CDS&E: Statistical Learning Tools for NMR Spectroscopy of Non-Crystalline Materials

在化学学部化学测量与成像项目的支持下，以及材料研究学部陶瓷项目的部分共同资助下，俄亥俄州立大学的Philip Grandinetti教授和他的团队正在开发机器学习工具，以利用核磁共振（NMR）光谱学（核磁共振成像（MRI）的基础技术）提高对含玻璃材料的物理和化学性质的理解。特种玻璃继续在广泛的技术应用中发挥关键作用，例如手持式电子设备显示和照明的玻璃基板，光纤，核废料储存和生物玻璃植入物。这些应用在广泛的环境、能源和健康相关问题上具有很高的社会影响。定制新玻璃组合物性能的一个主要挑战是关于玻璃结构的可用定量细节的不足，这决定了它们的宏观（体积）性能。格兰迪内蒂教授正在开发更灵敏的方法和开源软件工具，对核磁共振数据进行更深入的分析，并提供有关玻璃材料结构的更丰富细节。这项工作解决了一系列决定玻璃性能的因素，如尺寸稳定性、强度、相分离、硬度和化学耐久性。它通过与肯塔基州伯里亚学院（Berea College）的合作，为来自代表性不足群体的学生提供研究机会。合作为所有参与项目的学生提供了与工业界和跨国界科学家互动的机会。该项目的重点是解决核磁共振谱反演到核相互作用参数底层分布的病态问题，然后将这些参数定量映射到结构分布中。在这项工作中，Philip Grandinetti教授和他的团队正在开发开源的Python程序、文档和教程，以及相关的渐进式网络应用程序，以实现对实验一维和高维固态核磁共振光谱的快速、易于使用和通用的模拟和分析。他们利用他们最近的发现，四极核的高选择性激发可以延长核磁共振跃迁寿命，并提供显着的灵敏度增强。反过来，这一进展有望使统计学习工具扩展应用于无机氧化物材料的天然丰度O-17二维核磁共振光谱。对一系列碱土和碱土硅酸盐玻璃中改性剂阳离子聚类和四面体框架网络无序的定量研究是另一个目的。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。

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, and partial co-funding from the Ceramics Program in the Division of Materials Research, Professor Philip Grandinetti and his group at the Ohio State University are developing machine learning tools to improve understanding of the physical and chemical properties of glass-containing materials using Nuclear Magnetic Resonance (NMR) Spectroscopy – the technique upon which Magnetic Resonance Imaging (MRI) is based. Specialty glasses continue to play critical roles in a large range of technological applications, such as glass substrates for handheld electronic device displays and lighting, optical fibers, nuclear waste storage, and bio-glass implants. These applications have high societal impact across a wide range of environmental, energy, and health-related issues. A major challenge in tailoring the properties of new glass compositions is the inadequacy of available quantitative details about the structure of glasses, which determines their macroscopic (bulk) properties. Professor Grandinetti is developing more sensitive methods and open-source software tools that perform a deeper analysis of NMR data and give richer details about structure in glassy materials. The work addresses a range of factors determining glass properties such as dimensional stability, strength, phase separation, hardness, and chemical durability. It is providing research opportunities for students from underrepresented groups, in part through a partnership with Berea College in Kentucky. Collaborations provide all students involved in the project with opportunities for interactions with scientists in industry as well as across national boundaries. This project focuses on solving the ill-posed problem of inverting an NMR spectrum into its underlying distribution of nuclear interaction parameters, followed by a quantitative mapping of these parameters into structural distributions. In this effort, Professor Philip Grandinetti and his group are developing open-source Python programs, documentation, and tutorials, and associated progressive web apps to enable fast, easy-to-use, and versatile simulations and analyses of experimental one- and higher-dimensional solid-state NMR spectra. They capitalize on their recent discovery that highly selective excitation of quadrupolar nuclei can extend NMR transition lifetimes and provide dramatic sensitivity enhancements. This advance in turn is expected to enable expanded applications of the statistical learning tools to natural abundance O-17 2D NMR spectra of inorganic oxide materials. Quantification of modifier cation clustering and tetrahedral framework network disorder in a series of alkali and alkaline earth silicate glasses is another aim.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.