Machine learning for perovskites' reap-rest-recovery cycle

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dc.creatorJohn M. Howard
dc.creatorElizabeth M. Tennyson
dc.creatorBernardo Ruegger Almeida Neves
dc.creatorMarina Soares Leite
dc.date.accessioned2023-07-10T13:32:49Z
dc.date.accessioned2025-09-08T23:08:02Z
dc.date.available2023-07-10T13:32:49Z
dc.date.issued2019
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1016/j.joule.2018.11.010
dc.identifier.issn2542-4351
dc.identifier.urihttps://hdl.handle.net/1843/55994
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofJoule
dc.rightsAcesso Aberto
dc.subjectAprendizado do computador
dc.subject.otherMachine learning
dc.titleMachine learning for perovskites' reap-rest-recovery cycle
dc.typeArtigo de periódico
local.citation.epage337
local.citation.issue2
local.citation.spage325
local.citation.volume3
local.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.
local.identifier.orcidhttps://orcid.org/0000-0002-3990-5478
local.identifier.orcidhttps://orcid.org/0000-0003-0071-8445
local.identifier.orcidhttps://orcid.org/0000-0003-0464-4754
local.identifier.orcidhttps://orcid.org/0000-0003-4888-8195
local.publisher.countryBrasil
local.publisher.departmentICX - DEPARTAMENTO DE FÍSICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S2542435118305543

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