WP5 Dynamic integration of experimental and computational workflows for federated SDLs

WP5 Dynamic integration of experimental and computational workflows for federated SDLs ID: OSW22a934c3d6c95328b07f3a4c176f6cb2 | UUID: 22a934c3-d6c9-5328-b07f-3a4c176f6cb2
ID	OSW22a934c3d6c95328b07f3a4c176f6cb2
UUID	22a934c3-d6c9-5328-b07f-3a4c176f6cb2
Label	WP5 Dynamic integration of experimental and computational workflows for federated SDLs
Short name	WP5
Machine compatible name	Wp5DynamicIntegrationOfExperimentalAndComputationalWorkflowsForFederatedSdls
Types/Categories	Work package
Statements (outgoing)
Statements (incoming)
Details
Types/Categories<br>Definition: Entity, Item, Process, Work package: Work package Description<br>Definition: Entity: WP5 Dynamic integration of experimental and computational workflows for federated SDLs. Lead: DTU, co-lead IMDEA. Label(s)At least one label is required.<br>Definition: Entity: WP5 Dynamic integration of experimental and computational workflows for federated SDLs Short name(s)Abbreviation, Acronym, etc.<br>Definition: Entity: WP5 Actionees<br>Definition: Process: Tejs Vegge Maciej Haranczyk Belongs to (project)Project that this work package belongs to.<br>Definition: Work package: MaterialsCommons

WP5 Dynamic integration of experimental and computational workflows for federated SDLs. Lead: DTU, co-lead IMDEA.

Objectives

• Development of a dynamic and interoperable Workflow-of-Workflows (WoW) environment that automates complex experimental, computational, and machine learning tasks across a pan-European network of distributed Self-Driving Laboratories (SDLs). Each SDL node provides access to specialized equipment for synthesis, characterization, or simulation. The WoW framework enables asynchronous task deployment, meaning tasks can be executed independently and flexibly across different labs. A key feature is its ability to select the most suitable lab or tool, e.g., a specific instrument or ML model, based on which can deliver the most valuable data for optimizing a material or device-level performance. This decision-making is guided by an uncertainty-aware optimization model, ensuring efficient use of resources and accelerating cross-lab validation and innovation.

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