机器学习驱动的光伏设备用包晶石材料优化和表征进展综述

IF 13.1 1区 化学 Q1 Energy
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引用次数: 0

摘要

包光体太阳能电池(PSCs)发展迅速,已成为下一代可再生能源的潜在候选材料。然而,传统的试错方法和庞大的组成参数空间仍然是追求基于包晶石的光电器件的卓越性能和高稳定性的挑战。光电器件对新型材料的需求日益增长,大量数据库的建立使得数据驱动的机器学习(ML)方法在材料领域迅速发展。本综述简明扼要地概述了机器学习的基本程序、技术和最新突破,尤其是在预测包晶材料的物理特性方面。此外,它还重点介绍了旨在优化和筛选材料以提高 PSC 效率和稳定性的研究工作。此外,本综述还重点介绍了最近在使用表征数据进行 ML、探索其与材料特性和器件性能的相关性方面所做的努力。最后,我们提出了未来的展望,例如利用大型语言模型(LLM)和文本挖掘,加快新型光致发光材料的发现,并扩大其在各个光电领域的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices

Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices
Perovskite solar cells (PSCs) have developed rapidly, positioning them as potential candidates for next-generation renewable energy sources. However, conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics. The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machine-learning (ML) approaches to swiftly advance in the materials field. This review succinctly outlines the fundamental ML procedures, techniques, and recent breakthroughs, particularly in predicting the physical characteristics of perovskite materials. Moreover, it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs. Additionally, this review highlights recent efforts in using characterization data for ML, exploring their correlations with material properties and device performance, which are actively being researched, but they have yet to receive significant attention. Lastly, we provide future perspectives, such as leveraging Large Language Models (LLMs) and text-mining, to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.
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来源期刊
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
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