这篇论文教你怎么让AI实验室同时用好多个仪器，不浪费时间，搞材料合成的朋友可以看看。
本文针对自主实验室中AI代理规划实验时的硬件资源约束问题，提出一种两步方法。第一步使用约束规划找到最优调度，最小化总时间并满足设备容量和限制。第二步为每个任务建立状态依赖系统，确保最优调度稳健执行。该方法在金属有机框架（MOF）合成平台上验证有效。
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
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.