能源人工智能報告2024-70正式版-WN8

ANL-23/69ADVANCEDRESEARCHDIRECTIONSONAIFORENERGYReportonWinter2023WorkshopsClausDanielArgonneNationalLaboratoryJessC.GehinIdahoNationalLaboratoryKirstenLaurin-KovitzArgonneNationalLaboratoryBryanMorrealeNationalEnergyTechnologyLaboratoryRickStevensArgonneNationalLaboratoryWilliamTumasNationalRenewableEnergyLaboratoryApril2024ABOUTARGONNENATIONALLABORATORYArgonneisaU.S.DepartmentofEnergylaboratorymanagedbyUChicagoArgonne,LLCundercontractDE-AC02-06CH11357.TheLaboratory’smainfacilityisoutsideChicago,at9700SouthCassAvenue,Argonne,Illinois60439.ForinformationaboutArgonneanditspioneeringscienceandtechnologyprograms,see.DOCUMENTAVAILABILITYOnlineAccess:U.S.DepartmentofEnergy(DOE)reportsproducedafter1991andagrowingnumberofpre-1991documentsareavailablefreeatOSTI.GOV(),aserviceoftheUSDept.ofEnergy’sO?ceofScienti?candTechnicalInformation.ReportsnotindigitalformatmaybepurchasedbythepublicfromtheNationalTechnicalInformationService(NTIS):U.S.DepartmentofCommerceNationalTechnicalInformationService5301ShawneeRdAlexandria,VA22312Phone:(800)553-NTIS(6847)or(703)605-6000Fax:(703)605-6900Email:orders@ReportsnotindigitalformatareavailabletoDOEandDOEcontractorsfromtheO?ceofScienti?candTechnicalInformation(OSTI):U.S.DepartmentofEnergyO?ceofScienti?candTechnicalInformationP.O.Box62OakRidge,TN37831-0062Phone:(865)576-8401Fax:(865)576-5728Email:reports@DisclaimerThisreportwaspreparedasanaccountofworksponsoredbyanagencyoftheUnitedStatesGovernment.NeithertheUnitedStatesGovernmentnoranyagencythereof,norUChicagoArgonne,LLC,noranyoftheiremployeesoro?cers,makesanywarranty,expressorimplied,orassumesanylegalliabilityorresponsibilityfortheaccuracy,completeness,orusefulnessofanyinformation,apparatus,product,orprocessdisclosed,orrepresentsthatitsusewouldnotinfringeprivatelyownedrights.Referencehereintoanyspeci?ccommercialproduct,process,orservicebytradename,trademark,manufacturer,orotherwise,doesnotnecessarilyconstituteorimplyitsendorsement,recommendation,orfavoringbytheUnitedStatesGovernmentoranyagencythereof.Theviewsandopinionsofdocumentauthorsexpressedhereindonotnecessarilystateorre?ectthoseoftheUnitedStatesGovernmentoranyagencythereof,ArgonneNationalLaboratory,orUChicagoArgonne,LLC.CoverdesignbyArgonneNationalLaboratory.CoverimageryviaShutterstock.AIFORENERGYAdvancedResearchDirectionsonAIforEnergyReportontheU.S.DepartmentofEnergy(DOE)Winter2023WorkshopSeriesonArtificialIntelligence(AI)forEnergyProgramCommitteeClausDanielAssociateLaboratoryDirector,ArgonneNationalLaboratoryAssociateLaboratoryDirector,ArgonneNationalLaboratoryAssociateLaboratoryDirector,ArgonneNationalLaboratoryKirstenLaurin-KovitzRickStevensU.S.DepartmentofEnergyCerenSusut-BennettKeithBenesProgramManager,U.S.DepartmentofEnergySeniorFellow,U.S.DepartmentofEnergyKennethHamTechnologyManager,U.S.DepartmentofEnergyTechnologyManager,U.S.DepartmentofEnergySeniorTechnologyAdvisor,U.S.DepartmentofEnergyTassosGolnasMikeC.RobinsonKeyContributorsArgonneNationalLaboratoryNationalRenewableEnergyLaboratoryMihaiAnitescu,AlecPoczatek,AndrewSiegel,SibenduSom,RichardVilimRayGrout,BenjaminKroposkiNationalEnergyTechnologyLaboratoryBrookhavenNationalLaboratoriesKellyRoseMengYueOakRidgeNationalLaboratoryIdahoNationalLaboratoryPrasannaBalaprakash,PrashantJain,TejaKurugantiAhmadAlRashdan,ChristopherRitterPacificNorthwestNationalLaboratoryLawrenceBerkeleyNationalLaboratoryCourtCorley,RobertRalloMaryAnnPiette,TianzhenHongSandiaNationalLaboratoriesLawrenceLivermoreNationalLaboratoryMattRenoJohnGrosh,BrianVanEssenLosAlamosNationalLaboratoryHariViswanathanEditorialFrankAlexanderEmilyM.DietrichDirectorAIResearchandStrategicDevelopment,ArgonneNationalLaboratoryStrategicProgramCommunicationsLead,ArgonneNationalLaboratorySpecialThanksTotheArgonneNationalLaboratoryCommunicationsandPublicAffairsDivision’sWritingCenterofExcellence,includingkeysupportfromAndreaManningandLorenzaSalinasiAIFORENERGYEXECUTIVESUMMARYArtificialintelligence(AI)providesatransformationalopportunitytorapidlydeploynewcleanenergy,securecriticalgridenergyassetsfromthreatactors,andreducecapitalandoperationalcostsofnext-generationenergytechnologiesandtheconnectedsystemsthatembodythedemandsideofthetransformation.TheUnitedStateswillneedtoinvesttrillionsofdollarsinenergyinfrastructuretoreachthenation’sclean,resilientgoalsby2050.AttheDepartmentofEnergy(DOE)nationallaboratories,AIhasincrediblepotentialacrossnuclear,renewable,andcarbonmanagementdomainsduetotheabilitytorepresentEXEMPLARGRANDCHALLENGESFROMTHECHAPTERSOFTHEAIFORENERGYREPORT01NuclearEnergy:AcceleratingtheLicensingandRegulatoryProcess02PowerGrid:BuildingCyber-andAll-HazardsResilientandSecureEnergySystems03CarbonManagement:RealizingAVirtualSubsurfaceEarthModelunprecedentedsystemmodelsizes,provideintensecomputationalresources,andcaptureknowledgefromaworkforceofthenation’stopscientists.Inaggregate,AIcouldreducethecosttodesign,license,deploy,operate,andmaintainenergyinfrastructurebyhundredsofbillionsofdollarsifthefollowingappliedenergychallengesarerealized.04EnergyStorage:EquitableandAccessibleDeployment05EnergyMaterials:AdvancingBeyondMaterialPropertiesandPerformancetoAchieveLifecycle-AwareMaterialsDesignAIprovidesabreakthroughopportunitytoacceleratethedesign,deployment,andlicensingofnewenergycapacity.Commercialpowerplantdesignandlicensingareamulti-yeareffortthatcanaccountforupto50%oftimetomarketfornewenergydeployments.DOEestimatestheonboardingof1.6TWofnewsolarcapacityand200GWofnewnuclearcapacity,whileenablinghydrogen,geothermal,criticalminerals,andothercleanenergyresourcesby2050,withacostthatcouldapproachtrillionsofdollarsinnationalinvestmenttomeetgrowingglobalcleanenergydemand.Additionally,DOEestimatestheneedtoreducecoststolessthan$100/netmetrictonofCO2equivalentforbothcarboncaptureandstoragetoaddresscarbonpollution.AIhasthepotentialtoreduceschedulesbyapproximately20%acrossnewcleanenergydesigns,withpotentialsavingsinthehundredsofbillionsofdollarsby2050.Additionally,AIcanaugmentandextendtheenergydevelopmentworkforcethatwillbeinhighdemand.Theenergygrid’sgenerationcapabilitiesanddemand-sideneedsareexperiencingrapidchangesinrequirementsforsecure,reliable,andresilientplanningandoperationscontrols.Theincreasingvolumesofcommunications,controls,data,andinformationaregrowingthedigitallandscape,increasingflexibilityandimprovingthereliabilityandagilityofthegridbyincreasingvisibilitytooperatorsandconsumers.Integratingenergysystemstogetheracrossgridoperationscouldsavebillionsofdollarsannuallybyautomaticallyoptimizinggenerationanddemand-sideneeds.Autonomousoperationtechnologiescanprovidemonitoring,control,andmaintenanceautomationacrossvariouscleanenergytechnologies.Distributed,consumer-sitedtechnologiesarechangingthepowerloadwithelectricvehicles(EVs),distributedstorage,smartbuildings,andappliancesaddingnewintelligencetoloadswhilealsorequiringtheintegrationofconsumer-sitedcontrollability.Furthermore,newadvancednucleartechnologies,suchasmicroreactors,willlikelyneedtooperateautonomouslytorealizeeconomiesofscale.DeliveringAIcapabilitiesacrosstheoperationsandmaintenancelifecyclecantransformsafety,efficiency,andinnovationwithinnationalenergyproductionanddistributioninfrastructure.Thesitingofnewenergycapacityisacomplexchallengebalancingenergygenerationoptions,communityneeds,environmentalfactors,andresiliencyconsiderations.AIcouldaidcommunityenergyplanningbasedonacomprehensivedatasetandatrainedcommunityenergyfoundationmodelthatcapturescharacteristicsofandinteractionsbetweenphysicalinfrastructure,humanbehavior,andclimate/weatherimpacts.AItoolscanachievenationalcleanenergygoalsbydemocratizingcommunity-levelcleanenergyresourcesandfacilitatingtheidentificationofenergytransitionpathwaysthatreflectlocalobjectives,demographics,andlegacyinfrastructure.Naturaldisastersandhuman-causedeventsareoccurringmorefrequentlyandwithmoreintensity,deliveringsignificantimpactstothenation.Adverseweathereventsareincreasinglydisruptingsupplychains,damagingpropertyandassets,andmakingcertainareaslesshabitable.TheU.S.experiencedarecord28uniqueweather/climatedisastersthatcostatleast$1billionin2023.Climatechange,urbanization,populationgrowth,aginginfrastructure,anddeferredmaintenanceincreaseriskstocommunitiesandhumansurvival.AnAI-based,all-hazardsglobalresponsesystemthathasingestedglobalandEXECUTIVESUMMARY1AIFORENERGYstakeholderdatasets,facilitatinginternationalpreparation,response,andrecovery,canenhancepreparednessandresiliencesolutionsandinformfasterrecovery.Science-basedmodelsenhancedwithAImulti-modelingapproachescanimprovepredictionsofsubsurfacepropertiesandsystemstoimproveresourcediscoveryfordomesticcriticalmaterials,geothermalreservoirs,uranium,andwateropportunities.ThiscapabilitycouldcreateanationalsubsurfaceAIanddatatestbedtoenableresponsiblecommercial,regulatory,andscience-baseddiscoveryanddevelopment.AIcanimprovetheforecastingandpredictionofsubsurfacepropertiesandsystems,informingandtransformingourabilitytoreducerisksandresponsiblyinteractwiththesubsurface.Energymaterialinnovationiskeytorealizingnationalcleanenergygoals.Increasingautomationinmaterialslaboratories,suchasautonomouslaboratories,cantransformthedesignanddiscoveryofnewmaterials.AIcanalsoacceleratematerialsqualificationthroughautomationofmaterialstesting,leadingtonewenergytechnologiessuchasadvancednuclearreactorsandnewbatterycertifications.Inadditiontothesecross-cuttingopportunities,thereareuniqueusecasesinnuclear,renewable,andcarbonmanagementenergysystems.Forexample,whileemissions,prediction,measurement,andmitigationareuniquelyimportanttocarbonmanagement,theunderlyingcomputationalinfrastructurecouldbesharedacrossgrandchallenges.Unattendedoperationofnuclearreactorshasuniquelife-safetyconsiderations;however,manyplant-leveldigitaltwinsofpiping,valve,heatexchanger,andcoolingtowerscouldbesharedacrossappliedenergydomains.ADOEconsortiummodelfromallenergydomains,integratedwithexpertisefromsubject-matterexpertsfromthelaboratories,couldhelpensureanddriveefficiencyacrossresearchchallenges.Toaccomplishthesegrandchallenges,keydevelopmentsareneeded.Thelaboratoriesmustestablishaleadershipcomputingecosystemtotrainandhostdataandfoundationmodelsatever-increasingscales.Fine-tunedmodelsneedtobedevelopedforeachdomainthatarecoupled,wherepossible,withground-truth,first-principlesphysics.Althoughthelaboratorieshavehundredsofpetabytes’worthofdata,onlysmallamountsofthesedataarecataloged,warehoused,andreadyforAImodelingestion.Curationofone-of-a-kind,ground-truthdatacoupledwithenergyindustrydatawillbeessentialtobuildingmodelsatthesescales.Mostimportant,partnershipsacrosslaboratories,government,industry,andacademiaareessentialtorealizingthetransformationalbenefitsofAIforenergy.ThisAIforEnergyreportfurtherdetailsgrandchallengesthatprovidesignificantopportunitiesforenergyapplicationsacrossnuclearenergy,thepowergrid,carbonmanagement,energystorage,andenergymaterialsoverthenextdecade.ThemainconclusionsandopportunitiesfromthisstudyareavailableintheKeyFindingssectionofthisreport.EXECUTIVESUMMARY2AIFORENERGYINTRODUCTIONimprovetheeconomicsofnuclearsystemdesignandoperation.ThesechallengesspanmultiplescientificandengineeringdisciplinesandrequireAI’suniqueabilitytoprocessvastamountsofdataandintegratephysicsmodelsonascalepreviouslyunattainable.Thisintegrationmustbecarriedoutinaseamlessmanner.AIcanfacilitatethiscoordination,potentiallyreducingcostssignificantlycomparedtotraditionalnuclearenergydevelopmentanddeploymentapproaches.RecentGenerationIIIreactorcommissioningshaveexperiencednotabledelaysandcostoverruns,oftenduetoprematureconstructionstarts.AI,developedunderscienceandtechnologyinitiatives,canmitigatesuchissuesbyenhancingdesigncompletionandprocessefficiency.Theintricateinterdependencieswithinthenuclearenergysectorposechallengeswell-suitedforAIsolutions.Whileteamsofexpertsmightstrugglewiththebreadthanddepthofnecessaryknowledge—hamperedbylimitationssuchassuccessionplanningandindividualbias—AIoffersunparalleledknowledgecaptureandthecapabilitytodiscerncross-disciplinaryconnections.ThisadvantageiscriticalinthreespecificchallengeareaswhereAI/MLcansurpasstheperformanceofhumanteams:(1)streamliningthelicensingandregulatoryprocess;(2)acceleratingdeployment;and(3)facilitatingunattendedoperation.EmbracingandextendingAIcapabilitiescouldsignificantlyenhancethenuclearindustry’sefficiencyandinnovation,allwhilecontinuingtoimprovesafety.AnimportantaspectoftheU.S.DepartmentofEnergy’s(DOE)missionistoensurethenation’senergyindependenceandsecuritybothintheshortandlongterm.Keytomeetingthischallengearecontinuedadvancementsinartificialintelligence(AI),especiallyinthecontextofenergy.Asaninitialsteptowardaddressingthesechallenges,agroupofabout100expertsonAI/machinelearning(ML)andappliedenergyconvenedatArgonneNationalLaboratoryinDecember2023overthecourseoftwodaystomapoutfutureneedsrelatedtoutilizingAI.ThegoalofthemeetingwastodetailpressingtechnicalchallengesandproposeAI-assistedsolutions.Fivedomainareaswereidentified(detailedbelow),alongwithpotentialpathsforward.DOEisideallypositionedtoaddresschallengesassociatedwithenergyindependenceandsecurityduetoitsuniquesetofassets.Theseassetsincludeahighlyskilledworkforcewithrelevantdomainexpertise(nuclearengineering,chemistry,materialsscience,networkedsystems,etc.),andanarrayofworld-leadingexperimentalfacilitiesformakingadvancesinmaterials,chemistry,etc.Theseincludesynchrotronlightsources,nanocenters,high-performancecomputingresources,andautonomouslaboratories.ByintegratingtheseresourceswithotherAIcapabilitiesoutlinedinthepreviousAIforScience,Energy,andSecurity(AI4SES)report,theDOEcanleverageAItostayattheforefrontoftherapidlyevolvinglandscape.TheappliedenergyfocusdescribedinthisreportcentersonfiveareasvitaltotheenergyfutureoftheU.S.,aswellasunderscoresthecriticalrolethatAIcanplayinshapingourworld—highlightingtheurgencyandimportanceofbeingleadersinAItoensureimpactfulsolutionstoglobalenergyneeds.TheseareasincludeNuclearPower,PowerGrid,CarbonManagement,EnergyStorage,andEnergyMaterials.ItwillbeessentialtointegratethesetogetherandwithothereffortsinAIforscienceandtechnology.Complexity,thelarge-scaleeffortinvolved,real-timedecisionmakingrequired,robustnessofsystems,andsafetyimplicationsallposeextrachallenges.Thegrandchallengesdescribedinthisreportspanmultipledisciplinesandhavenotbeensolvedbyconventionalmethods.ThepowerofAIforsolvingsuchproblemsliesinitscapacitytosimultaneouslyhandlemultiplesystemTheglobalenergysystem,whichpowerstheworld’seconomy,iscurrentlyexperiencingatransformationunparalleledsincetheintroductionofelectricityoveracenturyago.Thisevolutionencompassestheshifttowardagrid,characterizedbyenhancedcomputercontrol,communication,informationexchange,anddataanalytics.Concurrently,thereisasurgeinsmart,distributedtechnologies,exemplifiedbythewidespreadadoptionofelectricvehicles,photovoltaics,localenergystoragesolutions,andintelligentbuildingsandappliances.Thistransitionisfurthercomplicatedbyincreasingelectrificationandasignificantshiftintheprimaryenergymixtowardmorevariablerenewableenergysources,suchaswindandsolarpower.Thefuturemanagementofthepowergridintroducesalevelofuncertainty,particularlyaspartsofthegridcomeunderdiverseownershipandjurisdictionalcontrol,complicatingfutureplanningandoperations.RecentdevelopmentsinAIofferpromisingsolutionstomanagethefuturegrid’sintricacies.AI'spotentialtorevolutionizeenergysystemoperationsisvast,includingbyenablingproactive,real-timemanagement;enhancingresilienceandsecurityagainstcyberandallotherhazards;andfacilitatingthecharacteristicswhileincorporatingbothdataandspecificdomain(e.g.,physics,chemistry,etc.)modelsandtodosoonascaleandatacomplexityotherwisenotpossible.NuclearenergyplaysapivotalroleinthecleanenergylandscapeoftheU.S.,representingabouthalfofitscleanelectricitygeneration.Toachieveitsfullpotential,thenuclearindustrymustadoptand,whererequired,advancethelatestAItoolsandtechnologies.AI’stransformativepotentialisparticularlyrelevantinmethodologieswhichcoulddrasticallyINTRODUCTION3AIFORENERGYdesignandplanningofa100%cleanelectricitysystemby2035.Advancesinmaterialsscienceforenergyapplicationsareneededforgenerating,storing,andutilizingenergyefficiently,encompassingstoragematerials,photovoltaics,Intermsofcarbonmanagement,DOE’sOfficeofFossilEnergyandCarbonManagement(FECM)isdedicatedtopioneeringtechnologiesaimedatreducingcarbonemissionsandlesseningtheenvironmentalfootprintoffossilfuelgenerationandusage.Inpursuitofthesegoals,severalgrandchallengeshavebeenidentified,includingdeveloping"disCO2ver,"adynamicdigitalsystemdesignedformulti-scalesimulationandforecastingtosupportinterofficeandextramuralcollaborations.ThistestbediscrucialforspeedinguptheU.S.shifttowardacarbon-neutraleconomybyimprovinggreenhousegas(GHG)mitigationandenhancingtheresilienceofenergyinfrastructure.Anothersignificantinitiativeisthepushtocreateavirtualsubsurfacedigitaltwin,usingAItofind,aggregate,andimproveaccesstomulti-modaldataThisendeavorwillenablemoreenvironmentallyfriendly,cleanenergyresourceextractionandsecurewasteandemissionsstorage.Additionally,effortsarefocusedonhasteningthedevelopmentandselectionofoptimalmaterialsforlarge-scalecarboncaptureandremoval.Wherethetransitiontorenewableswillbemorechallenging(e.g.,heavyindustry),toolsareneededforGHGemissionsprediction,measurement,andmitigation.Afutureneedhighlightedistheambitiontorendertheearth"transparent"throughAIandmulti-modaldata.Theproposedsolutionencompassesutilizingabroadarrayofgeophysicaltechniquestogatherdiversedatasets,employingAItoenhancesensordesignanddatacollection,andleveragingAIforsubsurfacecharacterization.thermoelectrics,catalysts,andadvancedalloys.ThesematerialsarecrucialfordrivingforwardU.S.objectivesincleanenergy,economicgrowth,andenergyjustice,aimingtoreducerelianceonnonrenewableresourcesandlessenenvironmentalimpacts.TomeettheU.S.targetsinsustainabilityandcleanenergyby2050,thereisanurgentneedtohastenthediscovery,design,production,andcertificationofenergymaterialswithtailoredpropertiesandperformance.Thisprocessinvolvesnavigatingvastparameterspaces,farbeyondmanualexplorationcapabilities,anddevelopingcost-effective,sustainableproductionmethodswhileaddressingdurabilityandlifecyclemanagementchallenges.AIissignificantlyimpactingenergymaterialsresearchbyacceleratingmaterialdiscoveryanddesign,enhancinglaboratoryautomationforquickersynthesisandtesting,andfacilitatingthetransitiontoindustrial-scaleapplication.AI'sroleistransformative,promisingtoleadtothediscoveryofnewmaterials,predicttheirproperties,andachievebreakthroughstoovercomeenergysectorchallenges.SuccessinthisdomaincouldcementU.S.leadershipindevelopinghigh-performance,safe,andenvironmentallyfriendlyenergymaterials,supportingashifttowardacirculareconomy.Thefocusareasinthisreportincludeimprovingenergygeneration,storage,andconversionefficiency;enhancingenvironmentalsustainabilityandscalability;andreducingenergyproductionanduseimpacts.Addressingtheseneedsrequiresnewscientificandtechnologicalbreakthroughstoacceleratematerialdiscovery,enhancepredictivedesign,andbridgethegapfromlaboratoryresearchtoindustrialapplication,movingbeyondtraditionaltrial-and-errormethodsforrapidmaterialdeployment.Energystorage,independentofitssource,whetherrenewable,nuclear,orcarbonmanagement,willcontinuetoplayacrucialroleinfutureenergysystems.Thedemandforimproved(includingmuchlargercapacity)energystoragesystemswillnecessitatediversetechnologiestomeetthevariedrequirementsacrossdifferentsocietalsegments.Thecomplexityandbreadthoftheserequirementspresentasignificantchallengeindevelopingnewsolutionsanddoingsoontherequiredacceleratedtimescale.Asaresultofthescaleoftheproblemandthecomplexcoordinationrequiredtodevelopanddeploythesesystems,traditionalprocessesaretooslowtorespondtoambitioustimelines.Thechallengesdiscussedinthisdocumentincludeacceleratingthedevelopmentofenergystoragetechnologies;ensuringefficientdeployment,operation,andcontrolofenergystoragesystems;andguaranteeingthatdeploymentisequitableandaccessibletoall.Inthisreport,theseveralrecurringthemesthathaveemergedinclude:

Theneedforrapidandaccurateinsilicodesignandtestingfrommaterials,chemistry,andstoragesystems.

Theneedforimprovedmethodsofquantifyinguncertaintiesinpredictionsandsystemperformance.

TheneedfortheuseofAItointegratemultimodaldataforbothscientificandtechnologicaladvancesaswellasforindustrypolicydesign,energy,andenvironmentaljustice.INTRODUCTION4AIFORENERGYKEYFINDINGSFORESTABLISHINGTHECROSS-CUTTINGASPECTSOFAISUPREMACYNEEDEDTOENSURESUCCESSINENERGYMISSIONAREASTheenergymissionareas—fossil,carbonmanagement,nuclear,renewable,andenergyefficientusageanddeliveryCROSSCUTTINGASPECTSNEEDEDINENERGYMISSIONAREAS—havecrosscuttingneedsforartificialintelligence(AI)technology.Thenatureoftheseneedsisanchoredinthehigh-consequenceenvironment,theurgency,andthecomplexityofthesystemsinvolved.Establishingmission-readytechnologyintheseareasbuildsamorerobustandtrustworthycapabilitythatmaturesthebaselinecapabilitynecessarytosupportexploratoryresearch.Thefollowingmustbepursuedacrossthefiveenergyareasofnuclearenergy,powergrid,carbonmanagement,energystorage,andenergymaterialsHigh-Consequence

High-ConsequenceDecisionsandCriticalOperations

AccreditationofAIMethodsThefiveareasdiscussedduringtheAIforEnergyworkshopandconsequentreportcoverlargeportionsoftheenergyspaceandsurfacearobustsetofcapabilitiesthatwillalsosupportthebroaderagenda.Theprimaryconclusionsinclude:

TrustworthinessandVerificationandValidation(V&V)

DevelopmentandMaintenanceofTalenttoRespond1.ThepotentialforAItohavetransformativeimpactontheenergymissioncriticaltoU.S.economicsecurityishigh.toAIImplicationsUrgency2.ItiscriticalfortheenergycommunitiestoincluderesearchintoAItechnologydevelopmentinordertocultivatetheappropriatetalentthatcanrespondtocrisesthatmayemergeinthefieldandrequireinterdisciplinaryexpertisetoaddress.

MoveatSpeedofField;Micro-Revolutionsvs.Incrementalism

MissionImperativeComplexity3.TheenergycommunityneedsarelargelyalignedwiththesixareasidentifiedintheAI@DOEroundtable,asfollows:energyefficientAI;intrinsicallyexplainableAI;scientificgenerativeAI;safe,secure,andtrustworthyAI;AIforprevention,preparedness,andrespondingtonationalemergencies;andAIforautomation.

InverseProblems

RobustnesstoChangingEnvironments

MultimodalandScalablecanbe:(1)demonstratedtobeprovablycorrect,withknownconditionsofwhentheywillbreak;(2)understoodwellenoughtoinspireconfidenceintheperformance;and(3)supportedbyaworkforcethatcandiagnoseandcorrectproblems.Solvingthissetofchallengesultimatelysupportssecurityanddiscoverytasks,aswell.4.Ensuringsafe,secure,andtrustworthysolutionshaselevatedimportanceintheenergymissionareas,andrigorouslyassessing,documenting,andcertifyingAItechnologiesregardingtheseconcernsareimportantdifferentiatorsfromscientificdiscoveryapplications.5.Investmentsinthesegeneralareasneedtobepursuedinanenvironmentofuseanchoredinenergyapplicationareastoaddresstheuniquefeaturesoftheenergyarea,anddoingsowillinherentlyincreasetherobustnessofsolutionsappliedinthescienceandsecurityarenas.Suchanenvironmentcanbecreatedthroughacoordinatedstructurethatcombinestheunderlyingcrosscuttingresearchwithend-useapplications.Successacrossthefiveenergyspacescoveredinthisreportwillinvolvesuccessintheareasofhigh-consequence,urgency,andcomplexity.High-ConsequenceHIGH-CONSEQUENCEDECISIONSANDCRITICALOPERATIONSIncontrasttoatraditionalresearchenvironment,itwillnotbesufficienttoemployamethodthatappearstoworkwell—o