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加速薄膜光伏技术的发展：一种使用光谱和光电技术的人工智能辅助方法

Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques.

作者信息

Grau-Luque Enric, Becerril-Romero Ignacio, Atlan Fabien, Huber Daniel, Harnisch Martina, Zimmermann Andreas, Pérez-Rodríguez Alejandro, Guc Maxim, Izquierdo-Roca Victor

机构信息

Catalonia Institute for Energy Research - IREC, Sant Adrià de Besòs, Barcelona, 08930, Spain.

Facultat de Física, Universitat de Barcelona (UB), C. Martí i Franquès 1-11, Barcelona, 08028, Spain.

出版信息

Small Methods. 2024 Dec;8(12):e2301573. doi: 10.1002/smtd.202301573. Epub 2024 Mar 28.

DOI:10.1002/smtd.202301573
PMID:38546017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11672176/
Abstract

Thin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time-consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI-assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.

摘要

薄膜光伏（TFPV）材料与器件具有多尺度、多层和多元素结构以及复杂的制造工艺，呈现出高度的复杂性。为应对这种复杂性，评估其物理化学性质对于生成一个提出其开发与优化策略的模型至关重要。然而，这个过程耗时且需要高度专业知识。在此背景下，采用组合分析（CA）和人工智能（AI）策略是加速这些复杂材料与器件开发的有力手段。这项工作介绍了一种便于将AI和CA用于TFPV技术开发的方法。该方法涵盖了从为CA合成样品到数据采集、AI辅助数据分析以及提取相关信息以加速研究的所有必要步骤。每个步骤都提供了必要概念、要求和程序的详细信息，并辅以文献中的示例进行说明。然后，将该方法应用于TFPV生产线的一组复杂样品，凸显了其即使在复杂情况下也能快速获取重要见解的能力。所提出的方法可应用于光伏以外的其他类型材料与器件，并可使用不同的表征技术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/789e/11672176/f68a2b9cc45f/SMTD-8-2301573-g007.jpg
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