000891312 001__ 891312
000891312 005__ 20240709082151.0
000891312 0247_ $$2doi$$a10.1016/j.matt.2021.01.008
000891312 0247_ $$2ISSN$$a2590-2385
000891312 0247_ $$2ISSN$$a2590-2393
000891312 0247_ $$2Handle$$a2128/27593
000891312 0247_ $$2altmetric$$aaltmetric:99291165
000891312 0247_ $$2WOS$$aWOS:000637800400003
000891312 037__ $$aFZJ-2021-01419
000891312 082__ $$a600
000891312 1001_ $$00000-0002-6179-1390$$aSun, Shijing$$b0$$eCorresponding author
000891312 245__ $$aA data fusion approach to optimize compositional stability of halide perovskites
000891312 260__ $$a[New York, NY]$$bElsevier$$c2021
000891312 3367_ $$2DRIVER$$aarticle
000891312 3367_ $$2DataCite$$aOutput Types/Journal article
000891312 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1618300500_11325
000891312 3367_ $$2BibTeX$$aARTICLE
000891312 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000891312 3367_ $$00$$2EndNote$$aJournal Article
000891312 520__ $$aDespite recent intensive efforts to improve the environmental stability of halide perovskite materials for energy harvesting and conversion, traditional trial-and-error explorations face bottlenecks in the navigation of vast chemical and compositional spaces. We develop a closed-loop optimization framework that seamlessly marries data from first-principle calculations and high-throughput experimentation into a single machine learning algorithm. This framework enables us to achieve rapid optimization of compositional stability for CsxMAyFA1−x−yPbI3 perovskites while taking the human out of the decision-making loop. We envision that this data fusion approach is generalizable to directly tackle challenges in designing multinary materials, and we hope that our successful showcase on perovskites will encourage researchers in other fields to incorporate knowledge of physics into the search algorithms, applying hybrid machine learning models to guide discovery of materials in high-dimensional spaces.
000891312 536__ $$0G:(DE-HGF)POF4-121$$a121 - Photovoltaik und Windenergie (POF4-121)$$cPOF4-121$$fPOF IV$$x0
000891312 588__ $$aDataset connected to CrossRef
000891312 7001_ $$00000-0001-9753-6802$$aTiihonen, Armi$$b1
000891312 7001_ $$0P:(DE-HGF)0$$aOviedo, Felipe$$b2
000891312 7001_ $$0P:(DE-HGF)0$$aLiu, Zhe$$b3
000891312 7001_ $$0P:(DE-HGF)0$$aThapa, Janak$$b4
000891312 7001_ $$0P:(DE-Juel1)187394$$aZhao, Yicheng$$b5$$ufzj
000891312 7001_ $$00000-0002-0748-0620$$aHartono, Noor Titan P.$$b6
000891312 7001_ $$0P:(DE-HGF)0$$aGoyal, Anuj$$b7
000891312 7001_ $$00000-0002-6974-410X$$aHeumueller, Thomas$$b8
000891312 7001_ $$0P:(DE-HGF)0$$aBatali, Clio$$b9
000891312 7001_ $$0P:(DE-HGF)0$$aEncinas, Alex$$b10
000891312 7001_ $$00000-0002-5773-1360$$aYoo, Jason J.$$b11
000891312 7001_ $$0P:(DE-HGF)0$$aLi, Ruipeng$$b12
000891312 7001_ $$0P:(DE-HGF)0$$aRen, Zekun$$b13
000891312 7001_ $$0P:(DE-Juel1)179536$$aPeters, I. Marius$$b14$$ufzj
000891312 7001_ $$0P:(DE-Juel1)176427$$aBrabec, Christoph J.$$b15$$ufzj
000891312 7001_ $$0P:(DE-HGF)0$$aBawendi, Moungi G.$$b16
000891312 7001_ $$0P:(DE-HGF)0$$aStevanovic, Vladan$$b17
000891312 7001_ $$0P:(DE-HGF)0$$aFisher, John$$b18
000891312 7001_ $$0P:(DE-HGF)0$$aBuonassisi, Tonio$$b19
000891312 773__ $$0PERI:(DE-600)3015776-6$$a10.1016/j.matt.2021.01.008$$gp. S2590238521000084$$n4$$p1305-1322$$tMatter$$v4$$x2590-2385$$y2021
000891312 8564_ $$uhttps://juser.fz-juelich.de/record/891312/files/Manuscript_Matter_post.pdf$$yPublished on 2021-02-01. Available in OpenAccess from 2022-02-01.
000891312 909CO $$ooai:juser.fz-juelich.de:891312$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000891312 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187394$$aForschungszentrum Jülich$$b5$$kFZJ
000891312 9101_ $$0I:(DE-588b)5008462-8$$60000-0002-6974-410X$$aForschungszentrum Jülich$$b8$$kFZJ
000891312 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179536$$aForschungszentrum Jülich$$b14$$kFZJ
000891312 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176427$$aForschungszentrum Jülich$$b15$$kFZJ
000891312 9130_ $$0G:(DE-HGF)POF3-121$$1G:(DE-HGF)POF3-120$$2G:(DE-HGF)POF3-100$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bEnergie$$lErneuerbare Energien$$vSolar cells of the next generation$$x0
000891312 9131_ $$0G:(DE-HGF)POF4-121$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vPhotovoltaik und Windenergie$$x0
000891312 9141_ $$y2021
000891312 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-08-18
000891312 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
000891312 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000891312 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2020-08-18
000891312 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-08-18
000891312 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-08-18
000891312 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-08-18
000891312 920__ $$lyes
000891312 9201_ $$0I:(DE-Juel1)IEK-11-20140314$$kIEK-11$$lHelmholtz-Institut Erlangen-Nürnberg Erneuerbare Energien$$x0
000891312 9801_ $$aFullTexts
000891312 980__ $$ajournal
000891312 980__ $$aVDB
000891312 980__ $$aUNRESTRICTED
000891312 980__ $$aI:(DE-Juel1)IEK-11-20140314
000891312 981__ $$aI:(DE-Juel1)IET-2-20140314