热电材料的第一性原理高通量研究
|
摘要
热电材料是一种新型能量转换材料,在温差发电或通电制冷等领域具有广泛应用。热电优值ZT值是衡量热电材料能量转换效率的关键参数,ZT值要求热电材料具有优异的电输运性能及较低的热导率。传统第一性原理热电材料研究往往关注少量样本下的电热输运性质理解与优化,很难得到系统性的规律,也不利于新体系的设计优化。材料基因组计划力求通过大数据、高通量手段去加速材料设计与发现,具有广阔的发展前景。在热电材料研究领域,第一性原理高通量计算也将在新材料预测与性能优化等方面起到越来越重要的作用。另一方面,高通量研究也带来了新的挑战,譬如电热输运性质的高通量算法发展、大数据分析手段等等,这些方面的问题决定了高通量方法在材料应用中的效率与准确性。本文综述了热电材料中现有的电热输运性质高通量计算方法,介绍了这些方法具体的应用案例,并对高通量与热电材料结合的未来发展趋势进行了展望。
Thermoelectric materials are a kind of energy conversion materials, which are extensively used in power generation or refrigeration. The key parameter that measure the performance of thermoelectric materials is the figure of merit ZT value, which requires material excellent electrical transport performance and low thermal conductivity.Standard first principles calculations on thermoelectric materials focus on small samples of materials, which is difficult to conclude general rules and propose new candidates. The Materials Genome Initiative speeds up the discovery and design of materials based on big data and high-throughput computational methods, which is promising in novel material screening. In thermoelectrics, first principles high-throughput calculations play an increasingly important role in the predicting and designing new materials. However, there are some drawbacks in the current high-throughput efforts for thermoelectric material screening, such as the demand of efficient high-throughput algorithms for transport properties, suitable tools for analyzing big data, etc. Solving these challenges strongly determines the efficiency and accuracy of high-throughput applications in thermoelectrics. This review summarizes several high-throughput theoretical methods and cases study on electrical and thermal transport properties in thermoelectric materials, and prospects the future trend of the combination of high-throughput and thermoelectric material research.
Thermoelectric materials are a kind of energy conversion materials, which are extensively used in power generation or refrigeration. The key parameter that measure the performance of thermoelectric materials is the figure of merit ZT value, which requires material excellent electrical transport performance and low thermal conductivity.Standard first principles calculations on thermoelectric materials focus on small samples of materials, which is difficult to conclude general rules and propose new candidates. The Materials Genome Initiative speeds up the discovery and design of materials based on big data and high-throughput computational methods, which is promising in novel material screening. In thermoelectrics, first principles high-throughput calculations play an increasingly important role in the predicting and designing new materials. However, there are some drawbacks in the current high-throughput efforts for thermoelectric material screening, such as the demand of efficient high-throughput algorithms for transport properties, suitable tools for analyzing big data, etc. Solving these challenges strongly determines the efficiency and accuracy of high-throughput applications in thermoelectrics. This review summarizes several high-throughput theoretical methods and cases study on electrical and thermal transport properties in thermoelectric materials, and prospects the future trend of the combination of high-throughput and thermoelectric material research.
引文
[1]SEEBECK T J. On the magnetic polarization of metals and mineralsbytemperaturedifferences.AnnalsofPhysics,1826,82(3):253–286.
[2]PELTIER J C A. New experiments on the heat effects of electric currents. Annals of Chemistry and Physics, 1834, 56:371–386.
[3]ZHANGQ,LIAOJ,TANGY,etal. Realizingathermoelectric conversion efficiency of 12%in bismuth telluride/skutterudite segmented modules through full-parameter optimization and energyloss minimized integration. Energy&Environmental Science, 2017,10(4):956–963.
[4]BULMAN G E, SIIVOLA E, SHEN B, et al. Large external delta t andcoolingpowerdensitiesinthin-filmBi2Te3-superlatticethermoelectric cooling devices. Applied Physics Letters, 2006, 89(12):122117–1–3.
[5]SHAKOURIA,ZHANGY. On-chipsolid-statecoolingforintegratedcircuitsusingthin-filmmicrorefrigerators.IEEETransactionsonComponentsandPackagingTechnologies,2005,28(1):65–69.
[6]WANGW,JIAF,HUANGQ,etal. Anewtypeoflowpower thermoelectric micro-generator fabricated by nanowire array thermoelectricmaterial.MicroelectronicEngineering,2005,77(3/4):223–229.
[7]LI JING-FENG. Macrofabrication technology of three-dimensional microdevices and their MEMS applications. Journal of Inorganic Materials, 2002, 17(4):657–664.
[8]HAUTIEG,JAINA,CHENH,etal.Novelmixedpolyanions lithium-ion battery cathode materials predicted by high-throughput abinitiocomputations.JournalofMaterialsChemistry,2011,21(43):17147–17153.
[9]DE JONG M, CHEN W, ANGSTEN T, et al. Charting the completeelasticpropertiesofinorganiccrystallinecompounds.Sci.Data, 2015, 2:150009–1–13.
[10]TAYLOR R H, CURTAROLO S, HART G L W. Guiding the experimentaldiscoveryofmagnesiumalloys.PhysicalReviewB,2011, 84(8):084101–1–17.
[11]HAUTIERG,FISCHERC,EHRLACHERV,etal. Datamined ionicsubstitutionsforthediscoveryofnewcompounds.Inorg.Chem., 2011, 50(2):656–663.
[12]CHENW,POHLSJAN-HENDRIK,HAUTIERG,etal. Understandingthermoelectricpropertiesfromhigh-throughputcalculations:trends, insights, and comparisons with experiment. Journal of Materials Chemistry C, 2016, 4(20):4414–4426.
[13]TOHER C, PLATA J J, LEVY O, et al. High-throughput computationalscreeningofthermalconductivity,debyetemperature,and gruneisen parameter using a quasiharmonic debye model. Physical Review B, 2014, 90(17):174107–1–14.
[14]BLANCOM,FRANCISCOE,LUANAV.Gibbs:isothermalisobaricthermodynamicsofsolidsfromenergycurvesusinga quasi-harmonic debye model. Computer Physics Communications,2004, 158(1):57–72.
[15]WANGS,WANGZ,SETYAWANW,etal. Assessingthethermoelectricpropertiesofsinteredcompoundsviahigh-throughput ab-initio calculations. Physical Review X, 2011, 1(2):021012–1–8.
[16]CARRETEJ,LIW,MINGON,etal. Findingunprecedentedly low-thermal-conductivityhalf-Heuslersemiconductorsviahighthroughputmaterialsmodeling.PhysicalReviewX,2014,4(1):011019–1–9.
[17]GOLDSMIDH,DOUGLASR.Theuseofsemiconductorsin thermoelectricrefrigeration.BritishJournalofAppliedPhysics,1954, 5(11):386–390.
[18]CHASMAR R, STRATTON R. The thermoelectric figure of merit and its relation to thermoelectric generators. International Journal of Electronics, 1959, 7(1):52–72.
[19]SLACK G A. Nonmetallic crystals with high thermal conductivity.Journal of Physics&Chemistry of Solids, 1973, 34(2):321–335.
[20]XI L, PAN S, LI X, et al. Discovery of high performance thermoelectricchalcogenidesthroughreliablehighthroughputmaterial screening. Journal of the American Chemical Society, 2018, 140(34):10785–10793.
[21]YANG J, XI L, QIU W, et al. On the tuning of electrical and thermal transport in thermoelectrics:an integrated theory–experiment perspective. npj Computational Materials, 2016, 2:15015–1–17.
[22]GIBBS Z M, RICCI F, LI G, et al. Effective mass and Fermi surface complexity factor from ab initio band structure calculations.npj Computational Materials, 2017, 3(1):8–1–7.
[23]CHEN LI-DONG, XIONG ZHEN, BAI SHENG-QIANG. Recent progress of thermoelectric nano-composites. Journal of Inorganic Materials, 2010, 25(6):561–568.
[24]YANJ,GORAIP,ORTIZB,etal. Materialdescriptorsforpredicting thermoelectric performance. Energy&Environmental Science, 2015, 8(3):983–994.
[25]ANDERSON ORSON L. A simplified method for calculating the debye temperature from elastic constants. Journal of Physics and Chemistry of Solids, 1963, 24(7):909–917.
[26]HILL RICHARD. The elastic behaviour of a crystalline aggregate.Proceedings of the Physical Society. Section A, 1952, 65(5):349–354.
[27]JIA T, CHEN G, ZHANG Y. Lattice thermal conductivity evaluated using elastic properties. Physical Review B, 2017, 95(15):155206–1–6.
[28]CLARKEDR.Materialsselectionguidelinesforlowthermal conductivity thermal barrier coatings. Surface and Coatings Technology, 2003, 163:67–74.
[29]CAHILLDG,POHLR. Latticevibrationsandheattransportin crystals and glasses. Annual Review of Physical Chemistry, 1988,39(1):93–121.
[30]CAHILLDG,BRAUNPV,CHENG,etal. Nanoscalethermal transport.II.2003–2012.AppliedPhysicsReviews,2014,1(1):011305–1–45.
[31]HAUKEJ,KOSSOWSKIT.ComparisonofvaluesofPearson's and Spearman's correlation coefficients on the same sets of data.Quaestiones Geographicae, 2011, 30(2):87–93.
[32]YANG J, LI H, WU T, et al. Evaluation of half-Heusler compounds asthermoelectricmaterialsbasedonthecalculatedelectrical transport properties. Advanced Functional Materials, 2008, 18(19):2880–2888.
[33]YING P, LI X, WANG Y, et al. Hierarchical chemical bonds contributingtotheintrinsicallylowthermalconductivityin?-MgAgSb thermoelectric materials. Advanced Functional Materials,2017, 27(1):1604145–1–8.
[34]LI W, LIN S, GE B, et al. Low sound velocity contributing to the high thermoelectric performance of Ag8SnSe6. Advanced Science,2016, 3(11):1600196–1–7.
[35]RICCI F, CHENW, AYDEMIR U,et al. An ab initio electronic transportdatabaseforinorganicmaterials.Sci.Data,2017,4:170085–1–13.
[36]ZHUH,HAUTIERG,AYDEMIRU,etal. Computationaland experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first-principles high-throughputscreening.JournalofMaterialsChemistryC,2015, 3(40):10554–10565.
[37]AYDEMIR U, P HLS J, ZHU H, et al. YCuTe2:a member of a new class of thermoelectric materials with cute4-based layered structure.Journal of Materials Chemistry A, 2016, 4(7):2461–2472.
[38]BERA C, SOULIER M, NAVONE C, et al. Thermoelectric properties of nanostructured Si1?xGex and potential for further improvement. Journal of Applied Physics, 2010, 108(12):124306–1–8.
[39]ZIOLKOWSKI P, WAMBACH M, LUDWIG A, et al. Application ofhigh-throughputseebeckmicroprobemeasurementsonthermoelectric half-Heusler thin film combinatorial material libraries.ACS Combinatorial Science, 2018, 20(1):1–18.
[40]CARRETEJ,MINGON,WANGSD,etal.Nanograined half-heuslersemiconductorsasadvancedthermoelectrics:anab initio high-throughput statistical study.AdvancedFunctionalMaterials, 2014, 24(47):7427–7432.
[41]LIAWA,WIENERM.Classificationandregressionbyrandom forest. R News, 2002, 23(23):18–22.
[42]JOLLIFFE I T. Principal component analysis. Berlin, Heidelberg:Springer, 2011:1094–1096.
[43]KRESSE G, FURTHMULLER J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 1996, 54(16):11169–11186.
[44]ONGSP,CHOLIAS,JAINA,etal. Thematerialsapplication programming interface(API):a simple, flexible and efficient API formaterialsdatabasedonrepresentationalstatetransfer(rest)principles. Computational Materials Science, 2015, 97:209–215.
[45]ONG S P, RICHARDS W D, JAIN A, et al. Python materials genomics(pymatgen):a robust, open-source python library for materialsanalysis.ComputationalMaterialsScience,2013,68:314–319.
[46]JAINA,ONGSP,HAUTIERG,etal. Thematerialsproject:a materialsgenomeapproachtoacceleratingmaterialsinnovation.APL Materials, 2013, 1(1):011002–1–11.
[47]ZHOUF,COCOCCIONIM,MARIANETTICA,etal. Firstprinciplespredictionofredoxpotentialsintransition-metalcompounds with LDA+U. Physical Review B, 2004, 70(23):235021–1–8.
[48]WANG L, MAXISCH T, CEDER G. A first-principles approach to studying the thermal stability of oxide cathode materials. Chemistry of Materials, 2007, 19(3):543–552.
[49]ONG S P, JAIN A, HAUTIER G, et al. Thermal stabilities of delithiated olivine MPO4(M=Fe, Mn)cathodes investigated using firstprinciplescalculations.ElectrochemistryCommunications,2010, 12(3):427–430.
[50]ADAMS S, RAO R P. High power lithium ion battery materials by computationaldesign.PhysicaStatusSolidia-Applicationsand Materials Science, 2011, 208(8):1746–1753.
[51]GIANNOZZI P, BARONI S, BONINI N, et al. Quantum espresso:a modular and open-source software project for quantum simulationsofmaterials.JournalofPhysics-CondensedMatter,2009,21(39):395502–1–19.
[52]ISAYEVO,OSESC,TOHERC,etal. Universalfragmentdescriptors for predicting properties of inorganic crystals. Nat. Commun., 2017, 8:15679–1–12.
[53]SUPKAAR,LYONSTE,LIYANAGEL,etal. AFLOW?:a minimalist approach to high-throughput ab initio calculations includingthegenerationoftight-bindinghamiltonians.Computational Materials Science, 2017, 136:76–84.
[2]PELTIER J C A. New experiments on the heat effects of electric currents. Annals of Chemistry and Physics, 1834, 56:371–386.
[3]ZHANGQ,LIAOJ,TANGY,etal. Realizingathermoelectric conversion efficiency of 12%in bismuth telluride/skutterudite segmented modules through full-parameter optimization and energyloss minimized integration. Energy&Environmental Science, 2017,10(4):956–963.
[4]BULMAN G E, SIIVOLA E, SHEN B, et al. Large external delta t andcoolingpowerdensitiesinthin-filmBi2Te3-superlatticethermoelectric cooling devices. Applied Physics Letters, 2006, 89(12):122117–1–3.
[5]SHAKOURIA,ZHANGY. On-chipsolid-statecoolingforintegratedcircuitsusingthin-filmmicrorefrigerators.IEEETransactionsonComponentsandPackagingTechnologies,2005,28(1):65–69.
[6]WANGW,JIAF,HUANGQ,etal. Anewtypeoflowpower thermoelectric micro-generator fabricated by nanowire array thermoelectricmaterial.MicroelectronicEngineering,2005,77(3/4):223–229.
[7]LI JING-FENG. Macrofabrication technology of three-dimensional microdevices and their MEMS applications. Journal of Inorganic Materials, 2002, 17(4):657–664.
[8]HAUTIEG,JAINA,CHENH,etal.Novelmixedpolyanions lithium-ion battery cathode materials predicted by high-throughput abinitiocomputations.JournalofMaterialsChemistry,2011,21(43):17147–17153.
[9]DE JONG M, CHEN W, ANGSTEN T, et al. Charting the completeelasticpropertiesofinorganiccrystallinecompounds.Sci.Data, 2015, 2:150009–1–13.
[10]TAYLOR R H, CURTAROLO S, HART G L W. Guiding the experimentaldiscoveryofmagnesiumalloys.PhysicalReviewB,2011, 84(8):084101–1–17.
[11]HAUTIERG,FISCHERC,EHRLACHERV,etal. Datamined ionicsubstitutionsforthediscoveryofnewcompounds.Inorg.Chem., 2011, 50(2):656–663.
[12]CHENW,POHLSJAN-HENDRIK,HAUTIERG,etal. Understandingthermoelectricpropertiesfromhigh-throughputcalculations:trends, insights, and comparisons with experiment. Journal of Materials Chemistry C, 2016, 4(20):4414–4426.
[13]TOHER C, PLATA J J, LEVY O, et al. High-throughput computationalscreeningofthermalconductivity,debyetemperature,and gruneisen parameter using a quasiharmonic debye model. Physical Review B, 2014, 90(17):174107–1–14.
[14]BLANCOM,FRANCISCOE,LUANAV.Gibbs:isothermalisobaricthermodynamicsofsolidsfromenergycurvesusinga quasi-harmonic debye model. Computer Physics Communications,2004, 158(1):57–72.
[15]WANGS,WANGZ,SETYAWANW,etal. Assessingthethermoelectricpropertiesofsinteredcompoundsviahigh-throughput ab-initio calculations. Physical Review X, 2011, 1(2):021012–1–8.
[16]CARRETEJ,LIW,MINGON,etal. Findingunprecedentedly low-thermal-conductivityhalf-Heuslersemiconductorsviahighthroughputmaterialsmodeling.PhysicalReviewX,2014,4(1):011019–1–9.
[17]GOLDSMIDH,DOUGLASR.Theuseofsemiconductorsin thermoelectricrefrigeration.BritishJournalofAppliedPhysics,1954, 5(11):386–390.
[18]CHASMAR R, STRATTON R. The thermoelectric figure of merit and its relation to thermoelectric generators. International Journal of Electronics, 1959, 7(1):52–72.
[19]SLACK G A. Nonmetallic crystals with high thermal conductivity.Journal of Physics&Chemistry of Solids, 1973, 34(2):321–335.
[20]XI L, PAN S, LI X, et al. Discovery of high performance thermoelectricchalcogenidesthroughreliablehighthroughputmaterial screening. Journal of the American Chemical Society, 2018, 140(34):10785–10793.
[21]YANG J, XI L, QIU W, et al. On the tuning of electrical and thermal transport in thermoelectrics:an integrated theory–experiment perspective. npj Computational Materials, 2016, 2:15015–1–17.
[22]GIBBS Z M, RICCI F, LI G, et al. Effective mass and Fermi surface complexity factor from ab initio band structure calculations.npj Computational Materials, 2017, 3(1):8–1–7.
[23]CHEN LI-DONG, XIONG ZHEN, BAI SHENG-QIANG. Recent progress of thermoelectric nano-composites. Journal of Inorganic Materials, 2010, 25(6):561–568.
[24]YANJ,GORAIP,ORTIZB,etal. Materialdescriptorsforpredicting thermoelectric performance. Energy&Environmental Science, 2015, 8(3):983–994.
[25]ANDERSON ORSON L. A simplified method for calculating the debye temperature from elastic constants. Journal of Physics and Chemistry of Solids, 1963, 24(7):909–917.
[26]HILL RICHARD. The elastic behaviour of a crystalline aggregate.Proceedings of the Physical Society. Section A, 1952, 65(5):349–354.
[27]JIA T, CHEN G, ZHANG Y. Lattice thermal conductivity evaluated using elastic properties. Physical Review B, 2017, 95(15):155206–1–6.
[28]CLARKEDR.Materialsselectionguidelinesforlowthermal conductivity thermal barrier coatings. Surface and Coatings Technology, 2003, 163:67–74.
[29]CAHILLDG,POHLR. Latticevibrationsandheattransportin crystals and glasses. Annual Review of Physical Chemistry, 1988,39(1):93–121.
[30]CAHILLDG,BRAUNPV,CHENG,etal. Nanoscalethermal transport.II.2003–2012.AppliedPhysicsReviews,2014,1(1):011305–1–45.
[31]HAUKEJ,KOSSOWSKIT.ComparisonofvaluesofPearson's and Spearman's correlation coefficients on the same sets of data.Quaestiones Geographicae, 2011, 30(2):87–93.
[32]YANG J, LI H, WU T, et al. Evaluation of half-Heusler compounds asthermoelectricmaterialsbasedonthecalculatedelectrical transport properties. Advanced Functional Materials, 2008, 18(19):2880–2888.
[33]YING P, LI X, WANG Y, et al. Hierarchical chemical bonds contributingtotheintrinsicallylowthermalconductivityin?-MgAgSb thermoelectric materials. Advanced Functional Materials,2017, 27(1):1604145–1–8.
[34]LI W, LIN S, GE B, et al. Low sound velocity contributing to the high thermoelectric performance of Ag8SnSe6. Advanced Science,2016, 3(11):1600196–1–7.
[35]RICCI F, CHENW, AYDEMIR U,et al. An ab initio electronic transportdatabaseforinorganicmaterials.Sci.Data,2017,4:170085–1–13.
[36]ZHUH,HAUTIERG,AYDEMIRU,etal. Computationaland experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first-principles high-throughputscreening.JournalofMaterialsChemistryC,2015, 3(40):10554–10565.
[37]AYDEMIR U, P HLS J, ZHU H, et al. YCuTe2:a member of a new class of thermoelectric materials with cute4-based layered structure.Journal of Materials Chemistry A, 2016, 4(7):2461–2472.
[38]BERA C, SOULIER M, NAVONE C, et al. Thermoelectric properties of nanostructured Si1?xGex and potential for further improvement. Journal of Applied Physics, 2010, 108(12):124306–1–8.
[39]ZIOLKOWSKI P, WAMBACH M, LUDWIG A, et al. Application ofhigh-throughputseebeckmicroprobemeasurementsonthermoelectric half-Heusler thin film combinatorial material libraries.ACS Combinatorial Science, 2018, 20(1):1–18.
[40]CARRETEJ,MINGON,WANGSD,etal.Nanograined half-heuslersemiconductorsasadvancedthermoelectrics:anab initio high-throughput statistical study.AdvancedFunctionalMaterials, 2014, 24(47):7427–7432.
[41]LIAWA,WIENERM.Classificationandregressionbyrandom forest. R News, 2002, 23(23):18–22.
[42]JOLLIFFE I T. Principal component analysis. Berlin, Heidelberg:Springer, 2011:1094–1096.
[43]KRESSE G, FURTHMULLER J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 1996, 54(16):11169–11186.
[44]ONGSP,CHOLIAS,JAINA,etal. Thematerialsapplication programming interface(API):a simple, flexible and efficient API formaterialsdatabasedonrepresentationalstatetransfer(rest)principles. Computational Materials Science, 2015, 97:209–215.
[45]ONG S P, RICHARDS W D, JAIN A, et al. Python materials genomics(pymatgen):a robust, open-source python library for materialsanalysis.ComputationalMaterialsScience,2013,68:314–319.
[46]JAINA,ONGSP,HAUTIERG,etal. Thematerialsproject:a materialsgenomeapproachtoacceleratingmaterialsinnovation.APL Materials, 2013, 1(1):011002–1–11.
[47]ZHOUF,COCOCCIONIM,MARIANETTICA,etal. Firstprinciplespredictionofredoxpotentialsintransition-metalcompounds with LDA+U. Physical Review B, 2004, 70(23):235021–1–8.
[48]WANG L, MAXISCH T, CEDER G. A first-principles approach to studying the thermal stability of oxide cathode materials. Chemistry of Materials, 2007, 19(3):543–552.
[49]ONG S P, JAIN A, HAUTIER G, et al. Thermal stabilities of delithiated olivine MPO4(M=Fe, Mn)cathodes investigated using firstprinciplescalculations.ElectrochemistryCommunications,2010, 12(3):427–430.
[50]ADAMS S, RAO R P. High power lithium ion battery materials by computationaldesign.PhysicaStatusSolidia-Applicationsand Materials Science, 2011, 208(8):1746–1753.
[51]GIANNOZZI P, BARONI S, BONINI N, et al. Quantum espresso:a modular and open-source software project for quantum simulationsofmaterials.JournalofPhysics-CondensedMatter,2009,21(39):395502–1–19.
[52]ISAYEVO,OSESC,TOHERC,etal. Universalfragmentdescriptors for predicting properties of inorganic crystals. Nat. Commun., 2017, 8:15679–1–12.
[53]SUPKAAR,LYONSTE,LIYANAGEL,etal. AFLOW?:a minimalist approach to high-throughput ab initio calculations includingthegenerationoftight-bindinghamiltonians.Computational Materials Science, 2017, 136:76–84.