From Platform to Knowledge Graph: Evolution of Laboratory Automation.

From Platform to Knowledge Graph: Evolution of Laboratory Automation.
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DOI:
10.1021/jacsau.1c00438
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发表时间:
2022-02-28
期刊:
影响因子:
8
通讯作者:
Kraft M
Kraft M
中科院分区:
其他
文献类型:
--
作者:
Bai J;Cao L;Mosbach S;Akroyd J;Lapkin AA;Kraft M

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随着计算能力和人工智能工具的发展,高保真计算机辅助实验变得越来越容易获得。实验硬件的进步也使研究人员能够达到过去不可能达到的准确度。在迈向下一代自动驾驶实验室的过程中,这两种资源的协调是化学科学中自主发现的焦点。为了实现这一目标,算法可访问的数据表示和标准化的通信协议是必不可少的。从这个角度来看,我们重新归类为五个功能组件的材料加速平台的基础上,最近推出的方法,并讨论了最近的案例研究,侧重于不同组件之间的数据表示和交换方案。新兴的技术,可互操作的数据表示和多智能体系统也讨论了他们最近在化工自动化的应用。我们假设,知识图技术,编排语义网络技术和多智能体系统,将是驱动力,使数据的知识,不断发展我们的方式自动化实验室。
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.
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