Collaborative Research: Decoding the Corrosion of Borate Glasses: From Fundamental Science to Quantitative Structure-Property Relationships

非技术描述：玻璃的化学耐久性是当今人们感兴趣的话题； fundamental understanding is of paramount importance to the glass industry and to the pursuit of overcoming various challenges relevant to the well-being of humanity and the environment, including nuclear waste management and development of novel biomaterials.该项目旨在通过实验研究和人工智能的统一来了解控制多组分硼酸盐玻璃腐蚀的基础科学。该项目的成功完成预计将为理解和描述玻璃腐蚀中的成分-结构-性能关系奠定新的基础知识基础，并推进基于机器学习的新模型以快速可靠地预测硼酸盐玻璃的腐蚀行为。美国玻璃/材料行业正面临经验丰富的玻璃工程师/科学家的严重短缺。该项目通过培训玻璃科学与工程领域的本科生和研究生来减少这一短缺，从而为美国玻璃/材料行业、学术界和国家实验室提供人才库。除了培训本科生和研究生科学与工程专业学生外，教育和推广活动还旨在激发初中和高中学生和教师的兴趣。技术细节：我们目前对玻璃腐蚀的理解主要基于经验数据，因为对于适用于广泛成分空间的玻璃溶解的主要机制尚未达成完全共识。因此，迫切需要对玻璃的化学成分、原子/分子结构和化学耐久性之间的联系进行深入、基本的了解，以解决关键且具有科学挑战性的问题（例如，设计具有所需化学耐久性的玻璃）。因此，该项目旨在结合实验研究和人工智能的优势，揭示决定硼酸盐玻璃在水性环境中溶解行为的潜在机制； and developing a cloud-based quantitative structure-property relationship (QSPR) model – powered by theory-guided machine learning engine – to predict the time-dependent corrosion behavior of oxide glasses.该项目采用了符合美国材料基因组计划的材料设计方法，是一项开创性的工作，代表了设计具有受控化学耐久性的氧化物玻璃方面的飞跃。除了揭示玻璃腐蚀的基本驱动因素和推进 QSPR 模型以可靠地预测玻璃腐蚀之外，该项目的一个重要成果是开发了在玻璃/材料科学和机器学习方面受过良好培训的本科生和研究生人才管道。 Further, the project's education plan incorporates a foundational, spiral approach that builds interest at the elementary, middle, and high school level students.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.

NON-TECHNICAL DESCRIPTION: Chemical durability of glass is a topic of interest today; fundamental understanding is of paramount importance to the glass industry and to the pursuit of overcoming various challenges relevant to the well-being of humanity and the environment, including nuclear waste management and development of novel biomaterials. This project aims at understanding the fundamental science governing corrosion of multicomponent borate glasses, achieved through the unification of experimental studies and artificial intelligence. Successful completion of this project is expected to lay the foundation of new fundamental knowledge to understand and describe composition-structure-property relationships in glass corrosion, and advance new machine learning-based models to promptly and reliably predict the corrosion behavior of borate glasses. The U.S. glass/materials industry is facing a severe shortage of experienced glass engineers/scientists. The project reduces this shortage by training undergraduate and graduate students in glass science and engineering, thus providing a talent pool for the U.S. glass/materials industry, academia, and national laboratories. The education and outreach activities are designed to invoke interest in students and teachers at the middle and high school levels, in addition to the training of undergraduate and graduate science and engineering students. TECHNICAL DETAILS: Our current understanding of glass corrosion is based primarily on empirical data, as there is still no complete consensus on the primary mechanism of glass dissolution that applies across a wide composition space. Therefore, there is an exigent need to develop robust, fundamental understanding of the linkage(s) between chemical composition, atomic/molecular structure, and chemical durability of glasses in order to address crucial and scientifically challenging problems (e.g., designing glasses with desired chemical durability). Accordingly, the project aims at combining the strengths of experimental studies and artificial intelligence to reveal the underlying mechanisms that dictate the dissolution behavior of borate glasses in aqueous environments; and developing a cloud-based quantitative structure-property relationship (QSPR) model – powered by theory-guided machine learning engine – to predict the time-dependent corrosion behavior of oxide glasses. Enabling the materials-by-design approach – which is in alignment with the U.S. Materials Genome Initiative – this project is a pioneering effort, representing a leap forward in designing oxide glasses with controlled chemical durability. Apart from revealing fundamental drivers of glass corrosion and advancing a QSPR model to reliably predict glass corrosion, a significant outcome of the project is the development of a talent pipeline of undergraduate and graduate students well-trained in glass/materials science and machine learning. Further, the project's education plan incorporates a foundational, spiral approach that builds interest at the elementary, middle, and high school level students.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.