From Platform to Knowledge Graph: Evolution of Laboratory Automation

    • Jiaru Bai1
    • Liwei Cao1
    • Sebastian Mosbach1, 2
    • Jethro Akroyd1, 2
    • Alexei A. Lapkin1, 2
    • Markus Kraft1, 2, 3, 4
    • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom
    • 2CARES, Cambridge Centre for Advanced, Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore, 138602
    • 3School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459
    • 4The Alan Turing Institute, London, NW1 2DB, United Kingdom

    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.

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