Foadian Elham, Sanchez Sheryl, Kalinin Sergei V, Ahmadi Mahshid
Institute for Advanced Materials and Manufacturing, Department of Materials Science and Engineering, Knoxville, Tennessee 37996, United States.
Physical Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
ACS Mater Au. 2024 Oct 25;5(1):11-23. doi: 10.1021/acsmaterialsau.4c00096. eCollection 2025 Jan 8.
Halide perovskites (HPs) are emerging as key materials in the fight against global warming with well recognized applications, such as photovoltaics, and emergent opportunities, such as photocatalysis for methane removal and environmental remediation. These current and emergent applications are enabled by a unique combination of high absorption coefficients, tunable band gaps, and long carrier diffusion lengths, making them highly efficient for solar energy conversion. To address the challenge of discovery and optimization of HPs in huge chemical and compositional spaces of possible candidates, this perspective discusses a comprehensive strategy for screening HPs through automated high-throughput and combinatorial synthesis techniques. A critical aspect of this approach is closing the characterization loop, where machine learning (ML) and human collaboration play pivotal roles. By leveraging human creativity and domain knowledge for hypothesis generation and employing ML to test and refine these hypotheses efficiently, we aim to accelerate the discovery and optimization of HPs under specific environmental conditions. This synergy enables rapid identification of the most promising materials, advancing from fundamental discovery to scalable manufacturability. Our ultimate goal of this work is to transition from laboratory-scale innovations to real-world applications, ensuring that HPs can be deployed effectively in technologies that mitigate global warming, such as in solar energy harvesting and methane removal systems.
卤化物钙钛矿（HPs）正成为应对全球变暖的关键材料，具有诸如光伏等公认的应用，以及诸如用于甲烷去除和环境修复的光催化等新兴机遇。这些当前和新兴的应用得益于高吸收系数、可调带隙和长载流子扩散长度的独特组合，使其在太阳能转换方面具有很高的效率。为了应对在庞大的可能候选物化学和组成空间中发现和优化HPs的挑战，本文讨论了一种通过自动化高通量和组合合成技术筛选HPs的综合策略。这种方法的一个关键方面是闭合表征循环，其中机器学习（ML）和人类协作起着关键作用。通过利用人类创造力和领域知识进行假设生成，并利用ML有效地测试和完善这些假设，我们旨在加速在特定环境条件下HPs的发现和优化。这种协同作用能够快速识别最有前景的材料，从基础发现推进到可扩展的可制造性。我们这项工作的最终目标是从实验室规模的创新过渡到实际应用，确保HPs能够有效地应用于缓解全球变暖的技术中，例如太阳能收集和甲烷去除系统。
