Martin Tyler B, Sutherland Duncan R, McDannald Austin, Kusne A Gilad, Beaucage Peter A
Materials Science & Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.
Chem Mater. 2025 Jun 6;37(12):4272-4281. doi: 10.1021/acs.chemmater.5c00860. eCollection 2025 Jun 24.
The pace of soft material formulation (re)-development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the microstructure of liquid formulations using small-angle neutron and X-ray scattering and, soon, a variety of other techniques. Here, we describe the design, philosophy, tuning, and validation of our active learning agent that guides the course of AFL experiments. We show how our extensive tuning results in an efficient agent that is robust to both the number of measurements and signal-to-noise variation. Finally, we experimentally validate our virtually tuned agent by addressing a model formulation problem: replacing a petroleum-derived component with a natural analog. We show that the agent efficiently maps both formulations and how post hoc analysis of the measured data reveals the opportunity for further specialization of the agent. With the tuned and proven active learning agent, our autonomously guided AFL platform will accelerate the pace of discovery of liquid formulations and help speed us toward a greener future.
随着消费者和新法规都要求产品在保持高性能标准的同时减少对环境的危害，软材料配方（重新）开发和设计的步伐正在迅速加快。为满足这一需求，我们开发了自主配方实验室（AFL），这是一个能够使用小角中子散射和X射线散射自动制备和测量液体制剂微观结构的平台，并且很快还将应用多种其他技术。在此，我们描述了指导AFL实验进程的主动学习代理的设计、理念、调整和验证。我们展示了我们广泛的调整如何产生一个高效的代理，该代理对测量次数和信噪比变化都具有鲁棒性。最后，我们通过解决一个模型配方问题对虚拟调整后的代理进行实验验证：用天然类似物替代石油衍生成分。我们表明该代理能够有效地映射两种配方，并且对测量数据的事后分析揭示了该代理进一步专业化的机会。借助经过调整和验证的主动学习代理，我们的自主引导AFL平台将加快液体制剂的发现步伐，并帮助我们更快地迈向更绿色的未来。
