论文标题
可扩展的多代理实验室框架用于实验室优化
Scalable Multi-Agent Lab Framework for Lab Optimization
论文作者
论文摘要
自主材料研究系统使科学家能够更聪明，学习速度更快，并在其研究中花费更少的资源。随着这些系统的数量，能力和复杂性的增长，出现了一个新的挑战 - 它们将如何在大型设施中共同工作？我们探索了这个问题的一种解决方案 - 多代理实验室控制框架。我们想到了一个自主材料科学实验室来证明这一框架 - 可以将来自不同研究活动的信息组合起来，以使眼前的科学问题结合起来。该框架可以1）考虑到现实的资源限制，例如设备使用，2）允许具有多种学习能力和能够运行重新搜索活动的目标的机器学习代理，以及3）促进多机构的协作和团队。该框架被称为多代理自主设施 - 可扩展的框架又称多任务。多任务可以使设施范围内的模拟，包括代理启动和代理代理交互。通过多任务的模块化，现实世界中的设施可以阶段在线，模拟仪器逐渐被现实世界的仪器取代。我们希望多任务在大规模的自主和半自治研究活动和设施中开放新的研究领域。
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises - how will they work together across large facilities? We explore one solution to this question - a multi-agent laboratory control frame-work. We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand. This framework can 1) account for realistic resource limits such as equipment use, 2) allow for machine learning agents with diverse learning capabilities and goals capable of running re-search campaigns, and 3) facilitate multi-agent collaborations and teams. The framework is dubbed the MULTI-agent auTonomous fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK makes possible facility-wide simulations, including agent-instrument and agent-agent interactions. Through MULTITASK's modularity, real-world facilities can come on-line in phases, with simulated instruments gradually replaced by real-world instruments. We hope MULTITASK opens new areas of study in large-scale autonomous and semi-autonomous research campaigns and facilities.
