Grizou Jonathan, Points Laurie J, Sharma Abhishek, Cronin Leroy
School of Chemistry, University of Glasgow, Joseph Black Building, University Avenue, Glasgow G12 8QQ, UK.
Sci Adv. 2020 Jan 31;6(5):eaay4237. doi: 10.1126/sciadv.aay4237. eCollection 2020 Jan.
We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.
我们描述了一种配备好奇算法（CA）的化学机器人助手，它能够有效地探索复杂化学系统可能呈现的状态。该CA机器人旨在以开放式方式探索配方，没有明确的优化目标。通过将CA机器人应用于自推进多组分水包油原细胞液滴的研究，在相同预算下，我们能够观察到比随机参数搜索更多一个数量级的液滴行为变化。我们证明，CA机器人能够观察到液滴对微小温度变化的突然且高度特异性的反应。利用时间-温度相图识别并分类了六种自推进液滴运动模式，并使用包括核磁共振（NMR）在内的多种技术进行了探测。这项工作说明了好奇算法如何能更好地利用有限的实验预算，并显著提高不可预测观察的速率，从而在配方化学中带来具有潜在应用价值的新发现。
