Machine Learning Roadmap for Perovskite Photovoltaics,The Journal of Physical Chemistry Letters

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Machine Learning Roadmap for Perovskite Photovoltaics
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2021-08-12 , DOI: 10.1021/acs.jpclett.1c01961
Meghna Srivastava ₁ , John M Howard ₂ , Tao Gong _{1,

3} , Mariama Rebello Sousa Dias ₄ , Marina S Leite ₁

Affiliation

Perovskite solar cells (PSC) are a favorable candidate for next-generation solar systems with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven prior to commercialization. However, traditional trial-and-error approaches to PSC screening, development, and stability testing are slow and labor-intensive. In this Perspective, we present a survey of how machine learning (ML) and autonomous experimentation provide additional toolkits to gain physical understanding while accelerating practical device advancement. We propose a roadmap for applying ML to PSC research at all stages of design (compositional selection, perovskite material synthesis and testing, and full device evaluation). We also provide an overview of relevant concepts and baseline models that apply ML to diverse materials problems, demonstrating its broad relevance while highlighting promising research directions and associated challenges. Finally, we discuss our outlook for an integrated pipeline that encompasses all design stages and presents a path to commercialization.

中文翻译：

钙钛矿光伏的机器学习路线图

钙钛矿太阳能电池 (PSC) 是下一代太阳能系统的理想候选者，其效率可与硅光伏相媲美，但必须在商业化之前证明其长期稳定性。然而，传统的 PSC 筛选、开发和稳定性测试的反复试验方法缓慢且劳动密集型。在这个视角中，我们对机器学习 (ML) 和自主实验如何提供额外的工具包以在加速实际设备进步的同时获得物理理解进行调查。我们提出了在设计的所有阶段（成分选择、钙钛矿材料合成和测试以及完整的设备评估）将 ML 应用于 PSC 研究的路线图。我们还概述了将 ML 应用于各种材料问题的相关概念和基线模型，展示其广泛的相关性，同时强调有前途的研究方向和相关挑战。最后，我们讨论了我们对包含所有设计阶段的集成管道的展望，并提出了商业化的途径。

更新日期：2021-08-19

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