A dynamic knowledge graph approach to distributed self-driving laboratories
The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed se...
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Engineering Knowledge Laboratory Bai, Jiaru Mosbach, Sebastian Taylor, Connor J. Karan, Dogancan Lee, Kok Foong Rihm, Simon D. Akroyd, Jethro Lapkin, Alexei A. Kraft, Markus A dynamic knowledge graph approach to distributed self-driving laboratories |
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The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Bai, Jiaru Mosbach, Sebastian Taylor, Connor J. Karan, Dogancan Lee, Kok Foong Rihm, Simon D. Akroyd, Jethro Lapkin, Alexei A. Kraft, Markus |
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Article |
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Bai, Jiaru Mosbach, Sebastian Taylor, Connor J. Karan, Dogancan Lee, Kok Foong Rihm, Simon D. Akroyd, Jethro Lapkin, Alexei A. Kraft, Markus |
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Bai, Jiaru |
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A dynamic knowledge graph approach to distributed self-driving laboratories |
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A dynamic knowledge graph approach to distributed self-driving laboratories |
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A dynamic knowledge graph approach to distributed self-driving laboratories |
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A dynamic knowledge graph approach to distributed self-driving laboratories |
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A dynamic knowledge graph approach to distributed self-driving laboratories |
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dynamic knowledge graph approach to distributed self-driving laboratories |
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2024 |
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https://hdl.handle.net/10356/174919 |
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sg-ntu-dr.10356-1749192024-04-16T04:24:11Z A dynamic knowledge graph approach to distributed self-driving laboratories Bai, Jiaru Mosbach, Sebastian Taylor, Connor J. Karan, Dogancan Lee, Kok Foong Rihm, Simon D. Akroyd, Jethro Lapkin, Alexei A. Kraft, Markus School of Chemical and Biomedical Engineering Cambridge Centre for Advanced Research and Education in Singapore Engineering Knowledge Laboratory The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days. National Research Foundation (NRF) Published version 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”. This project was cofunded by European Regional Development Fund via the project “Innovation Centre in Digital Molecular Technologies”, UKRI via project EP/S024220/1 “EPSRC Centre for Doctoral Training in Automated Chemical Synthesis Enabled by Digital Molecular Technologies”. Part of this work was also supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1. J.B. acknowledges financial support provided by CSC Cambridge International Scholarship from Cambridge Trust and China Scholarship Council. C.J.T. is a Sustaining Innovation Postdoctoral Research Associate at Astex Pharmaceuticals and thanks Astex Pharmaceuticals for funding. S.D.R. acknowledges financial support from Fitzwilliam College, Cambridge, and the Cambridge Trust. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation. 2024-04-16T04:24:11Z 2024-04-16T04:24:11Z 2024 Journal Article Bai, J., Mosbach, S., Taylor, C. J., Karan, D., Lee, K. F., Rihm, S. D., Akroyd, J., Lapkin, A. A. & Kraft, M. (2024). A dynamic knowledge graph approach to distributed self-driving laboratories. Nature Communications, 15(1), 462-. https://dx.doi.org/10.1038/s41467-023-44599-9 2041-1723 https://hdl.handle.net/10356/174919 10.1038/s41467-023-44599-9 38263405 2-s2.0-85183030587 1 15 462 en Nature Communications © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. application/pdf |