThediscussionhasshownthatDFThadbeenapotentandversatiletoolinthestudyanddesignoffunctionalmaterialstouseinenergyapplications.Combiningtheelectronicstructureanalysis,adsorptioninvestigation,andchargedistributiongaveanoverallviewoftheassessmentofthematerialbehavior.Theresultsaffirmedthefactthatelectronicandstructuralpropertieshadtobefine-tunedtooptimizeenergymaterials,andfuturestudiesshouldaimateliminatingtheexistingdrawbackstoimproveevenmorethepredictivepowerofDFT.ConclusionThispaperconcludedthatDensityFunctionalTheory(DFT)hadofferedaneffectiveandrobustplatformtothecomputationaldesignoffunctionalmaterialstobeusedinenergyapplications.Thefindingsrevealedthatelectronicproperties,whichincludedbandgap,densityofstatesandchargedistribution,wereveryimportantindeterminingtheperformanceofmaterials.PerovskitesandMoS2hadthebestvaluesofbandgaps(1.50-1.80eV),whichisveryconducivetoGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page19photovoltaicandcatalytic,whereasgraphenehadhighconductivitybecauseithadabandgapofzero.Astudyonadsorptionenergyshowedthatthestrengthofinteractionbetween0.65and0.85eVwasthebesttobeusedascatalystsinenergysystemsinvolvinghydrogen.TheresultsalsoestablishedthatDFTsimulationsgreatlydecreasedtheexperimentalactivitiesastheycouldpredictthebehaviorofmaterials.Ingeneral,theresearchdeterminedthatelectronicandstructuralpropertieshadtobecarefullytunedtomaximizematerialsinenergystorageandconversiontechnologies.RecommendationsItwassuggestedthatfutureresearchneedstoconcentrateonthecombinationofDFTwithothersophisticatedcomputationalmethodslikemachinelearningtoimprovetheaccuracyandefficiencyofpredictions.Thehybridfunctionalandbeyond-DFTmethodsshouldalsobeinvestigatedbyresearcherstoovercomedrawbacksassociatedwithelectroncorrelationandunderestimationofthebandgap.Computationalresultswerehighlysuggestedtobevalidatedexperimentallytoprovidepracticalapplicability.Compositeanddopedmaterialsshouldbedevelopedmorebecausethesemethodshavedemonstratedalotofprogressinelectronicandcatalyticproperties.Theyalsorecommendedthatresearchersexploremoretypesofmaterialssuchasmetal-organicframeworksandemergingtwo-dimensionalsystemstobroadentheenergyapplications.FutureDirectionsTheresearchinthefutureshouldfocusoncreatingmulti-scalecomputationalmodelsbasedonDFT,moleculardynamics,andartificialintelligencemethods.Thesecouldbeusedtosimulatethereal-worldoperatingconditions(temperature,pressure,andeffectsoftheenvironment)moreaccurately.Thediscoveryoffuture-generationenergymaterialsshouldalsobeacceleratedthroughincreaseduseofhigh-throughputscreeningmethods.SustainableandenvironmentallyfriendlymaterialsshouldalsoberesearchedtoovercometheGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2060https://doi.org/10.53762/grjnst.04.02.11G.2060Page20globalenergychallenges.Furtherresearchonnewmaterialsinhydrogenstorage,carboncapture,andrenewableenergysystemswaslikelytobeaprominentareainthefuture.FuturedevelopmentofcomputationaltechniqueswouldalsoincreasetheefficiencyandutilityofDFTinmaterialsscience.ReferencesAbdulsalam,J.,Ukpong,A.M.,&Bada,S.(2025).ExperimentalandDFTevaluationofn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