Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

Howard, John M.; Tennyson, Elizabeth M.; Neves, Bernardo R. A.; Leite, Marina S.

doi:10.1016/j.joule.2018.11.010

Cited by 67 publications

(73 citation statements)

References 69 publications

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“…Unified procedures for stability studies and consistency in data reporting could lead to the creation of a large machine-readable database on PSC stability. Machine learning (ML) methods [145][146][147] could potentially identify patterns in such data, detect statistically significant stress factors, correlate repeated phenomena in different studies to detect universal degradation mechanisms and stabilizing approaches, and predict lifetimes and failure modes. Information from ageing measurements under the relevant stressors can optimize the steps required for supervised learning algorithms.…”

Section: Protocols Applications and Outlookmentioning

confidence: 99%

“…To accommodate the large number of perovskites possibly suitable for PV, a shared-knowledge repository database has been proposed 145 , where positive and negative results from stability tests are considered equally important for ML (that is, not only the champion cell but also the suboptimal or underperforming cells aged under similar conditions), because they all represent valuable training data. It is therefore critical that a trend emerges that all cells that undergo ageing tests are measured and reported for the duration, even total failures.…”

Section: Protocols Applications and Outlookmentioning

confidence: 99%

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Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

et al. 2020

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Improving the long-term stability of perovskite solar cells is critical to the deployment of this technology. Despite the great emphasis laid on stability-related investigations, publications lack consistency in experimental procedures and parameters reported. It is therefore challenging to reproduce and compare results and thereby develop a deep understanding of degradation mechanisms. Here, we report a consensus between researchers in the field on procedures for testing perovskite solar cell stability, which are based on the International Summit on Organic Photovoltaic Stability (ISOS) protocols. We propose additional procedures to account for properties specific to PSCs such as ion redistribution under electric fields, reversible degradation and to distinguish ambient-induced degradation from other stress factors. These protocols are not intended as a replacement of the existing qualification standards, but rather they aim to unify the stability assessment and to understand failure modes. Finally, we identify key procedural information which we suggest reporting in publications to improve reproducibility and enable large data set analysis.

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Section: Protocols Applications and Outlookmentioning

confidence: 99%

Section: Protocols Applications and Outlookmentioning

confidence: 99%

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

et al. 2020

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“…Inspired by recent studies on inverse design of polymers and inorganic solids [23][24][25] , as well as on using machine learning to understand PSCs' properties [26][27][28] , we present a machine-learning framework to investigate LD organic-inorganic perovskites serving as a capping layer for MAPbI 3 . We elucidate which properties of capping layers are responsible for enhancing stability, and the underlying mechanisms whereby they work.…”

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confidence: 99%

How machine learning can help select capping layers to suppress perovskite degradation

et al. 2020

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Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI3) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

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“…[132,133] A number of wellestablished algorithms in other fields have been borrowed and applied in materials science research, including support vector machine, gradient boosting regression, and deep neural networks. Suc cessful utilization of ML techniques in materials sciencerelated research has been implemented in various subjects, including chiral metamaterial structure design, [134] perovskite solar cell performance forecasting, [135,136] battery lifetime predictions, [137] and guidance for synthesis strategy of quantum dots. [138] In par ticular, ML has lately been utilized to predict the mechanical (e.g., elastic moduli, tensile strength, etc.…”

Section: Discussionmentioning

confidence: 99%

Emergent Opportunities with Metallic Alloys: From Material Design to Optical Devices

Gong

Lyu

Palm

et al. 2020

Advanced Optical Materials

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devices is the fact that their dielectric function (i.e., permittivity, ε = ε 1 + iε 2) is predefined. Thus, several research groups, including ours, have recently merged two almost orthogonal fields, photo nics, and metallurgy, to pursue metallic materials with arbitrary permit tivity. [1-5] Alloying is now a burgeoning framework for achieving materials with engineered optical properties, encom passing both nanostructures and thin films, as will be surveyed in this Review. Plasmonics exploits the interaction of incident electromagnetic fields with the col lective motion of free electrons (plasmons) in metals. This phenomenon confines the electromagnetic fields in close vicinity of the metal interfaces, dramatically enhancing the electrical field intensity surrounding the material. [6-8] Plasmons can be divided into two categories: surface plasmon polaritons (SPP) that propagate along the metal and dielectric interface, and localized surface plasmon resonance (LSPR) that are confined in a subwavelength nanostructure. For the latter, their optical scattering or absorp tion (and/or the nearby dielectrics and semiconductors) can be significantly increased. As a result, these enhancement effects are the underpinnings to a range of novel optical processes, and have found countless applications in photovoltaics, [9-11] photo catalysis, [12-14] bio and chemicalsensing, [15,16] electrooptical modu lation, [17,18] and superabsorbers. [19-21] As expected, the features of either type of plasmon are strongly dependent upon the dielectric function of the material, which is somewhat fixed in metals. Concerning materials, coinage metals, such as Au, Ag, or Cu, are widely used in photonics due to their abundant free electrons and chemical stability. [1] Other noble metals including Metallic nanostructures and thin films are fundamental building blocks for next-generation nanophotonics. Yet, the fixed permittivity of pure metals often imposes limitations on the materials employed and/or on device performance. Alternatively, metallic mixtures, or alloys, represent a promising pathway to tailor the optical and electrical properties of devices, enabling further control of the electromagnetic spectrum. In this Review, a survey of recent advances in photonics and plasmonics achieved using metal alloys is presented. An overview of the primary fabrication methods to obtain subwavelength alloyed nanostructures is provided, followed by an in-depth analysis of experimental and theoretical studies of their optical properties, including their correlation with band structure. The broad landscape of optical devices that can benefit from metallic materials with engineered permittivity is also discussed, spanning from superabsorbers and hydrogen sensors to photovoltaics and hot electron devices. This Review concludes with an outlook of potential research directions that would benefit from the on demand optical properties of metallic mixtures, leading to new optoelectronic materials and device opportunities.

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Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

Cited by 67 publications

References 69 publications

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

How machine learning can help select capping layers to suppress perovskite degradation

Emergent Opportunities with Metallic Alloys: From Material Design to Optical Devices

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