Digital Twins
A dynamic knowledge graph approach to distributed self-driving laboratories
Jiaru Bai, Computational Modelling Group, Department of Chemical Engineering and Biotechnology, University of Cambridge
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.
Automated laboratory management, powered by The World Avatar™ ecosystem
Dr Amit Bhave, CMCL
CMCL is an award-winning company offering digital engineering solutions across energy, infrastructure, chemicals/materials and automotive sectors. The talk introduces the application of a holistic digital laboratory framework, integrated into The World Avatar™ ecosystem, to accelerate scientific discovery by automating experimental research. The framework, developed to address challenges in interoperability and adaptability, employs a hierarchical and semantic structure within interconnected dynamic knowledge graphs. This approach facilitates deep knowledge representation, allowing for efficient resource distribution, improved reproducibility, and cost effectiveness. The framework is extended to specialised facilities, such as research laboratories, demonstrating its versatility in digitalizing and automating management. Key features include seamless data sharing, computational reasoning, and gradual task automation, enhancing efficiency and sustainability. The talk emphasizes the breakthroughs achieved, including IoT sensor network integration, airflow management, and semantic linking of BIM and BMS data, showcasing the potential for operational excellence and sustainability in future research facilities.