A mobile robotic chemist
A mobile robotic chemist
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DOI：
10.1038/s41586-020-2442-2
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发表时间：
2020-07-09
期刊：
影响因子：
64.8
通讯作者：
Cooper, Andrew I.
中科院分区：
文献类型：
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作者：
Burger, Benjamin;Maffettone, Phillip M.;Cooper, Andrew I.
诸如电池、生物材料和多相催化剂等技术具有由分子和介观组分的混合物定义的功能。到目前为止，这种多长度尺度的复杂性还无法通过原子模拟完全捕捉，并且根据第一原理设计此类材料的情况仍然很少（1-5）。同样，实验的复杂性随着变量的数量呈指数级增长，这将大多数搜索限制在材料空间的狭窄区域。机器人可以协助实验搜索（6-14），但由于所需的样品类型、操作、仪器和测量的多样性，它们在材料研究中的广泛采用具有挑战性。在这里，我们使用一个移动的机器人来寻找改进的光催化剂，用于从水制氢（15）。该机器人自主运行了8天，在10个变量的实验空间内进行了688次实验，由批量贝叶斯搜索算法驱动（16-18）。这种自主搜索确定了比初始配方活性高六倍的光催化剂混合物，选择了有益成分并取消了负面成分。我们的策略是使用一个灵巧的自由漫游机器人（21-24），使研究人员而不是仪器自动化。这种模块化的方法可以部署在传统的实验室，用于一系列超出计算机的研究问题。
Technologies such as batteries, biomaterials and heterogeneous catalysts have functionsthat are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare(1-5). Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches(6-14)but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water(15). The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm(16-18). This autonomous search identified photocatalyst mixturesthat were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous(19,20)free-roaming robot(21-24), automating the researcher ratherthan the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.