The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Abstract

In this work, we highlight the need for dynamic workflow managers in self‐driving laboratories (SDLs) and optimizers for Materials Acceleration Platforms (MAPs). While MAPs leverage artificial intelligence (AI) and automation to accelerate materials discovery, static workflow managers limit scalability, and flexibility. We demonstrate the benefits of modular, adaptable orchestrators by integrating PerQueue, a dynamic workflow manager, into a color‐mixing robot, streamlining task coordination and AI‐driven optimization. Additionally, we assess various MAP‐relevant methodologies based on their maturity and readiness for integration. This assessment underlines the importance of flexible workflow managers in advancing SDLs and optimizers, paving the way for more effective future MAP implementations. Achieving seamless integration requires addressing challenges like ensuring data provenance, adhering to findable, accessible, interoperable, reusable principles, and adapting workflows to changing experimental conditions—key elements for the successful deployment of MAPs.