BIGDATA: IA: Collaborative Research: Data-Driven, Multi-Scale Design of Liquid Crystals for Wearable Sensors for Monitoring Human Exposure and Air Quality

液晶是一种响应材料，可用于制造低成本、高选择性的化学传感器。液晶为部署数百万可穿戴化学传感器（例如，在手机上或附着在衣服上）提供了一种潜在的可扩展方法，可以收集人体暴露于空气中有毒污染物的高分辨率数据。这些信息对于了解与空气质量有关的健康风险、制定尽量减少工人接触危险环境的工业做法以及探测点源（例如制造爆炸物）至关重要。液晶传感器的工作原理是，当传感器暴露在化学环境中时，将发生在分子水平上的事件放大为光信号。放大过程涉及一系列紧密耦合的现象，跨越多个长度和时间尺度。这个范围超出了目前可以直接从第一原理来描述、建模和预测的范围。该项目旨在结合第一原理和数据驱动的方法来克服这一技术挑战。所开发的方法将能够预测液晶设计变量对光学信号信息含量的影响，并将对化学传感技术和功能材料的设计产生革命性的影响。该项目的多学科性质将培养新一代工程师，将数据科学整合到先进功能材料的设计和分析中。K-12年级的学生和公众将参与开发对模型目标化学物质（例如苏打水中的二氧化碳）做出反应的动手液晶传感器。该项目将研究可扩展的机器学习技术，该技术能够有效地利用大量实验和第一性原理模拟数据，以发现和理解控制液晶性能的多尺度现象。具体来说，项目目标是：i)研究使用密度泛函理论和分子动力学模拟来识别液晶界面内部和界面上发生的潜在时空事件的纳米尺度描述符（例如结合能）；ii)建立特征提取技术来识别液晶光学信号的合适宏观尺度描述符（例如光学响应时间和纹理场）；iii)开发机器学习技术，使创建能够映射纳米尺度和宏观尺度描述符的多尺度模型成为可能。这些能力将结合在一个强化学习框架中，这将有助于指导实验数据的收集和创新液晶系统设计的识别。该项目的最终工程目标是设计LC传感器来推断涉及一氧化碳、臭氧、氮和硫氧化物的暴露事件。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Liquid crystals are responsive materials that can be used to manufacture low-cost and highly selective chemical sensors. Liquid crystals provide a potentially scalable approach toward deploying millions of wearable chemical sensors (e.g., in mobile phones or attached to clothing) that collect high-resolution data on human exposure to toxic contaminants in the air. This information is key to understanding health-risks associated with air quality, developing industrial practices that minimize workers' exposure to hazardous environments, and detecting point sources (e.g., fabrication of explosives). Liquid crystal sensors work by amplifying events that occur at the molecular-level into an optical signal when the sensor is exposed to a chemical environment. The amplification process involves a sequence of tightly coupled phenomena spanning multiple length and time scales. This span in scales lies beyond what is currently possible to characterize, model, and predict directly from first principles. This project seeks to combine first-principles and data-driven methodologies to overcome this technical challenge. The methods developed will enable the prediction of the influence of liquid crystal design variables on the information content of optical signals and will lead to a revolutionary impact on chemical sensing technologies and on the design of functional materials. The multidisciplinary nature of this project will train a new generation of engineers in the integration of data science into the design and analysis of advanced functional materials. K-12 students and the public will be engaged through development of hands-on liquid crystal sensors that respond to model target chemicals (e.g., carbon dioxide from sodas).The project will investigate scalable machine learning techniques that enable the efficient use of large sets of experimental and first-principles simulation data to uncover and understand multi-scale phenomena that govern the performance of liquid crystals. Specifically, the project goals are to: i) Investigate the use of density functional theory and molecular dynamics simulations to identify nanoscale descriptors of the underlying spatiotemporal events occurring within and at liquid crystal interfaces (e.g., binding energies), ii) Establish feature extraction techniques to identify suitable macroscale descriptors of liquid crystal optical signals (e.g., optical response times and texture fields), and iii) Develop machine learning techniques that enable the creation of multi-scale models capable of mapping nanoscale and macroscale descriptors. These capabilities will be combined in a reinforcement learning framework that will help guide experimental data collection and identification of innovative liquid crystal system designs. The ultimate engineering goal of the project is to design LC sensors to infer exposure events involving carbon monoxide, ozone, and nitrogen and sulfur oxide.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.