Selective Thermophotovoltaic Emitter with Aperiodic Multilayer Structures Designed by Machine Learning
Selective Thermophotovoltaic Emitter with Aperiodic Multilayer Structures Designed by Machine Learning
复制标题
机器学习设计的非周期多层结构选择性热光伏发射器
DOI：
10.1021/acsaem.0c03201
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发表时间：
2021
影响因子：
6.4
通讯作者：
Zhao Changying
中科院分区：
文献类型：
--
作者：
Zhang Wenbin;Wang Boxiang;Zhao Changying
控制热光伏（TPV）发射器的热发射特性可以有效地降低光伏电池中的热损耗。此外，选择性TPV发射器可以促进从各种高温废热源发电。为了克服实现TPV发射器的完美光谱选择性的挑战，人工智能的发展提供了一个超越传统范式的选择性TPV发射器的优化愿景。在这项工作中，我们证明了一个高选择性，非周期性的TPV发射器具有高品质因数（FOM）可以实现与贝叶斯优化的帮助。选择性TPV发射极的设计在5.23 × 109个由多个组件组成的多层膜中的候选结构上进行了优化，以最大化FOM。最大FOM可以在计算不到总候选结构的0.67%内实现，这比其他机器学习算法要好得多。对于锑化镓光伏电池，得到的最佳结构是具有82.16% FOM的非周期性多层结构。利用多靶溅射系统制备了优化结构的多层膜，获得了FOM为81.35%的发射特性实验结果，明显优于基于前人研究设计和制备的同类材料的多层膜。此外，我们还分析了TPV系统的效率，并测量了所制备样品的热稳定性。结果表明，贝叶斯优化方法在选择性TPV发射体的设计中是有效的，基于机器学习的超材料设计方法可以推广到其他领域中昂贵的黑箱全局优化问题。
Controlling the thermal emission characteristics of a thermophotovoltaic (TPV) emitter can effectively reduce the thermal losses in a photovoltaic cell. Moreover, selective TPV emitters can facilitate the generation of electricity from a variety of high-temperature waste heat sources. To overcome the challenge of achieving perfect spectral selectivity of TPV emitters, the development of artificial intelligence provides a vision for the optimization of selective TPV emitters beyond the conventional paradigm. In this work, we demonstrate that a highly selective, aperiodic TPV emitter with a high figure of merit (FOM) can be achieved with the help of Bayesian optimization. The design of the selective TPV emitter is optimized over 5.23 × 109candidate structures in multilayers consisting of multiple components to maximize the FOM. The maximum FOM could be realized within calculations for less than 0.67% of the total candidate structures, which is much better than other machine learning algorithms. As for the gallium antimonide photovoltaic cell, the resulting optimal structure is an aperiodic multilayer structure with an FOM of 82.16%. The optimal structure is then fabricated by a multi-target sputtering system, and the experimental result of the emission characteristics is achieved with an FOM of 81.35%, which is significantly better than those of multilayers with similar material designed and fabricated based on previous studies. What is more, we have analyzed the efficiency of the TPV system and measured the thermal stability of the fabricated samples. The results demonstrate that Bayesian optimization is efficient in designing selective TPV emitters, and the machine-learning-based design of metamaterials can be extended for the expensive black-box global optimization problems in other field applications.