CAREER: Connecting Grain Boundary's Metastability Evolution to Mechanical Behavior of Nanocrystalline Alloys During Non-Equilibrium Processing

非技术概述：工程金属广泛用于先进的能源、运输和国防应用。 有趣的是，纳米晶体金属是由微小的纳米尺寸的微晶紧密堆积在一起制成的，具有上级强度和更好的耐用性。 在每个微晶内，金属原子以高度有序的方式排列。然而，由于微晶可以具有相对于彼此的各种空间取向，邻近微晶之间的边界附近的原子不再很好地对准并且被视为缺陷。这些缺陷对机械强度的提高起着决定性的作用。 因此，为了了解这些缺陷如何提高机械强度，PI和他的团队将使用阿贡国家实验室提供的同步加速器X射线源，并辅以PI开发的先进原子建模技术，以确定这些缺陷附近的原子在各种应力、温度和机械变形速率下变形时的运动。 这些见解将用于推进纳米微晶金属的时间依赖性和环境敏感性机械性能的预测理论。这项研究的结果也将被利用到课堂教学中，使用虚拟现实（VR）辅助教学创新。此外，该项目旨在通过各种平台来指导STEM领域的下一代劳动力，激励资源不足的K-12学生和具有不同背景的潜在未来工程师，并吸引公众参与并提高他们对材料科学的认识，从而产生更广泛的影响。了解亚稳态晶界在复杂环境中的行为对于开发先进能源，运输，国防应用。该项目的主要目标是建立一个基本的基础来描述在极端条件下晶界的结构和亚稳态演化，并使用这个基础来解释和预测晶界介导的变形和由此产生的机械性能的纳米晶合金发生在非平衡加工。该项目将结合联合收割机先进的原子采样技术、势能景观理论和机器学习工具，以：（i）通过诱导特定位置的扰动，获得无序原子堆积环境中的基本结构重排和相关活化能谱的系综;（ii）能够通过非-（iii）建立自洽动力学理论，在势能面中构建晶界各种亚稳态之间的连通性，并预测随时间变化的亚稳态演化;以及（iv）利用势能面辅助的新型原子模型方法，揭示了亚稳晶界与钉扎位错在多个时间尺度上的相互作用机制。该项目还将进行设计的假设驱动实验，包括X射线衍射，亚烧蚀飞秒激光脉冲和纳米压痕，以验证建模和理论预测。拟议的研究将直接联系原子级过程（例如，研究纳米晶合金的宏观行为，从而开拓纳米晶合金的新设计/加工空间，找出优化途径，以获得在复杂环境下具有更高性能和耐用性的新状态。研究计划将结合教育创新和推广活动，包括：（i）在开发的关于晶界和结构材料中其他缺陷的新课程中利用虚拟现实和其他先进技术;（ii）通过参加密歇根大学和国家实验室的暑期学校，指导STEM领域的下一代劳动力;（iii）透过不同的平台，外展资源不足的K-12学生和不同背景的未来工程师，并分享首席研究员的研究和职业发展道路，以告知和启发他们，并促进高等教育;并且，在本发明中，（四）探索博物馆等有吸引力的场所，以吸引公众参与并提高他们对材料科学的认识。该奖项由材料研究部的金属和金属纳米结构以及凝聚态物质和材料理论项目共同资助。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。

Non-Technical Summary:Engineering metals are used broadly for advanced energy, transportation, and defense applications. Intriguingly, nano-crystalline metals, which are made of tiny nanometer-sized crystallites packed tightly together, exhibits superior strength and improved durability. Within each crystallite, the metal atoms are arranged in a highly orderly manner. However, as the crystallites can have various spatial orientations with respect to each other, the atoms near the boundaries between neighboring crystallites are no longer well aligned and are viewed as defects. These defects play a decisive role in the enhanced mechanical strength. Thus, to understand how these defects improve the mechanical strength, the PI and his team will use synchrotron X-ray sources available at the Argonne National Laboratory complemented with advanced atomistic modeling techniques developed by the PI in order to determine the motion of these atoms near these defects during deformation under various stresses, temperatures, and rates of mechanical deformation. Such insights will be used to advance predictive theory of the nano-crystallite metals’ time-dependent and environment-sensitive mechanical performances. The outcome of this research will also be leveraged into classroom instruction using a virtual reality (VR)-assisted teaching innovation. In addition, this project aims for broader impacts by outreaching to a variety of platforms to mentor the next generation workforce in STEM, inspire under-resourced K-12 students and potential future engineers with diverse backgrounds, and engage the public and increase their awareness of material science.Technical Summary:Understanding the behavior of metastable grain boundaries at complex environments is crucial to develop cost-effective and high-performance nanocrystalline alloys demanded in advanced energy, transportation, and defense applications. The primary objective of this project is to establish a fundamental basis to describe the structural and metastability evolution of grain boundaries at extreme conditions, and to use this basis to explain and predict the grain boundary-mediated deformation and the resultant mechanical properties of nanocrystalline alloys taking place under non-equilibrium processing. The project will combine advanced atomistic sampling techniques, potential energy landscape theory, and machine learning tool to: (i) obtain the ensemble of elementary structural rearrangements and associated activation energy spectra in disordered atomic packing environments by inducing location-specific perturbations; (ii) enable an automated detection of kernel atoms prone to plastic deformation through the non-affine displacement field analysis using a random sample consensus machine learning algorithm; (iii) establish a self-consistent kinetic theory to construct the connectivity between grain boundaries’ various metastable states in the potential energy landscape and to predict the evolution of time-dependent metastability; and, (iv) discover the interaction mechanisms between metastable grain boundaries and pinning dislocations across multiple timescales using potential energy landscape-assisted novel atomistic modeling protocol. This project will also carry out designed hypothesis-driven experiments, including X-ray diffraction, sub-ablation femtosecond laser pulses, and nano-indentation, to validate the modeling and theoretical predictions. The proposed research will directly link the atomic level processes (e.g., short-range atomic rearrangements and non-affine atomic displacement) inside the grain boundaries with the macroscopic behavior of nanocrystalline alloys, which may facilitate exploiting new design/processing space of nanocrystalline alloys, identifying optimized routes to access novel states with enhanced performance and durability under complex environments.The research program will be integrated with educational innovations and outreach activities, including: (i) utilizing virtual reality and other advanced techniques in the developed new course on the subject of grain boundaries and other defects in structural materials; (ii) mentoring the next generation workforce in STEM fields by participating in Summer Schools both at the University of Michigan and at National Laboratories; (iii) outreaching to under-resourced K-12 students and potential future engineers with diverse background through various platforms, and sharing the PI’s research and career path to inform and inspire them and to promote higher education; and, (iv) exploring the attractive venues such as museums to engage the public and increase their awareness of materials science.This award is cofunded through the Metals and Metallic Nanostructures and Condensed Matter and Materials Theory programs in the Division of Materials Research.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.