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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Main Authors: Bai, Jiaru, Mosbach, Sebastian, Taylor, Connor J., Karan, Dogancan, Lee, Kok Foong, Rihm, Simon D., Akroyd, Jethro, Lapkin, Alexei A., Kraft, Markus
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/10356/174919
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-174919
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Knowledge
Laboratory
spellingShingle 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
description 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.
author2 School of Chemical and Biomedical Engineering
author_facet 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
format Article
author Bai, Jiaru
Mosbach, Sebastian
Taylor, Connor J.
Karan, Dogancan
Lee, Kok Foong
Rihm, Simon D.
Akroyd, Jethro
Lapkin, Alexei A.
Kraft, Markus
author_sort Bai, Jiaru
title A dynamic knowledge graph approach to distributed self-driving laboratories
title_short A dynamic knowledge graph approach to distributed self-driving laboratories
title_full A dynamic knowledge graph approach to distributed self-driving laboratories
title_fullStr A dynamic knowledge graph approach to distributed self-driving laboratories
title_full_unstemmed A dynamic knowledge graph approach to distributed self-driving laboratories
title_sort dynamic knowledge graph approach to distributed self-driving laboratories
publishDate 2024
url https://hdl.handle.net/10356/174919
_version_ 1814047302778617856
spelling 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