CAREER: Understanding Grain Boundary Strength via Adaptive Electron Backscatter Diffraction and Multiscale Analysis

人类使用金属和合金已有数千年的历史，并通过开发新的制造方法不断提高其强度和性能。这种改进金属的过程在很大程度上依赖于猜测和检查。虽然人们已经变得更善于猜测，更快地检查，但智能设计更坚固，更安全，更有弹性的金属的基础知识仍然缺失。改进金属和合金的部分挑战在于，它们的性能取决于数万亿个缺陷的类型、密度和分布，这些缺陷比几个原子大不了多少。这些缺陷的两种重要类型是位错，其允许金属变形，以及晶界，其在位错移动通过金属时充当位错的屏障。晶界的特性影响位错在材料中移动的难易程度，进而影响金属的强度。然而，晶界的性质和它对位错运动的阻挡有多强之间的直接联系还没有建立。通过在百万分之一米或更小的超小长度尺度上观察晶界，该项目将建立这种直接联系。要做到这一点，电子显微镜，能够成像材料下降到单个原子的水平，将被用来看看位错如何积累在材料附近的晶界，而材料正在弯曲。人工智能（AI）将被内置到电子显微镜中，以便快速自动地探索成千上万的晶界，以获得对晶界特征如何与其强度相关联的统计理解。这种理解将有助于指导新金属和合金的开发，这些金属和合金在应用中更坚固，更安全，更持久。这项工作的更广泛的推广包括将来自代表性不足社区的高中生纳入暑期实习研究计划。这些实习生将与该计划支持的研究生密切合作，调查金属的强度，并将制定包含虚拟现实元素的课程计划，以便在下一学年带回课堂。暑期实习还将包括参观Novelis研究中心，这是一家全球性的铝公司，总部位于亚特兰大附近。技术概述晶界在决定金属和合金的机械行为和失效敏感性方面的核心作用早已被认识到。然而，努力了解晶界特性的变化如何影响材料性能受到阻碍，是什么决定了个别晶界的强度的不完全理解。本项目的目的是确定决定晶界强度的特性，这里定义为晶界对位错传播的阻挡强度。将开发一种新的基于自适应网格重划分电子背散射衍射（AR-EBSD）的方法，将在线处理和自动自适应网格重划分相结合，以快速对构建机器学习方法可以应用的库所需的数万个晶界进行采样。这种方法将与透射电子显微镜（TEM）表征和原子模拟相关联的晶界强度与位错转移机制。这种耦合的方法将有利于一个前所未有的探索晶界空间方面的晶界调查的数量，允许严格的晶界强度函数建立。此外，在拟议的工作过程中开发的多尺度电子显微镜技术将是一个广泛适用的除了在环境和极端条件下调查材料变形的材料表征工具箱。此外，代表性不足的少数民族参与STEM研究的管道将通过“可视化科学”暑期实习计划为来自代表性不足社区的高中生创造。这些实习生将与该计划支持的研究生一起研究金属的韧性断裂行为，学习机械测试和表征技术，并参观Novelis研究中心，这是一家全球铝公司，总部位于亚特兰大附近。 为了提高该项目的广泛影响，实习生还将制定包含虚拟现实元素的课程计划，并带回高中课堂。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

NON-TECHNICAL SUMMARYPeople have been using metals and alloys for thousands of years and have constantly improved their strength and performance through developing new ways to make them. This process of improving the metals has relied largely on guess and check. Although people have become better at guessing and faster at checking, the foundational knowledge to intelligently design stronger, safer, more resilient metals is still missing. Part of the challenge in improving metals and alloys is that their performance is dependent on the type, density, and distribution of trillions of defects that are not much bigger than a few atoms. Two important types of these defects are dislocations, which allow metals to deform, and grain boundaries, which act as barriers to dislocations as they move through the metal. The character of a grain boundary influences how easily dislocations can move through the material, and in turn, affects the strength of the metal. However, a direct link between the character of a grain boundary and how strong of a barrier it is to dislocation motion has not been established. By looking at grain boundaries at ultra-small length scales of one millionth of a meter and smaller, this project will establish that direct link. To do so, electron microscopes, capable of imaging materials down to the level of individual atoms, will be used to see how dislocations accumulate in the material near grain boundaries while the material is being bent. Artificial intelligence (AI) will be built into the electron microscopes in order to rapidly and automatically explore tens of thousands of grain boundaries to obtain a statistical understanding of how grain boundary character is connected to its strength. This understanding will be instrumental in guiding the development of new metals and alloys that are stronger, safer, and longer lasting in application. The broader outreach of this work includes integrating high school students from underrepresented communities in a summer internship research program. These interns will work closely with graduate students supported by the program to investigate the strength of metals and will also develop lesson plans incorporating virtual reality elements to take back to their classes in the following school year. The summer internship will also include visits to the Novelis research center, a global Al company with research headquarters near Atlanta.TECHNICAL SUMMARYThe central role of grain boundaries has long been recognized in dictating the mechanical behavior and failure susceptibility of metals and alloys. However, efforts to understand how variations in grain boundary characteristics affect material properties have been hampered by an incomplete understanding of what determines the strength of individual grain boundaries. The purpose of this project is to determine the characteristics that dictate grain boundary strength, here defined as the barrier strength that grain boundaries pose to dislocation propagation. A new adaptive remeshing electron backscatter diffraction (AR-EBSD)-based approach will be developed, combining in-line processing and automated adaptive grid remeshing to rapidly sample the tens of thousands of grain boundaries needed to build a library to which machine learning approaches can be applied. This approach will be coupled with transmission electron microscopy (TEM) characterization and atomistic simulations to correlate grain boundary strength with dislocation transfer mechanisms. This coupled approach will facilitate an unprecedented exploration of grain boundary space in terms of the number of grain boundaries investigated, allowing rigorous grain boundary strength functions to be established. In addition, the multiscale electron microscopy techniques developed over the course of the proposed work will be a widely applicable addition to the materials characterization toolbox in investigating material deformation under ambient and extreme conditions. Furthermore, a pipeline for underrepresented minorities to engage in STEM research will be created by a “visualizing science” summer internship program for high school students from underrepresented communities. These interns will work with graduate students supported by this program to investigate the ductile fracture behavior of metals, learn mechanical testing and characterization techniques, and visit the Novelis research center, a global Al company with research headquarters near Atlanta. To enhance the broader impact of this program, the interns will also develop lesson plans incorporating virtual reality elements to take back to their high school classes.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.