Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Liu, Wu; Lu, Yunxiang; Wei, Dong; Huo, Xiaomin; Huang, Xiwei; Li, Yaoyao; Meng, Juan; Zhao, Suling; Qiao, Bo; Liang, Zhiqin; Xu, Zheng; Song, Dandan

doi:10.1039/d2ta04788h

Cited by 22 publications

(22 citation statements)

References 44 publications

(48 reference statements)

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“…Massuyeau et al 2022 [198] Perovskite Liu et al 2022 [166] PCE LR, RF, XGBoost ANN Experiment ML was employed to predict the relation between the PCE and interface passivation material at the atomic level, as well as rapidly screen interface materials for PSCs. The predictions are further validated using DFT and experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Liu et al [ 166 ] used ML to screen interfacial materials by studying the correlation between PCE and interfacial passivation materials at the atomic level (Figure 7d). Based on SHAP values, feature importance, and correlation analysis, the most critical 15 features were selected from over 300 features.…”

Section: Implementation Of ML For the Advancement Of Efficient And St...mentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Implementation Of ML For the Advancement Of Efficient And St...mentioning

confidence: 99%

Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

Liu,

Wang,

Shi

et al. 2023

Solar RRL

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“…Furthermore, recently, Wu Liu et al adopted ML to screen perovskite/hole transport layer interface materials. 30 This work demonstrated that ML can be effectively used to investigate passivation materials without knowledge of chemistry and resulted in reference functional groups for the synthesis of new passivation materials. However, guidelines for the screening of passivation materials in p–i–n type PSCs are still lacking.…”

Section: Introductionmentioning

confidence: 91%

Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

et al. 2023

Self Cite

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“…In a recent study, Liu et al implemented ML for analyze the impact of interfacial modifiers over the device performance. [ 136 ] They have collected the data from 100 materials that are used as modifiers at the perovskite/HTL interface. On the basis of collected data, an RF model was developed, and their model predicted with an RMS value of 0.7%.…”

Section: Various Applications Of ML In Pscsmentioning

confidence: 99%

Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

Bansal,

Mishra,

Dixit

et al. 2023

Energy Tech

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Within a short period of time, perovskite solar cells (PSC) have attracted paramount research interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC research is significantly accelerated their holistic understanding of device requisite properties. ML techniques are increasingly employed to discover stable perovskite materials, optimize device architecture and processing, and analyze PSC characterization data. This review provides an in‐depth exploration of ML applications in PSC advancement through an analysis of existing literature. The review commences with an introduction to the ML workflow, detailing each step, followed by concise overviews of perovskite materials and PSC operation. Later sections explore the diverse ways ML contributes to PSC development, which ranges from the optoelectronic property prediction of perovskites, discovery of novel perovskites, PSC device structure optimization, and comprehensive PSC analyses. The challenges impeding PSC commercialization are discussed, along with ML's potential to mitigate them. The review concludes by highlighting current limitations in employing ML for PSC research and suggests potential solutions. It also outlines prospective research directions for ML applications in PSC research, aiming to develop highly efficient and stable PSCs.

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Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Abstract: Intelligently screening the passivation materials is critical to improving power conversion efficiency (PCE) of the perovskite solar cell (PSC), while it is still lacking. Herein, machine learning is employed to...

Cited by 22 publications

References 44 publications

Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

Machine‐Learning Accelerating the Development of Perovskite Photovoltaics

Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

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