Toward autonomous design and synthesis of novel inorganic materials.
Toward autonomous design and synthesis of novel inorganic materials.
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DOI：
10.1039/d1mh00495f
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发表时间：
2021-05
影响因子：
13.3
通讯作者：
N. Szymanski;Yan Zeng;Haoyan Huo;Christopher J. Bartel;Haegyeom Kim;G. Ceder
中科院分区：
文献类型：
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作者：
N. Szymanski;Yan Zeng;Haoyan Huo;Christopher J. Bartel;Haegyeom Kim;G. Ceder
由人工智能（AI）驱动的自主实验为无机材料的发现和开发带来了令人兴奋的机会。在此，我们回顾了自动驾驶实验室设计的最新进展，包括自动化材料合成和表征的机器人技术，以及人工智能解释实验结果并提出新的实验程序。我们专注于通过基于溶液的路线，固态反应和薄膜沉积自动化无机合成的努力。在每一种情况下，都与有机化学中的相关工作建立了联系，在有机化学中，自动化更为常见。表征技术主要在相鉴定的背景下进行讨论，因为这项任务对于了解合成过程中形成的产物至关重要。研究了深度学习在分析多变量表征数据和执行相位识别方面的应用。为了实现“闭环”材料合成和设计，我们进一步提供了使用主动学习来合理指导实验迭代的优化算法的详细概述。最后，我们强调了自动驾驶无机材料合成平台未来发展的几个关键机遇和挑战。
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.