OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler

Abstract

The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery processes across diverse domains. However, the development of an operating system for material acceleration platform faces challenges in simultaneously managing diverse experiments from multiple users. Specifically, when it is utilized by multiple users, the overlapping challenges of experimental modules or devices can lead to inefficiencies in both resource utilization and safety hazards. To overcome these challenges, we present an operation control system for material acceleration platform, namely, OCTOPUS, which is an acronym for operation control system for task optimization and job parallelization via a user-optimal scheduler. OCTOPUS streamlines experiment scheduling and optimizes resource utilization through integrating its interface node, master node and module nodes. Leveraging process modularization and a network protocol, OCTOPUS ensures the homogeneity, scalability, safety and versatility of the platform. In addition, OCTOPUS embodies a user-optimal scheduler. Job parallelization and task optimization techniques mitigate delays and safety hazards within realistic operational environments, while the closed-packing schedule algorithm efficiently executes multiple jobs with minimal resource waste. Copilot of OCTOPUS is developed to promote the reusability of OCTOPUS for potential users with their own sets of lab resources, which substantially simplifies the process of code generation and customization through GPT recommendations and client feedback. This work offers a solution to the challenges encountered within the platform accessed by multiple users, and thereby will facilitate its widespread adoption in material development processes.