Optimal Resource Utilization for Autonomous Laboratory Orchestrators
2026-07-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors worked on making sure their autonomous lab robots use all their machines efficiently when planning experiments. They created a two-step method: first, a computer program finds the best schedule to finish tasks quickly without overloading any machines. Then, they built a system that tracks the progress of each task to make sure the plan runs smoothly in real life. This helps their lab handle complex experiments with different machines more effectively.
autonomous laboratoriesAI agentsconstraint programmingschedulingresource utilizationmetal-organic frameworkshardware constraintstask executionstatus dependencies
Authors
Austin McDannald, Julia Tisaranni, Howie Joress
Abstract
In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real-world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2-step method to address resource utilization for our autonomous platform for metal-organic framework synthesis. First, we use constraint programming to find optimal schedules. This finds schedules that minimizes the total time while still satisfying the limitations and capacities of the hardware. Secondly, we use a system of status dependencies for each task, which allows for the robust execution of the optimal schedules.