Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation
Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation
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DOI：
10.1016/j.pmatsci.2022.101043
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发表时间：
2022-11
影响因子：
37.4
通讯作者：
Yunchao Xie;Kianoosh Sattari;Chi Zhang;Jian Lin
中科院分区：
文献类型：
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作者：
Yunchao Xie;Kianoosh Sattari;Chi Zhang;Jian Lin
对具有上级性能的新型材料的需求不断增长，激发了人工智能和自动化时代对传统研究范式的改造。自主实验平台（AEP）已经成为一个令人兴奋的研究前沿，它通过将数据驱动的算法（如机器学习（ML））与材料开发循环中的实验自动化（从合成，表征和分析到决策）相结合，实现了完全的自主性。在这篇综述中，我们首先介绍了如何开发数据驱动算法来解决材料问题。然后，我们系统地总结了自动化材料合成，ML使能的数据分析和决策的最新进展。最后，我们讨论了奋进开发下一代AEP以最终实现自动驾驶或自动驾驶实验室的挑战和机遇。这篇综述将为研究人员提供见解，旨在了解ML在材料科学中的前沿，并在实验室中部署AEP以加速材料开发。
The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomyviaintegrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. This review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.