机器学习辅助揭示性能最佳的单异质结热光伏电池
Machine Learning Assisted Revelation of the Best Performing Single Hetero-junction Thermophotovoltaic Cell
AI总结：
本文利用机器学习筛选热光伏电池材料并优化器件结构，提出p-Ge/n-InGaAsSb单异质结电池，在1578 K黑体温度下实现16.50%效率。
摘要：
在本工作中，机器学习（ML）技术被用于探索性能最高的单异质结热光伏电池。首先，研究采用ML方法考察了传统同质结TPV电池，以寻找最佳材料组合。ML方法尤其被用于分析模型中每个参数的重要性，从而增强对系统行为的理解并促进设计优化。在此研究之后发现，当Ge与具有0.53 eV直接带隙的最佳基底层InGaAsSb化合物配对时，Ge成为最有效的发射极层。随后，本文提出了一种p-Ge/n-InGaAsSb单异质结TPV电池，该电池执行具有p-n结构的器件输运模型。该电池分别在黑体温度（TBB）1578 K和电池温度300 K下运行。通过细致的优化工作，TPV电池的性能得到显著提升，实现了16.50%这一令人瞩目的效率。与该效率相伴的是短路电流JSC=15.53 A/cm2、开路电压VOC=0.47 V以及填充因子FF=79.5%。这些发现表明，这种结构配置对于开发高性能TPV电池具有相当大的前景。
英文摘要：
In this work, Machine Learning (ML) techniques have been employed to explore the highest performing single-heteronunction thermophotovoltaic cell. Initially, traditional homo junction TPV cells have been explored using ML methodologies for the optimal material combinations. ML methods have notably been devoted to analyze the importance of each parameter in the model, thereby improving the comprehension of the system's behavior and facilitating design optimization. Following this investigation, it has been found that Ge emerged as the most effective emitter layer when paired with the optimal base layer, InGaAsSb compound that possesses a direct bandgap of 0.53 eV. Subsequently, a p-Ge/n-InGaAsSb single-heterojunction TPV cell is introduced executing a device transport model featuring a p-n structure. This cell operated at black body (TBB) and cell temperatures of 1578 K and 300 K, respectively. Through meticulous optimization efforts, the performance of the TPV cell is significantly enhanced resulting in an impressive efficiency of 16.50%. This efficiency is accompanied by a short circuit current, JSC=15.53 A/cm2, an open-circuit voltage, VOC=0.47 V, and a fill factor FF=79.5%. These findings suggest that this structural configuration holds considerable promise for the development of high-performance TPV cells.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
