人工智能、科学发现和产品创新 Artificial Intelligence,Scientific Discovery,and Product Innovation

ArtificialIntelligence,ScientificDiscovery,andProductInnovation*

AidanToner-Rodgers†

MIT

November4,2024

Thispaperstudiestheimpactofartificialintelligenceoninnovation,exploitingtherandomizedintroductionofanewmaterialsdiscoverytechnologyto1,018scientistsintheR&DlabofalargeU.S.firm.AI-assistedresearchersdiscover44%morematerials,resultingina39%increaseinpatentfilingsanda17%riseindownstreamproductin-novation.Thesecompoundspossessmorenovelchemicalstructuresandleadtomoreradicalinventions.However,thetechnologyhasstrikinglydisparateeffectsacrosstheproductivitydistribution:whilethebottomthirdofscientistsseelittlebenefit,theoutputoftopresearchersnearlydoubles.Investigatingthemechanismsbehindtheseresults,IshowthatAIautomates57%of“idea-generation”tasks,reallocatingresearcherstothenewtaskofevaluatingmodel-producedcandidatematerials.TopscientistsleveragetheirdomainknowledgetoprioritizepromisingAIsuggestions,whileotherswastesignificantresourcestestingfalsepositives.Together,thesefindingsdemonstratethepotentialofAI-augmentedresearchandhighlightthecomplemen-taritybetweenalgorithmsandexpertiseintheinnovativeprocess.Surveyevidencerevealsthatthesegainscomeatacost,however,as82%ofscientistsreportreducedsatisfactionwiththeirworkduetodecreasedcreativityandskillunderutilization.

*IamespeciallygratefultoDaronAcemoglu,DavidAutor,JacobMoscona,andNinaRousillefortheirguidanceandsupport.IalsothankNikhilAgarwal,JulesBaudet,VladimirBulovi´c,JamieEmery,SarahGertler,JuliaGilman,JonGruber,DanielLuo,FlorianMudekereza,ShakkedNoy,JulieSeager,AnandShah,AdvikShreekumar,ScottStern,LauraWeiwu,WhitneyZhang,andseminarparticipantsattheMITAppliedMicroLunchforhelpfulcomments.Iamindebtedtoseveralofthelab’sscientistsfortheirgenerosityinexplainingthematerialsdiscoveryprocess.ThisworkwassupportedbytheGeorgeandObieShultzFundandtheNationalScienceFoundationGraduateResearchFellowshipProgramunderGrantNo.2141064.IRBapprovalforthesurveywasgrantedbyMIT’sCommitteeontheUseofHumansasExperimentalSubjectsunderIDE-5842.JELCodes:O31,O32,O33,J24,L65.

†MITDepartmentofEconomics;Email:

aidantr@;

Website:

aidantr@github.io

1

1Introduction

TheeconomicimpactofartificialintelligencewilldependcriticallyonwhetherAItechnologiesnotonlytransformtheproductionofgoodsandservices,butalsoaugmenttheprocessofinnovationitself(

Aghionetal.,

2019;

Cockburnetal.,

2019)

.Recentadvancesindeeplearningshowpromiseingeneratingscientificbreakthroughs,particularlyinareassuchasdrugdiscoveryandmaterialssciencewheremodelscanbetrainedonlargedatasetsofexistingexamples(

Merchantetal.,

2023;

Mullowneyetal.,

2023;

DeepMind,

2024)

.Yetlittleisknownabouthowthesetoolsimpactinventioninareal-worldsetting,whereR&Dbottlenecks,organizationalfrictions,orlackofreliabilitymaylimittheireffectiveness.Asaresult,theimplicationsofAIforboththepaceanddirectionofinnovationremainuncertain.Moreover,theconsequencesforscientistsareambiguous,hingingonwhetherAIcomplementsorsubstitutesforhumanexpertise.

Toprovideevidenceonthesequestions,IexploittherandomizedintroductionofanAItoolformaterialsdiscoveryto1,018scientistsintheR&DlabofalargeU.S.firm.Thelabfocusesonapplicationsofmaterialsscienceinhealthcare,optics,andindustrialmanufacturing,employingresearcherswithadvanceddegreesinchemistry,physics,andengineering.Traditionally,scientistsdiscovermaterialsthroughanexpensiveandtime-consumingsystemoftrialanderror,concep-tualizingmanypotentialstructuresandtestingtheirproperties.TheAItechnologyleveragesdevelopmentsindeeplearningtopartiallyautomatethisprocess.Trainedonthecompositionandcharacteristicsofexistingmaterials,themodelgenerates“recipes”fornovelcompoundspredictedtopossessspecifiedproperties.Scientiststhenevaluatethesecandidatesandsynthesizethemostpromisingoptions.Onceresearcherscreateausefulmaterial,theyintegrateitintonewproductprototypesthatarethendeveloped,scaled,andcommercialized.

ThelabrolledoutthetoolinthreewavesstartinginMayof2022.Teamsofresearcherswererandomlyassignedtowaves,allowingmetoidentifytheeffectsofthetechnologybycomparingtreatedandnot-yet-treatedscientists.Thecohortsarebalancedonobservableslikeeducation,experience,andpastperformance,confirmingsuccessfulrandomization.UsingdetaileddataoneachstageofR&D,IstudyAI’simpactonmaterialsdiscoveryanditsdownstreameffectsonpatentingandproductinnovation.

AI-assistedscientistsdiscover44%morematerials.Thesecompoundspossesssuperiorproper-ties,revealingthatthemodelalsoimprovesquality.Thisinfluxofmaterialsleadstoa39%increaseinpatentfilingsand,severalmonthslater,a17%riseinproductprototypesincorporatingthenewcompounds.Accountingforinputcosts,thetoolboostsR&Defficiencyby13-15%.Theseresultshavetwoimplications.First,theydemonstratethepotentialofAI-augmentedresearch.Second,

2

theyconfirmthatthesediscoveriestranslateintoproductinnovations,notfullybottleneckedbylaterstagesofR&D.

AIacceleratesthepaceofinnovation,buthownovelarethesebreakthroughs?Akeyconcernwithusingmachinelearningforscienceisitspotentialtoamplifythe“streetlighteffect”(

Khurana,

2023;

Kim,

2023;

Hoelzemannetal.,

2024

).Becausemodelsaretrainedonexistingknowledge,theymightdirectsearchtowardwell-understoodbutlow-valueareas.Contrarytothishypothesis,IfindthatthetoolincreasesnoveltyinallthreestagesofR&D.Ifirstmeasuretheoriginalityofnewmaterialsthemselves,followingthechemicalsimilarityapproachof

Deetal.

(2016)

.Comparedtoexistingcompounds,model-generatedmaterialshavemoredistinctphysicalstructures,suggestingthatAIunlocksnewpartsofthedesignspace.Second,Ishowthatthisleadstomorecreativeinventions.Patentsfiledbytreatedscientistsaremorelikelytointroducenoveltechnicalterms—aleadingindicatoroftransformativetechnologies(

Kalyani,

2024)

.Third,IfindthatAIchangesthenatureofproducts:itbooststheshareofprototypesthatrepresentnewproductlinesratherthanimprovementstoexistingones,engenderingashifttowardmoreradicalinnovation(

Acemoglu

etal.,

2022a)

.

Next,Iturntothetechnology’sdistributionaleffects,showingthatitdisproportionatelybenefitshigh-abilityscientists.Iconstructameasureofinitialproductivitybasedondiscoveriesinthepre-treatmentperiod.Toaccountforthepossibilitythatcertaincompoundsareinherentlyeasiertodiscover,Icontrolformaterialtypeandapplication.Estimatingseparatetreatmenteffectsforeachproductivityquantile,Idocumentstrikinglydisparateimpactsacrosstheabilitydistribution.Whilethebottomthirdofresearchersseeminimalgains,theoutputoftop-decilescientistsincreasesby81%.Consequently,90:10performanceinequalitymorethandoubles.ThissuggeststhatAIandhumanexpertisearecomplementsintheinnovationproductionfunction.

Thesecondpartofthepaperinvestigatesthemechanismsbehindtheseresults.Combiningrichtextdataonscientistactivitieswithalargelanguagemodeltocategorizethemintoresearchtasks,IshowthatAIdramaticallychangesthediscoveryprocess.Thetoolautomatesamajorityof“ideageneration”tasks,reallocatingscientiststothenewtaskofevaluatingmodel-suggestedcandidatecompounds.IntheabsenceofAI,researchersdevotenearlyhalftheirtimetoconceptualizingpotentialmaterials.Thisfallstolessthan16%afterthetool’sintroduction.Meanwhile,timespentassessingcandidatematerialsincreasesby74%.AIthereforehascountervailingeffects:whileitreplaceslaborinthespecificactivityofdesigningcompounds,itaugmentslaborinthebroaderdiscoveryprocessduetoitscomplementaritywithevaluationtasks.

Next,Ishowthatscientists’differentialskillinjudgingAI-generatedcandidatecompoundsexplainsthetool’sheterogeneousimpact.Icollectdataonthematerialsresearcherstestandthe

3

outcomesoftheseexperiments.TopscientistsleveragetheirexpertisetoidentifypromisingAIsuggestions,enablingthemtoinvestigatethemostviablecandidatesfirst.Incontrast,otherswastesignificantresourcesinvestigatingfalsepositives.Indeed,asignificantminorityofresearchersordertheirtestsnobetterthanrandomchance,seeinglittlebenefitfromthetool.Evaluationabilityispositivelycorrelatedwithinitialproductivity,explainingthewideninginequalityinscientists’performance.Theseresultsdemonstratethegrowingimportanceofanewresearchskill—assessingmodelpredictions—thatcomplementsAItechnologies.Ithereforeprovideevidencefortheargumentin

Agrawaletal.

(2018)thatimprovementsinmachinepredictionmakehuman

judgmentanddecision-makingmorevaluable.

Tounderstandthesourceoftheselargedifferencesinjudgment,Iconductasurveyofthelab’sresearchers.Theresponsesrevealthecentralroleofdomainknowledge.Scientistsskilledinevaluationcredittheirtrainingandexperiencewithsimilarmaterialsaskeytotheirassessmentprocess.Meanwhile,thosewhostruggletojudgeAI-suggestedcompoundsreportthattheirbackgroundofferslittleassistance.Supportingthisexplanation,researchersinthetopquartileofevaluationabilityare3.4timesmorelikelytohavepublishedanacademicarticleontheirmaterialoffocus.Whilesomepositthatbigdataandmachinelearningwillrenderdomainknowledgeobsolete(

Anderson,

2008;

Gennatasetal.,

2020

),theseresultsshowthatonlyscientistswithsufficientexpertisecanharnessthepowerofAI.

Icombinemyestimateswithasimplemodeltoillustratehoworganizationaladaptationcanamplifythetool’simpact.AIaltersthereturnstospecificskills,increasingthevalueofjudgmentwhilediminishingtheimportanceofideageneration.Therefore,adjustingemploymentpracticestoprioritizescientistswithstrongjudgmentimpliessignificantproductivitygains.Inthefinalmonthofmysample—excludedfromtheprimaryanalysis—thelabfired3%ofitsresearchers.Consistentwiththetheory,83%ofthesescientistswereinthebottomquartileofjudgment.Thelabmorethanoffsetthesedeparturesthroughincreasedhiring,expandingitsworkforceonnet.Duetothischangeinthecompositionofresearchers,myestimatesmayunderstateAI’slonger-runimpact.

Theconsequencesofnewtechnologiesextendbeyondproductivity.Theycanprofoundlyaffectworkerwellbeingandtheexpertiseneededtosucceedonthejob(

NazarenoandSchiff,

2021;

Soffia

etal.,

2024)

.Theseconsiderationsareparticularlysalientinthecontextofinnovation,astheymediateAI’simpactonwhobecomesascientist,thefieldstheyenter,andskillstheyinvestin.Thefinalpartofthepaperexploresthesequestionsusingthesurvey.

Researchersexperiencea44%reductioninsatisfactionwiththecontentoftheirwork.Thiseffectisfairlyuniformacrossscientists,showingthateventhe“winners”fromAIfacecosts.Respondentsciteskillunderutilizationandreducedcreativityastheirtopconcerns,highlightingthedifficulty

4

ofadaptingtorapidtechnologicalprogress.Moreover,theseresultschallengetheviewthatAIwillprimarilyautomatetedioustasks,allowinghumanstofocusonmorerewardingactivities.Whileenjoymentfromimprovedproductivitypartiallyoffsetsthisnegativeeffect,especiallyforhigh-abilityscientists,82%ofresearchersseeanoveralldeclineinwellbeing.

Inadditiontoimpactingjobsatisfaction,workingwiththetoolchangesmaterialsscientists’viewsonartificialintelligence.BeliefintheabilityofAItoenhanceproductivitynearlydoubles.Atthesametime,concernsoverjoblossremainconstant,reflectingthecontinuedneedforhumanjudgment.However,duetothechangingresearchprocess,scientistsexpectAItoaltertheskillsneededtosucceedintheirfield.Consequently,thenumberofresearchersplanningtoreskillrisesby71%.Thesefindingsshowthathands-onexperiencewithAIcanmeaningfullyinfluenceviewsonthetechnology.Theresponsesalsorevealanimportantfact:domainexpertsdidnotanticipatetheeffectsdocumentedinthispaper.

Whilemystudyfocusesonmaterialsscience,theinsightsmayapplymoregenerallytofieldswherethediscoveryprocessrequiressearchoveravastbutwell-definedtechnologicalspace.Thischaracterizesareaswherethefoundationalprinciplesareknown,butcomplexitymakesitchallengingtoidentifyspecificinstances.Indrugdiscovery,forexample,thepropertiesofatomicbondsarewellestablished,butthelargenumberofpossiblechemicalconfigurationsmakestheproblemextremelydifficult.Deeplearningmodels—whichexcelinextractingfeaturesfromcomplexdata—havethepotentialtotransformresearchinsuchsettings(

Hassabis,

2022

).Beyondmaterialsscienceandpharmaceuticals,severaleconomicallyimportantfieldsfallintothiscategory,includingstructuralbiology(

Jumperetal.,

2021;

Abramsonetal.,

2024;

Subramaniam,

2024

),genomics(

Caudaietal.,

2021

),climatology(

Kochkovetal.,

2024

),andevencertainpartsofmathematics(

Tao,

2024;

Trinhetal.,

2024

).

RelatedLiteratureThispapercontributestofourrelatedliteratures.First,itaddstoalargebodyofevidenceontheconsequencesofnewtechnologiesforproductivity,labordemand,and

organizations(Autoretal.,

1998;

AtheyandStern,

2002;

Bresnahanetal.,

2002;

Autoretal.,

2003;

Bloometal.,

2014;

Autor,

2015;

GaricanoandRossi-Hansberg,

2015;

Webb,

2020;

Acemogluand

Restrepo,

2022;

Agrawaletal.,

2019;

Acemogluetal.,

2022b;

Koganetal.,

2023;

Autoretal.,

2024)

.Whilethesestudiesfocusontheproductionofgoodsandservices,Iconsiderabreakthroughthataugmentstheprocessofinnovationitself.Instandardmodels,technologiesthatautomateproductiontaskshavequalitativelydifferenteffectsthanthoseperformingresearchtasks,giventhecompoundingsocialreturnstoinnovation(

Aghionetal.,

2019;

JonesandSummers,

2020)

.

5

Therefore,understandingtheimplicationsofAIforR&Disofcentralimportance

.1

Indeed,myresultssuggestthatanalysesofAIthatdonotconsidereffectsonscienceandinnovation(e.g.,

Acemoglu,

2024

)maybeincomplete

.2

Second,Icontributetorecentevidenceontheproductivityeffectsof“generative”AI.Thisliteraturestudiestheapplicationoflargelanguagemodelstomid-skillwritingandcodingtasks,revealingthatLLMsboostaverageoutputandcompresstheproductivitydistribution(

Brynjolfsson

etal.,

2023;

Dell’Aquaetal.,

2023;

NoyandZhang,

2023;

Pengetal.,

2023;

Cuietal.,

2024)

.Ianalyzetheadoptionofadifferenttypeofgenerativemodel—onethatproducesnovelmaterialdesignsratherthanwords.WhileIsimilarlyobservelargegainsinperformance,AIismostusefultohigh-abilityscientistsinmysetting,increasinginequality.Thissuggeststhattheso-called“leveling-up”effectiscontext-dependentandnotageneralfeatureofAItechnologies.Moreover,sincethemodelunlocksbreakthroughsbeyondthecapabilitiesofevenhighlytrainedscientists,myfindingshelpalleviateconcernsthatAIwillcontributeonlytolow-valuetasks(

Angwin,

2024)

.

Third,Irelatetoworkonhuman-AIcollaboration,particularlytherelationshipbetweenalgo-rithmsandexpertise(

Kleinbergetal.,

2017;

Chouldechovaetal.,

2018;

Cowgill,

2018;

Chengand

Chouldechova,

2022;

Donahueetal.,

2022;

Leitãoetal.,

2022;

MullainathanandObermeyer,

2022;

Aluretal.,

2023;

Angelovaetal.,

2023;

Agarwaletal.,

2024;

Aluretal.,

2024;

Gruberetal.,

2024)

.ResearchersdisplayconsiderablevariationintheirabilitytoevaluateAI-suggestedcompounds.Discretionboostsdiscoveryratesfortwo-thirdsofscientists,wholeveragetheirdomainknowledgetoimproveuponthemodel.Thisshowsthattheseexpertsobservecertainfeaturesofthematerialsdesignproblemnotcapturedbythealgorithm.Incontrast,asignificantminorityofscientistsseelittlebenefitfromthetechnologyduetopoorjudgment.Thisdichotomyhighlightstheneedforfurtherresearchonhuman-AIcollaborationingenerativetasks.

Finally,Iaddtoanascentliteratureontheimplicationsofartificialintelligenceforscience

andinnovation(Cockburnetal.,

2019;

Agrawaletal.,

2023;

Besirogluetal.,

2023;

Ludwigand

Mullainathan,

2024;

Manningetal.,

2024;

MullainathanandRambachan,

2024)

.ThispaperprovidesthefirstcausalevidenceofAI’simpactonreal-worldR&D.IshowthatAIcanautomatethecriticalideationstepofthematerialsdiscoveryprocess,leadingtoanincreaseininventionandariseinproductinnovation.Moreover,Iquantifytheeffectofthisautomationonscientists,revealinga

1ArecentdebatecentersaroundAI’smacroeconomicimplications(

Bostrom,

2014;

BrynjolfssonandUnger,

2023;

ClancyandBesiroglu,

2023;

ErdilandBesiroglu,

2023;

RamaniandWang,

2023;

Schulman,

2023;

Aschenbrenner,

2024;

Chowetal.,

2024;

KorinekandSuh,

2024)

.WhilethesepapersagreethatAI’simpactoninnovationiscritical,alackofempiricalevidenceonthisquestionhaspreventedconsensus.

2

Acemoglu

(2024)expectsthatsignificantimpactsofAIonoverallscienceandinnovationaremorethanadecade

away.Whilemyresultsdonotdirectlyspeaktosuchaggregateeffects,theydoshowthatAItechnologiescanalreadyaccelerateR&Dinspecificsettings.

6

dramaticreallocationacrossresearchtasks.Consequently,Iestablishamicro-foundationforthefindingthatinvestmentsinAItechnologiespredictsubsequentfirmgrowth(

Babinaetal.,

2024

).

OrganizationThepaperisstructuredasfollows.Section

2

providesinformationonthesetting,detailingthematerialsscienceR&DprocessandtheAItechnology.Section

3

describesthedata,measurementstrategy,andresearchdesign.Section

4

presentsthemainresultsondiscovery,patenting,andproductinnovation.Section

5

analyzesheterogeneityintheeffectofAIacrossscientistsandSection

6

studiesthedynamicsofthehuman-AIcollaboration.Section

7

exploresthetool’simpactonscientists’jobsatisfactionandbeliefsaboutAI.Section

8

concludes.

2SettingandBackground

2.1MaterialsScienceR&DLab

MysettingistheR&DlabofalargeU.S.firm.Thelabspecializesinmaterialsscience,afieldthatintegratesinsightsfromphysics,chemistry,andengineeringtocreatenovelsubstancesandincorporatethemintoproducts.Oftendeemedthe“unsunghero”oftechnologicalprogress,advancesinmaterialsscienceunderpinmanysignificantbreakthroughs(

Diamandis,

2020

).Thepurificationofsiliconinthe1950senabledthedevelopmentofintegratedcircuits,layingthefoundationformoderncomputing.Graphene—acrystallinecarbonlatticecreatedin2004—hastransformednumerousproductsrangingfrombatteriestodesalinationfilters.Morerecently,novelphotovoltaicstructureshaveenhancedsolarpanelefficiency,drivingthesteepdeclineinrenewableenergycosts.Andinmedicine,biocompatiblecompoundsallowimplantstomergeseamlesslywithhumantissue,improvingdrugdeliverysystems.

Moskowitz

(2022)estimatesthatmorethan

twothirdsofnewtechnologiesrelyoninnovativematerials,highlightingthefield’seconomicimportance.

Thelabinmystudyfocusesonapplicationsofmaterialsscienceinhealthcare,optics,andindustrialmanufacturing,producingawiderangeofpatentsandproductinnovations.Tocreatethesetechnologies,thefirmemploysthousandsofhighlytrainedscientistswithadvanceddegreesinchemicalengineering,physics,andmaterialsscience.Researchersareorganizedinteams,specializinginmaterial-applicationpairsrelevanttotheirexpertise.Thesegroupsalsoincludesupportstaffsuchastechniciansandadministrators.

Figure

1

summarizestheR&Dpipeline.First,scientistsdefineasetoftargetpropertiesandgenerateideasfornewcompoundspredictedtosatisfytheserequirements.BeforetheintroductionofAI,researchersemployedacombinationofdomainknowledgeanditerativecomputational

7

FalsePositives

procedurestocreatepreliminarydesigns.Giventhedifficultyofpredictingmaterialcharacteristics,thisprocessistime-intensiveandinvolvesmanyfalsepositives(

Reiseretal.,

2022

).

NewProduct

Development

Finalized

Market

Prototype

andScaling

Product

Release

Prioritization

ViableMaterial

Idea

Generation

Testing

PatentFiling

DevelopmentandScaling

ImprovedProductPrototype

CandidateMaterials

FinalizedProduct

MarketRelease

Invention

Commercialization

Figure1.MaterialsScienceR&DPipeline

Notes:ThisfiguresummarizesthematerialsscienceR&Dpipeline.First,scientistsgeneratenewcompounddesigns.Next,theyevaluatethesecandidatesandtestthemostpromising.Afterscientistsdiscoveraviablematerial,theytypicallyfileforapatentandincorporateitintoproductprototypes.Thesecouldrepresententirelynewproductsorimprovementstoexistinglines.Finally,prototypesaredeveloped,mass-produced,andreleasedtomarket.Myanalysisfocusesonthe“invention”steps,depictedinblue.

Next,scientistsevaluatecandidatecompoundsanddecidewhichtotest.Experimentsarecostly,soprioritizationiskeytoefficiency.

3

Toidentifythemostpromisingcandidates,researchersjudgecompounddesignsusingsimulationsandpriorexperiencewithsimilarmaterials.Oncethelabselectsacandidatefortesting,theyadvanceitthroughaseriesofexperiments.First,theyattempttosynthesizethematerial,rulingoutalargeshareofcandidatesthatdonotyieldstablecompounds.Next,theyconductteststoassessitspropertiesatboththeatomicandmacroscales.Finally,theysubjectittoreal-worldconditionssuchasheat,pressure,orhumaninteraction.

Afterdiscoveringamaterial,scientistsoftenfileforapatent.Thiscanpertaintoasinglecompound,acombinationofcompounds,oranewtechnologythatusesthem.Patentsrequirethreecriteria:novelty,utility,andnon-obviousness.Asaresult,theymarkthestageofresearchwhereascientificdiscoverytransformsintoausefulinvention.Patentapplicationstypicallytaketwoyearsforapproval,somyanalysisfocusesonfilings.Historically,morethan80%ofthelab’sapplicationshavebeenapproved.

Finally,thelabintegratesnewmaterialsintoproducts.Thesecouldrepresententirelynewproductlinesorimprovementstoexistingones.Ineithercase,researchersfirstdevelopaprototypethatisthenadvancedthroughdevelopmentandmassproduction.The“commercializationlag”

3Forexample,themachinesusedtogrowcrystalstructuresareextremelycapital-intensiveandrequirecostlyinputsforeachuse.

8

betweenthecreationofaworkingprototypeanditsmarketreleasecanbesubstantial,rangingfromyearstodecades.Consequently,myanalysisfocusesontheprototypingstage.Thispaper’sresultsshouldthereforebeunderstoodasreflectingthe“invention”componentofR&D(

Budish

etal.,

2015)

.

2.2DeepLearningforMaterialsDiscovery

Materialsdiscoveryischallengingduetoitscomplexity.Thespaceofplausiblechemicalconfig-urationsisvast,requiringscientiststoexploremanypotentialcompounds.Moreover,whilethepropertiesofatomicbondsarewellknown,itisdifficulttopredicthowtheywillaggregateintolarge-scalecharacteristics.Deeplearningmodels—whichexcelinextractingfeaturesfromcomplexdata—havethepotentialtoovercomethesechallenges(

Hassabis,

2022

).

Recentyearshaveseenanexplosionoflarge,standardizeddatabasescompilingthestructureandcharacteristicsofknowncompounds.Combinedwithalgorithmicprogressandincreasedcompute,thishasgreatlyimprovedtheperformanceofdeeplearninginmaterialsscience(

Reiser

etal.,

2022)

.Asaresult,thefieldhasshownrapidlygrowinginterestinthesetechniques.

CumulativePercentIncrease

2,000

1,000

0

MaterialsPublic

Scienceations

Materials

Science

PhDS

yllabi

20152017201920212023

Figure2.TheRiseofDeepLearninginMaterialsScience

Notes:Thisfigureshowstheriseofdeeplearninginmaterialsscience.Thebluecirclesrepresentthecumulativepercentincreaseinmaterialssciencepublicationsmentioningdeeplearningsince2015,basedondatafromWebofScience.ThepurplediamondsplotthecumulativepercentincreaseinmaterialssciencePhDsyllabicoveringdeeplearning,sourcedfromOpenSyllabus.

Figure

2

illustratestheriseofdeeplearninginmaterialsscience.Since2015,thenumberofmaterialssciencepublicationsreferencingdeeplearninghasgrownmorethan20-fold.Thisfollowsadecadeoffoundationalprogressinthecomputerscienceliterature

.4

Atthesametime,deep

4Seminalpapersinclude

Goodfellowetal.

(2014);

MirzaandOsindero

(2014);

Radfordetal.

(2016);

Arjovskyetal.

(2017);

Karrasetal.

(2018)

.

9

learninghasbecomeintegratedintomaterialssciencecurricula,evidencedbyasharpincreaseinPhDsyllabicoveringthesemethods.

2.3AITool

Thelab’sAItechnologyisasetofgraphneuralnetworks(GNNs)trainedonthestructureandpropertiesofexistingmaterials.Introducedby

Scarsellietal.

(2009),GNNsexte