新材料英语 课件 Unit 6 The AI-powered materials revolution

新材料英语CollegeEnglishfor

NewAgriculturalSciences6UnitTheAI-poweredmaterialsrevolution新材料英语EnglishforAdvancedMaterialsScienceAfterstudyingthisunit,youwillbeableto:identifythebasicprinciplesofmachinelearninganditsapplicationsinmaterialsscienceandengineering;explainhowAIisacceleratingthedevelopmentofnewmaterialsandrecognizeitslimitations;useevidenceeffectivelytomakeyouracademicwritingmoreengagingandconvincing;Learning

objectives新材料英语EnglishforAdvancedMaterialsScienceengageinaninternationalpaneldiscussionontheviabilityofAI-assistedmaterialsdiscovery.Learning

objectives新材料英语EnglishforAdvancedMaterialsScienceCONTENTSTellingChina’sstoriesUnlockingthetopicViewingthroughthelensExploringthefrontier新材料英语EnglishforAdvancedMaterialsScienceSettingthesceneImaginedesigninganewmaterialnotinalab,butwithacomputerthatlearnsfrommillionsofexistingexamples.ThisisthepromiseofAI,wheremachinelearning–acoreAItechnology–istransformingmaterialsscienceandengineering.Inthisunit,youwillexplorehowAI,specificallycomputermodelingandmachinelearning,isacceleratingresearchandinnovationbyanalyzingdata,predictingmaterialproperties,andautomatingtasks.新材料英语EnglishforAdvancedMaterialsScienceSettingthesceneYourmissioninthisunitistoparticipateinaninternationalpaneldiscussiononAI-drivenbreakthroughsinspacematerialsscience.ReadytoseehowAIishelpingbuildthefutureofmaterials,oneatomatatime?Let’sdivein.新材料英语EnglishforAdvancedMaterialsScienceScanthecodeandcompletethe

technicalvocabularyexerciseonUcampus.compilevt.编译opensourcesoftwaren.开源软件brittlenessn.脆性nuclearreactorn.核反应堆

absorbern.吸热体naturallanguageprocessing(NLP)n.

自然语言处理Wordbank

HowcanyouemployAItechnologytoempowermaterialsscience?Watchthevideoclipandcompletetheoutlinewithwhatyouhear.(Withsubtitles)(Withoutsubtitles)WhenmaterialssciencemeetsmachinelearningReferenceanswers(1)_____________(2)_____________(3)_______________________________(4)________________(5)________________(6)________________(7)________________datasetsdecisionsthestudyofmaterials’propertiesevolvingrapidlynon-existentdatacollectiontime-consumingScanthecodeformorecomprehensionexercisesonUcampus.

ScriptsWhenmaterialssciencemeetsmachinelearningMachinelearning(ML)involvesprogrammingcomputersthatlearnastheygo.Apartofartificialintelligence,machinelearninglooksforpatternsindatasetsandusesthemtomakedecisionsorpredictions.Prof.DaneMorganandDr.RyanJacobsfromtheUniversityofWisconsin-MadisonintheU.S.,areinvestigatingtheopportunitiesandchallengesposedbymachinelearningforthefieldofmaterialsscienceandengineering,orMS&E.MS&Einvolvesthestudyofmaterials’propertiesandthecreationofnewmaterialsforspecificpurposes.MachinelearninginMS&Eisinitsearlystagesbutevolvingrapidly.ResearchersinMS&ElikeDaneandRyannowhaveaccesstomassivedigitaldatabasesthatcompilethestructureandpropertiesofcountlessmaterials.Withpowerfulcomputingandmodelingcapabilities,it’sbecomingeasiertoexploreandpredictcomplexrelationshipsbetweenmaterials’structure,composition,andtheirproperties.Mostofthiscomputingpowercomesfromopen-sourcesoftware,withthefundamentalmachinelearningalgorithmsavailabletoeveryoneforfree.Machinelearningisabletofindpatternsinhugedatasetsthatwouldbeimpossibleforhumanstosee.Itcanextracttheinformationfromdatatoformlinksbetweenamaterial’scomposition,theelementsitcontains,andstructure,thepositionofatoms,toparticularpropertiesorfunctions.Forexample,thebrittlenessofsteelisaffectedbythetypeanddistributionofatomswithinitsstructure.Machinelearningcanbeusedtoassessthis,whichisextremelyusefulforselectingatypeofsteelforaspecificpurpose,withinanuclearreactor,forinstance.Aswellastestingexistingmaterials,MLcanbeusedtopredictstructure-property-performancerelationshipsincurrentlynon-existentmaterials.Molecularscalesimulationsareusedtounderstandpredictedpropertiesasakeyofthediscoveryanddesignprocess.Forexample,machinelearningisbeingusedtodiscoverwhichmaterialscanbeusedtomakesolarpanelsasefficientlyaspossible,bypredictinghowstructuralchangestoasolarcellabsorbermaterialaffectitsabilitytoconvertsunlightintoelectricity.MachinelearningisalsoaneffectivemeansofdatacollectionthroughnaturallanguageprocessingorNLP.Manuallysearchingresearchpapersforrelevantinformationismassivelytime-consuming,butNLPmethodscanextractinformationfarmorerapidly.AlotofMS&Edataisintheformofimages.Researchersareofteninterestedinfindingindividualobjectsinanimage,suchasmissingatomsordefects.Computervisionmachinelearningalgorithmsencodethecomplexrelationshipspresentinimages,likecontrastchangesorthepresenceofedgesaroundanobject,enablingthealgorithmtolearnwhataparticularmicrostructureordefectmaylooklike.Likeallscientificfields,MS&Eincludestime-consumingtaskssuchasmixinghundredsofcombinationsofdifferentelementstotesttheirproperties.Increasingly,machinelearningisperformingthesetasks,savingresearchersalotoftime,effort,andmoney.DaneandRyanbelievethatinthefuture,eachstepofthescientificprocess,fromhypothesisgenerationtorunningexperimentstoanalyzingresults,willbeaidedbymachinelearning.Thishuman-machinerelationshipwillenableresearchtoprogressatanever-increasingrate.Whatwouldyouinvestigateinthefieldofmaterialsscienceandengineering?Howwillyouusemachinelearninginthefuture?Imagineyouareleadingamaterialslab.Workingroupsanddiscussthequestions.WhattypesofinnovationsinmaterialsscienceandengineeringdoyouthinkcouldbeparticularlyenhancedbyAI?InwhatsituationsmightAIbelesseffective?Beyondmachinelearning,whatothercurrentAItechnologiesormethodswouldyouintroducetoacceleratetheseinnovations?Describeonespecifictechnologyormethodandexplainhowitwouldhelpinthefield.AIcansignificantlydriveinnovationinareassuchashigh-throughputmaterialdiscovery,whereitpredictsstructure–propertyrelationshipsfornewmaterialstoshortenresearchanddevelopmentcycles,andmicrostructuredefectdetection,wherecomputervisionenablespreciseandrapidanalysis.Italsobooststhedevelopmentofcustomizedmaterials(e.g.,optimizedsolarcellabsorbersornuclearreactorsteels).ReferenceanswersHowever,AIislesseffectiveinexploringnovelmaterialsystemswithnoexistingdatasets,asmachinelearningreliesonlargeamountsofdata,andinexperimentsunderextremeconditions,wherereal-worldfactorsaretoocomplextosimulateaccuratelyandhumanexpertiseisrequiredforvalidation.IwouldintroduceGenerativeAdversarialNetworks(GANs).GANsconsistofageneratorandadiscriminatorthatcompetetoproducerealisticsyntheticdata.Inmaterialsscienceandengineering,GANscangeneratevirtualdatasetsofnovelmaterialstructuresandproperties,especiallywhenreal-worldexperimentaldataarescarce(e.g.,foremergingnanomaterials),therebyhelpingaddressthedatascarcitybottleneckformachinelearningmodels.ReferenceanswersAdditionally,GANscansimulaterarematerialdefectsormaterialresponsesunderextremeconditions,enablingresearcherstoevaluatematerialperformancewithoutconductingcostlyandtime-consumingreal-worldexperiments.1

Insteadofcontinuingtodevelopnewmaterialsintheold-fashionedway–stumblingacrossthembyluckandthenpainstakinglymeasuringtheirpropertiesinthelaboratory,researchersarenowusingcomputermodelingandmachinelearningtechniquestogeneratelibrariesofcandidatematerialsbythetensofthousands.Evendatafromfailedexperimentscanprovideusefulinput.CanAIcreatethenextwondermaterial?Manyofthesecandidatesarecompletelyhypothetical,butengineersarealreadybeginningtoshortlistthoseworthsynthesizingandtestingforspecificapplicationsbysearchingthroughtheirpredictedproperties–forexample,howwelltheywillworkasaconductororaninsulator.2However,aseventheproponentsarequicktopointout,thejourneyfromcomputerpredictionstoreal-worldtechnologiesisnotaneasyone.Theexistingdatabasesarefarfromincludingallknownmaterials,letaloneallpossibleones.Thedata-drivendiscoveryworkswellforsomematerials,butnotforothers.Evenafteraninterestingmaterialissingledoutonacomputer,synthesizingitinalaboratorycantakeyears.Still,researchersinthisfieldareconfidentthatthereisatroveofcompoundswaitingtobediscovered,whichcouldkick-startinnovationsinelectronics,energy,robotics,healthcare,andtransportation.3Inlate2011,GerbrandCederandKristinPerssonrelaunchedtheirMaterialsGenomeProjectastheMaterialsProject.Thefollowingyear,StefanoCurtarolopostedhisowndatabase,calledAFLOW,basedonthesoftwarehehaddevelopedatDukeUniversity.In2013,ChrisWolverton,amaterialsresearcheratNorthwesternUniversity,launchedtheOpenQuantumMaterialsDatabase(OQMD).Allthreeofthesedatabasesshareasubstantialcollectionofknownmaterialstakenfromawidelyusedexperimentallibrary,theInorganicCrystalStructureDatabase.Thesematerialsaresolidsthathavebeencreatedatleastonceinalaboratoryanddescribedinapaper,butwhoseelectronicormagneticpropertiesmayneverhavebeenfullytested.Theyarethestartingpointfromwhichnewmaterialscanbederived.4Wherethethreedatabasesdifferisinthehypotheticalmaterialstheyinclude.TheMaterialsProjecthasrelativelyfew,includingabout15,000computedstructuresderivedfromCeder’sandPersson’sresearchonlithiumbatteries.“Weonlyincludetheminthedatabaseifwe’reconfidentthecalculationsareaccurate,andifthereisareasonablechancethattheycanbemade,”saysPersson.Another130,000orsoentriesarestructurespredictedbytheNanoporousMaterialsGenomeCenterattheUniversityofMinnesota,focusingonzeolitesandmetal-organicframeworks.AFLOWisthelargestdatabase,featuringnearly4milliondifferentmaterialsandabout800millioncalculatedpropertiestodate.Itincludesnumeroushypotheticalmaterials,manyofwhichwouldexistforonlyafractionofasecondintherealworld.Wolverton’sOQMDincludeshundredsofthousandsofhypotheticalmaterials,calculatedbytakingalistofcrystalstructurescommonlyobservedinnatureand“decorating”themwithelementschosenfromalmosteverypartoftheperiodictable.5Scientistsarealreadydoingsciencewiththedata–andsoareusersfromothergroups.TheMaterialsProjecthasidentifiedseveralpromisingcathodesthatmayworkbetterthanexistingonesinlithiumbatteries,aswellasmetaloxidesthatcouldimprovetheefficiencywithwhichsolarcellscapturesunlightandturnitintoenergy.Inrecentyears,thefieldhasgraduallygrowntoharnessthepotentialofmachinelearning,whichismassivelyincreasingourunderstandingofmaterials’propertiesandhowwecandiscoveranddesignnewmaterials.Forexample,scientistsfromMITandToyotaResearchInstitutehavejoinedforcestopioneergenerativeAI-drivendesignofnovelpolymerelectrolytes.ThisbreakthroughnotonlydemonstratesAI’sformidablecapabilitiesinmaterialsdesign,butalsobreathesnewvitalityintosolid-stateelectrolytedevelopment.

6However,therearestillmanyhurdlestoovercomebeforematerialsgenomicscanliveuptoitspromises.Oneofthelargestisthatcomputersimulationsstillgivefewcluesonhowaninterestingmaterialcanbemadeinalab–letalonemass-produced.“Wecomeupwithinterestingideasfornewcompoundsallthetime,”saysCeder.“Sometimesittakestwoweekstomakeit.Othertimes,westillcan’tmakeitaftersixmonths,andwedon’tknowwhetherwehaven’tdonetherightthingoritjustcan’tbemade.”7Anotherlimitationisthatmaterialsgenomicshashithertobeenappliedalmostexclusivelytowhatengineerscallfunctionalmaterials–compoundsthatcanperformspecifictaskssuchasabsorbinglightinasolarcellorlettingelectricalcurrentpassinatransistor.However,thetechniquedoesnotlenditselfwelltothestudyofstructuralmaterials,suchassteel,whichareneededtobuildaircraftwings,bridges,orengines.Thisisbecausemechanicalproperties,suchasspringinessandhardness,dependonhowamaterialisprocessed–somethingthatquantum–mechanicalcodesbythemselvescannotdescribe.Fortunately,theadventofmachinelearninginmaterialssciencecurrentlyoffersnewwaystouncoverpreviouslyoverlookedstructure-composition-property-processingcorrelations,whichdemonstratethepowerofdata-drivenmethodologiesandmighthopefullyshednewlightonmaterialsscience.8Intheshortterm,moredataexchangewithexperimentscangivecomputationsarealitycheckandhelptorefinethem.Inthelongrun,therearestillmanyopenquestionsfortheuseofAIinmaterialsscience,suchashowtohandlesparseandnoisydataandhowtopreventunwantedelementalintrusionfromsynthesisorrecycling.However,whenitcomestodesigningcompositionallycomplexalloys,AIwillplayamoreimportantroleinthenearfuture,especiallywiththedevelopmentofalgorithms,theavailabilityofhigh-qualitymaterialsdatasets,andthegrowthofhigh-performancecomputingresources.JustlikewhatisstatedinMoore’sLaw,ascomputationalpowercontinuestoincrease,techniquesthatarestilloutofreachforcurrentcomputersmaysoonbecomeviable.Insteadofcontinuingtodevelopnewmaterialsintheold-fashionedway–stumblingacrossthembyluckandthenpainstakinglymeasuringtheirpropertiesinthelaboratory,researchersarenowusingcomputermodellingandmachine-learningtechniquestogeneratelibrariesofcandidatematerialsbythetensofthousands.[Meaning]:Researchersnolongerrelyonthetraditionalmethodofdiscoveringnewmaterialsbychanceandthentestingtheirpropertiesinthelaboratory.Instead,theynowusecomputermodellingandmachinelearningtoquicklycreatedatabasescontainingtensofthousandsofpotentialmaterials.[Knowledgefocus]Thesentenceadoptsthestructure“InsteadofdoingA,researchersaredoingB”toemphasizetheparadigmshiftinresearchmethods.Theen-dashintroducesaparentheticalexplanationthatclarifieswhatthe“old-fashionedway”involves(i.e.,stumblingacrossmaterialsbyluckandpainstakinglymeasuringtheirpropertiesinthelaboratory),therebymakingthecontrastbetweentraditionalandmodernapproachesmoreexplicit.Wolverton’sOQMDincludeshundredsofthousandsofhypotheticalmaterials,calculatedbytakingalistofcrystalstructurescommonlyobservedinnatureand“decorating”themwithelementschosenfromalmosteverypartoftheperiodictable.[Meaning]：Wolverton’sdatabasecontainshundredsofthousandsoftheoreticalmaterials.Researchersgeneratethesematerialsbytakingcrystalstructurescommonlyfoundinnatureandsystematicallysubstitutingdifferentchemicalelementsintothosestructures.[Knowledgefocus]Theterm“decorating”hereservesasavividmetaphor.Incrystallography,itreferstousingaknowncrystalstructure(suchasaperovskiteorrock-saltstructure,showninFigure1)asaframeworkortemplate.Bysubstitutingdifferentchemicalelementsintotheatomicpositionswithinthisframework,researcherscanpredicttheoreticallypossiblenewcompounds.Fig.1Schematicdiagramoftheprocedureof“decorating”aperovskitestructureABX3

using

Mg,Si,

andO.Thisisbecausemechanicalproperties,suchasspringinessandhardness,dependonhowamaterialisprocessed–somethingthatquantum-mechanicalcodesbythemselvescannotdescribe.[Meaning]：Thislimitationexistsbecausemechanicalpropertiessuchasspringinessandhardnessdependstronglyonhowamaterialisprocessed.Theseeffectsarisefrommanufacturingprocessandthereforecannotbefullycapturedbyquantum-mechanicalcalculationsalone.[Knowledgefocus]:Quantum-mechanicalcodesexcelatdescribingatomicbonding,butthisrepresentsonlyoneaspectofamaterial’sproperties.Theperformanceofstructuralmaterialssuchassteelislargelydeterminedbytheirmicrostructuralfeaturessuchasgrainsize,defects,andgrainboundaries,whichdevelopduringmacroscopicprocessing,likeforgingandheattreatment.Thelengthandtimescalesassociatedwiththesemicrostructuralfeaturesarefarbeyondwhatcanbehandledbyquantum-mechanicalcalculationsalone.JustlikewhatisstatedinMoore’slaw,ascomputationalpowercontinuestoincrease,techniquesthatarestilloutofreachforcurrentcomputersmaysoonbecomeviable.[Meaning]:AspredictedbyMoore’sLaw,advancesincomputationalpowerarelikelytoenablethepracticalapplicationoftechniquesthattoday’scomputersarenotyetcapableofsupporting.[Words&Phrases]Theexpression“outofreach”means“notabletobeachieved.”Inthepassage,“techniquesthatarestilloutofreachforcurrentcomputers”suggeststhatthesetechniquesarebeyondthecapabilitiesoftoday’scomputationalmethodsbutmaybecomeachievableascomputationalpowerincreases.e.g.Developingmaterialsthatcombinehighstrengthwithlowweightusedtobeoutofreachformanyengineers,butadvancesinnanomaterialshavemadesuchdesignspossible.人工智能能创造出下一种神奇材料吗？1

研究人员不再沿用老式方法开发新材料，也就是不再靠运气偶然发现，然后在实验室里费尽心力地测量其各种属性，他们如今正利用计算机建模和机器学习技术生成包含数以万计候选材料的信息库。即使是失败实验所产生的数据，也能提供有用的输入信息。许多候选材料完全是假设出来的，但工程师们已经开始通过搜索其预测性能进行筛选，例如它们作为导体或绝缘体的表现如何，从而将那些值得合成并针对特定应用进行测试的材料列入候选名单。2然而，正如支持者们很快指出的那样，从计算机预测到现实世界的技术应用，这段历程并不容易。现有的数据库远未包含所有已知材料，更不用说所有可能存在的材料。数据驱动的发现方法对某些材料有效，但对另一些材料却并不适用。即使在计算机上筛选出一种有趣的材料，在实验室中合成它可能需要数年时间。尽管如此，该领域的研究人员仍相信，还有大量的化合物等待被发现，而这些发现将推动电子、能源、机器人、医疗保健和交通运输领域的创新。32011年底格布兰德·西达尔和克里斯汀·佩森重新启动他们的“材料基因组计划”，并更名为“材料计划”。次年，斯特凡诺·库尔塔罗洛发布了他自己的数据库AFLOW，该数据库是基于他在杜克大学开发的软件建立的。2013年，（美国）西北大学的材料研究员克里斯·沃尔弗顿推出了开放量子材料数据库OQMD。这三个数据库共享一个规模庞大的已知材料库，这些材料取自一个广泛使用的实验数据库——无机晶体结构数据库。这些都是至少在实验室中制备过一次并在论文中已有描述的固体材料，但其电子或磁性属性可能从未经过全面测试。它们是衍生新材料的起点。4这三个数据库的区别在于它们收录的假设材料不同。“材料计划”收录的假设材料相对较少，其中包含约15,000种从西达尔和佩森的锂电池研究中得到的计算结构。“只有当我们确信计算是准确的，并且这些材料有较大可能被制备出来时，我们才会将它们收录到数据库中，”佩森说道。另有约13万个条目是由明尼苏达大学纳米多孔材料基因组中心预测的结构，主要关注沸石和金属有机框架。AFLOW是规模最大的数据库，迄今已收录将近400万种不同的材料以及约8亿项计算得到的材料属性。它包含大量假设材料，其中许多在现实世界中可能只存在一瞬之间。沃尔弗顿推出的开放量子材料数据库包含数十万种假设材料，这些材料是通过选取自然界中常见的晶体结构，并用元素周期表中各个区域的元素对其进行“装饰”计算而得到的。5科学家们已经在使用这些数据进行科学研究——其他团队的用户也是如此。“材料计划”已经确定了几种有前景的锂电池正极材料（电化学上对应阴极材料），它们在锂电池中的性能可能优于现有材料；同时还确定了一些金属氧化物，能提高太阳能电池捕获阳光并将其转化为能量的效率。近年来，该领域逐渐开始利用机器学习的潜力，这极大地加深了我们对材料属性以及如何发现和设计新材料的理解。例如，麻省理工学院和丰田研究所的科学家联手，开创了由生成式人工智能驱动的新型聚合物电解质设计。这一突破不仅展示了人工智能在材料设计方面的强大能力，也为固态电解质的开发注入了新的活力。6然而，在材料基因组学能够兑现其承诺之前，仍有许多障碍需要克服。最大的障碍之一是计算机模拟对于如何在实验室中制备一种有趣的材料几乎没有提供任何线索——更不用说大规模生产了。“我们总能提出关于新化合物的有趣设想，”西达尔说。“有时候，两周就能把它做出来。而有时候，六个月过去了我们仍然无法制备出来，而且我们不知道究竟是做的方法不对，还是这种材料就是无法被制备出来。”7另一个局限性在于，材料基因组学迄今为止几乎只被应用于工程师所说的“功能材料”——即能够执行特定任务的化合物，如在太阳能电池中吸收光能或在晶体管中让电流通过。然而，该技术并不适合研究结构材料，例如制造飞机机翼、桥梁或发动机所需的钢材。这是因为材料的机械属性，如弹性和硬度，取决于材料是如何被加工的——这是个单靠量子力学代码本身无法描述的过程。幸运的是，机器学习在材料科学中的兴起，为揭示以前常被忽视的“结构—组成—属性—加工”之间的相关性提供了新的途径，这展示了数据驱动方法的力量，并有望为材料科学带来新的启示。8从短期来看，加强计算与实验之间的数据共享，能够对计算进行现实检验，并帮助完善计算。从长远来看，人工智能在材料科学中的应用仍有许多悬而未决的问题，比如如何处理稀疏和嘈杂的数据，以及如何防止合成或回收过程中不需要的元素混入。然而，当涉及到设计成分复杂的合金时，人工智能将在不久的未来发挥更重要的作用，特别是随着算法的发展、高质量材料数据集的可用性以及高性能计算资源的增加。正如摩尔定律所描述的那样，随着计算能力的不断提升，一些目前仍超出计算机能力范围的技术，未来可能很快就会变得切实可行。Readthepassageandcompletethesummarywithinformationfromthepassage.ReadingandsynthesizingThefieldofmaterialsscienceisundergoingatransitionfromtraditionaltrial-and-errorapproachestodata-drivendiscoveryusing1)______________________andmachinelearningtechniques.Thesetechniquesallowresearcherstogeneratelargelibrariesof2)______________________.computermodelingcandidatematerialsAlthoughthejourneyfromcomputerpredictionsto3)______________________remainschallenging,researchersbelievemanynewcompoundswillbediscoveredandinspireinnovationsinfieldssuchaselectronics,energy,andhealthcare.Threemajordatabases–theMaterialsProject,AFLOW,andOQMD–shareagreatdealofknownmaterials,yetdiffergreatlyinthenumberandtypeof4)______________________theyinclude.Theseresourceshaveenableddiscoveriessuchaspromisingcathodesaswellas5)______________________,real-worldapplicationshypotheticalmaterialsmetaloxidesandmachinelearningisfurtherexpandingourunderstandingof6)______________________andhowwediscovernewmaterials.However,obstaclespersist,includingdifficultiesinsynthesizingnewcompoundsinalabandlimitedapplicabilityto7)___________________,yetprogresscontinues.Inthenearfuture,AIisexpectedtotakeonalargerroleindesigningcompositionallycomplexalloys.As8)______________________continuestoadvance,methodsoncebeyondreachmaysoonbecomepracticalformaterialsdiscovery.materials’propertiesstructuralmaterialscomputationalpowerScanthecodeformorecomprehensionexercisesonUcampus.completefourlanguageexercisesonUcampus,includingtechnicalvocabulary,generalvocabulary,sentencestructure,andtransla