Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS One. 2020 Apr 16;15(4):e0229862. doi: 10.1371/journal.pone.0229862. eCollection 2020.
The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.
当前的爱迪生式发现方法需要长达二十年的基础和应用研究，才能使材料技术进入市场。如此缓慢且资本密集的周转需要颠覆性策略来加速创新。自动驾驶实验室有可能通过人工智能为自动化提供动力，从而实现实验的变革，从而实现自主发现。但是，缺乏足够的软件解决方案严重阻碍了自动驾驶实验室的发展。在本文中，我们朝着解决这一挑战取得了进展，我们提出并开发了 ChemOS 的实现；一个可移植，模块化和通用的软件包，它为自动驾驶实验室的部署和运行提供了必要的结构化层。ChemOS 便于自动化设备的集成，并且能够远程控制自动化实验室。ChemOS 可以在不同程度的自主性下运行；从完全无人监督的实验到积极地将研究人员的投入和反馈纳入实验循环。ChemOS 的灵活性提供了广泛的功能，在五个应用程序上得到了证明，这些应用程序在不同的自动化设备上执行，突出了软件包的各个方面。
