Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA.
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Nature. 2023 Dec;624(7990):86-91. doi: 10.1038/s41586-023-06734-w. Epub 2023 Nov 29.
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
为了缩小计算筛选和新型材料实验实现之间的差距，我们引入了 A-Lab，这是一个用于无机粉末固态合成的自动化实验室。该平台使用计算、文献中的历史数据、机器学习 (ML) 和主动学习来规划和解释使用机器人执行的实验结果。在连续 17 天的运行中，A-Lab 从 58 个目标中实现了 41 种新型化合物，其中包括使用来自 Materials Project 和 Google DeepMind 的大规模从头算相稳定性数据识别的各种氧化物和磷酸盐。合成配方是由针对文献训练的自然语言模型提出的，并使用基于热力学的主动学习方法进行优化。对失败合成的分析为改进当前的材料筛选和合成设计技术提供了直接和可行的建议。高成功率证明了人工智能驱动的平台在自主材料发现方面的有效性，并激发了进一步整合计算、历史知识和机器人技术。
