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，可用于制造低成本和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提供了一种潜在的可扩展方法（例如，在移动的电话中或附着在衣服上），其收集关于人类暴露于空气中的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相关的健康风险、制定最大限度地减少工人暴露于危险环境的工业实践以及检测点源（例如，制造爆炸物）。当传感器暴露于化学环境时，液晶传感器通过将分子水平上发生的事件放大为光信号来工作。放大过程涉及一系列跨越多个长度和时间尺度的紧密耦合现象。这种尺度的跨度超出了目前可以直接从第一原理描述、建模和预测的范围。该项目旨在将联合收割机第一原理和数据驱动方法相结合，以克服这一技术挑战。所开发的方法将能够预测液晶设计变量对光信号的信息内容的影响，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，将数据科学融入先进功能材料的设计和分析。K-12学生和公众将通过开发对模型目标化学品（例如，该项目将研究可扩展的机器学习技术，这些技术能够有效地使用大量的实验和第一原理模拟数据，以发现和理解控制液晶性能的多尺度现象。具体而言，该项目的目标是：i）研究密度泛函理论和分子动力学模拟的使用，以确定发生在液晶界面内和液晶界面处的潜在时空事件的纳米级描述符（例如，结合能），ii）建立特征提取技术以识别液晶光信号的合适的宏观尺度描述符（例如，光学响应时间和纹理场），以及iii）开发能够创建能够映射纳米尺度和宏观尺度描述符的多尺度模型的机器学习技术。这些功能将结合在一个强化学习框架中，这将有助于指导实验数据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，以推断涉及一氧化碳、臭氧、氮和硫氧化物的暴露事件。该奖项反映了NSF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.