Multi-task scheduling of self-driving laboratories under scientific constraints
TL;DR
This paradigm establishes a practical scheduling framework for concurrent multitasking in SDLs, ensuring the experimental consistency required to generate the high-fidelity datasets foundational to autonomous, AI-driven scientific discovery.
Abstract
Self-driving laboratories (SDLs) integrate automation, robotics, and artificial intelligence to autonomously execute scientific experiments. As SDLs evolve toward concurrent multi-task execution and support for heterogeneous experiments, experimental scheduling becomes a critical decision-making layer, determining the temporal order and execution timing of operations under strict scientific constraints, including operation precedence, station allocation, batch processing, experimental parameters, and critically, time synchronization between consecutive operations. For example, in inorganic synthesis, temporal deviations during nucleation can fundamentally alter material properties. While SDLs offer a promising route toward accelerated discovery, the absence of explicit scheduling for concurrent experiments leads to resource conflicts and uncontrolled interruptions. These deviations undermine the reproducibility and data quality essential for artificial intelligence modeling. Here, we present a multi-task scheduling algorithm that jointly accounts for scientific constraints. We integrated this algorithm into an SDL using a closed-loop communication architecture that enables tight coordination between scheduling and robotic experiment execution. The algorithm was validated through the concurrent multi-task synthesis of gold nanoparticles and metal–organic frameworks, distinct chemical reactions governed by nucleation and growth kinetics. By synchronizing robotic operations with experimental stations as well as chemical workflow, the algorithm preserves chemical fidelity and ensures consistent material quality across concurrent multi-task executions, unattainable with a conventional scheduling algorithm that does not adequately account for scientific constraints. This paradigm establishes a practical scheduling framework for concurrent multitasking in SDLs, ensuring the experimental consistency required to generate the high-fidelity datasets foundational to autonomous, AI-driven scientific discovery.