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From Platform to Knowledge Graph: Evolution of Laboratory Automation

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Cao, liwei 
Mosbach, sebastian 
Akroyd, jethro 
Lapkin, alexei 


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 towards 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 standardised communication protocols are indispensable. In this perspective, we recategorise 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 hypothesise 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 laboratory.


Funder: Alexander von Humboldt-Stiftung


Knowledge graph, digital twin, chemistry digitalization, closed-loop optimization, laboratory automation

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American Chemical Society (ACS)
Engineering and Physical Sciences Research Council (EP/R029369/1)
Engineering and Physical Sciences Research Council (EP/R009902/1)
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and Pharma Innovation Platform Singapore (PIPS) via grant to CARES Ltd “Data2Knowledge, C12”. The authors are grateful to EPSRC (grant number: EP/R029369/1) and ARCHER for financial and computational support as a part of their funding to the UK Consortium on Turbulent Reacting Flows ( This work was co-funded by EPSRC (grant number: EP/R009902/1) “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products”. The authors thank Dr Jacob W. Martin for his advice on information management. The authors thank Dr Andrew C. Breeson for his help with proofreading. The authors thank Yiqun Bian and Guanhua Li for their helpful recommendations and feedback on colour scheme, which helped to improve the overall aesthetic expression of the TOC graphic. J. Bai acknowledges financial support provided by CSC Cambridge International Scholarship from Cambridge Trust and China Scholarship Council. M. Kraft gratefully acknowledges the support of the Alexander von Humboldt Foundation.