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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/55994
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dc.creatorJohn M. Howardpt_BR
dc.creatorElizabeth M. Tennysonpt_BR
dc.creatorBernardo Ruegger Almeida Nevespt_BR
dc.creatorMarina Soares Leitept_BR
dc.date.accessioned2023-07-10T13:32:49Z-
dc.date.available2023-07-10T13:32:49Z-
dc.date.issued2019-
dc.citation.volume3pt_BR
dc.citation.issue2pt_BR
dc.citation.spage325pt_BR
dc.citation.epage337pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.joule.2018.11.010pt_BR
dc.identifier.issn2542-4351pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/55994-
dc.description.resumoPerovskite photovoltaics are efficient and inexpensive, yet their performance is dynamic. In this Perspective, we examine the effects of H 2 O, O 2 , bias, temperature, and illumination on device performance and recovery. First, we discuss pivotal experiments that evaluate perovskites’ ability to go through a reap-rest-recovery (3R) cycle, and how machine learning (ML) can help identify the optimum values for each operating parameter. Second, we analyze perovskite dynamics and degradation, emphasizing the research challenges surrounding this 3R cycle. We then outline experiments that could identify the impact of environmental factors on recovery for different perovskite compositions. Finally, we propose an ML paradigm for maximizing long-term performance and predicting device performance recovery, including a shared-knowledge repository. By reframing perovskites’ optoelectronic transiency within the context of recovery rather than degradation, we highlight a set of research opportunities and the artificial intelligence solutions needed for the commercial adoption of these promising solar cell materials.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE FÍSICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofJoule-
dc.rightsAcesso Abertopt_BR
dc.subjectMachine learningpt_BR
dc.subject.otherAprendizado do computadorpt_BR
dc.titleMachine learning for perovskites' reap-rest-recovery cyclept_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S2542435118305543pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-3990-5478pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-0071-8445pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-0464-4754pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4888-8195pt_BR
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