Computation of photovoltaic and stability properties of hybrid organic–inorganic perovskites via convolutional neural networks

Aristizabal-Ferreira, Victor Alexander; Guevara‐Vela, José Manuel; Vega, Arturo Sauza-de la; Pendás, Ángel Martín; Fuentes-Pineda, Gibrán; Rocha‐Rinza, Tomás

doi:10.1007/s00214-022-02875-9

Cited by 3 publications

(1 citation statement)

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“…ML have also been applied to predict properties thermodynamic properties such as atomization energies [96] and formation energies. [97,98] Electronic properties, such as carrier effective mass, [99] conductivity, [100] and density of states, [101] have also been the predicted results of ML.…”

Section: Other Propertiesmentioning

confidence: 99%

Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

Liu,

Wang,

Shi

et al. 2023

Solar RRL

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Perovskite solar cells (PSC) are a potential candidate for next‐generation photovoltaic technology. Despite the significant advancements in the field of PSCs, the ongoing development of stable and efficient metal halide perovskite materials, along with their successful integration into photovoltaic applications, remains challenges. These challenges originate from the diverse range of device structures and perovskite compositions, requiring meticulous consideration and optimization. Traditional trial‐and‐error methods are characterized by their sluggishness and labor‐intensive nature. Recently, the emergence of extensive datasets and advancements in computer hardware have facilitated the utilization of machine learning (ML) across multiple domains, including in various fields for material discovery and experimental optimization. Herein, the fundamental procedure of ML is briefly introduced, and latest progress of ML in the materials development and solar cell fabrication is comprehensively reviewed. The utilization of ML in PSCs at all stages of design can be categorized into four main areas: screening perovskite material, fabrication process optimization, device structure optimization, and understanding mechanism. The challenges and outlooks on the future development of ML are finally discussed. It is highly expected that this review can offer valuable guidance for the design and development of highly efficient and stable PSCs.

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Section: Other Propertiesmentioning

confidence: 99%

Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

Liu,

Wang,

Shi

et al. 2023

Solar RRL

View full text Add to dashboard Cite

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Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Liu

Tan

Liang

et al. 2023

Adv Funct Materials

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Data-driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game-playing problems is unmatched by traditional simulation computing software and trial-error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist of perovskite materials, transport layer materials, and electrodes. Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML. However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years, so it is necessary to provide a review of the involved ML technologies, the application status, the facing urgent challenges and the development blueprint.

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