Collaborative Research: Dynamics of Short Range Order in Multi-Principal Element Alloys

合作研究：多主元合金中的短程有序动力学

• 批准号: 2348955
• 负责人: Gregory Thompson
• 金额: $ 35.99万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348955&HistoricalAwards=false
• 关键词: Collaborative; Research; Dynamics; Short; Range

NON-TECHNICAL SUMMARYNearly all metals used in practice are alloys, meaning that they are a mixture of different types of metal atoms. Depending on the alloy, different atomic types arrange in either an ordered or disordered way within a crystal. Alloys where the different atoms have a disordered or random arrangement can benefit from improved properties including increased strength or corrosion resistance. It has recently been proposed that in some disordered alloys the actual atomic arrangement could be more subtle, appearing to be ordered (or non-random) over short distances and disordered over longer distances. This phenomenon, known as short range ordering, is implicated in the exceptional properties of a recently developed group of alloys known as multi-principal element alloys (MPEAs) that have particular promise for next-generation applications in power generation and national defense. However, one difficulty is that short range ordering remains problematic to measure. This research will use a microscope called an atom probe to detect the types and locations of individual atoms, but with an uncertainty that can make the measurement of subtle ordering inconclusive. Here, artificial intelligence will be used to detect any short range ordering present within the data. Using this new means of analysis, the project will then test the idea that short range ordering in stable conditions is independent of how the alloy was made. In doing so, this project will have a broad impact on understanding how to engineer short range ordering in alloys by means of their processing, all of which has implications for how MPEAs will develop as next-generation materials. Alongside the research, artificial intelligence teaching modules will be created to expose and educate middle and high school students in the use of this emerging technology. These will be disseminated through a workshop for secondary school teachers on how to integrate materials-centric examples into physics, chemistry, and physical science classrooms.TECHNICAL SUMMARYMulti-principal element alloys (MPEAs), often called high-entropy alloys, are an emerging alloy class with initial evidence of exceptional mechanical properties and significant compositional design flexibility. However, the rational design of MPEAs is hindered by a lack of fundamental knowledge about the chemical short range order (SRO), which are the local correlations in the distribution of atomic species. This project will characterize the evolution of SRO in a model CrCoNi MPEA to evaluate two hypotheses; first, that SRO in MPEAs reaches a stable state that is independent of the fabrication conditions, and second, that the relaxation time to the stable SRO is governed by diffusion kinetics (which themselves depend on the SRO). Samples will be fabricated by vacuum arc melting, direct current sintering, and high-pressure torsion consolidation to generate measurably different initial SRO states. SRO characterization will be done by atom probe tomography (APT) cross-correlated with electron scattering for a pair distribution function as well as energy filtered high resolution transmission electron microscopy imaging. The APT datasets will be analyzed by a machine learning approach where the data is modeled as a sample from an underlying pairwise-interaction Markov point process. Experimental data from a series of isothermal annealing experiments will be used to calibrate a mathematical model for the mutual interactions of the SRO and the self-diffusivity. The model will be used to develop time-temperature-SRO diagrams for MPEAs to be integrated into service. The project is expected to deliver: (1) Maturation of a fundamental scientific understanding of MPEAs’ compositional stability from which these materials can be deployed into service in extreme environments. (2) A robust machine learning APT analysis method that expands the technique to address high solute clustering characteristics in alloys. (3) The development of the next-generation STEM workforce at the graduate level as well as through secondary school teachers via a materials camp that instructs how to incorporate materials into the physics, chemistry, and physical science curriculum.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.

非技术概要实践中使用的几乎所有金属都是合金，这意味着它们是不同类型金属原子的混合物，不同原子类型在晶体中以有序或无序的方式存在。无序或随机排列可以受益于改进的性能，包括增加强度或耐腐蚀性，最近有人提出，在一些无序合金中，实际的原子排列可能更加微妙，看起来是有序的（或非随机的）。这种现象被称为短程有序，它与最近开发的一组称为多主元素合金（MPEA）的合金的特殊性能有关，该合金对下一代应用特别有希望。然而，一个困难是短程有序仍然难以测量，这项研究将使用一种称为原子探针的显微镜来检测单个原子的类型和位置，但测量的不确定性。微妙的在这里，人工智能将用于检测数据中存在的任何短程有序性，然后该项目将测试稳定条件下的短程有序性与合金的制造方式无关的想法。在此过程中，该项目将对理解如何通过加工来设计合金的短程有序产生广泛的影响，所有这些都对 MPEA 将如何发展为下一代材料以及研究和人工智能教学产生影响。将创建模块来公开和教育中学生如何使用这种新兴技术。这些内容将通过中学教师研讨会进行传播，内容涉及如何将以材料为中心的示例融入到物理、化学和物理科学课堂中。技术摘要多主元合金（ MPEA），通常称为高熵合金，是一种新兴的合金类别，具有卓越的机械性能和显着的成分设计灵活性的初步证据，但由于缺乏基础知识，MPEA 的合理设计受到阻碍。关于化学短程有序 (SRO)，它是原子种类分布的局部相关性。该项目将描述 CrCoNi MPEA 模型中 SRO 的演化，以评估两个假设：MPEA 中的 SRO 达到稳定状态。这与制造条件无关，其次，稳定 SRO 的弛豫时间受扩散动力学控制（扩散动力学本身取决于 SRO）。样品将通过真空电弧熔化、直流电制造。烧结和高压扭转固结以产生可测量的不同初始 SRO 状态，这将通过原子探针断层扫描 (APT) 与电子散射的电子对分布函数以及能量过滤高分辨率透射电子显微镜成像进行互相关。 APT 数据集将通过机器学习方法进行分析，其中数据被建模为来自一系列等温的基础配对交互马尔可夫点过程的样本。退火实验将用于校准 SRO 和自扩散率相互作用的数学模型。该模型将用于开发 MPEA 的时间-温度-SRO 图，以便集成到服务中。 ：(1) 对 MPEA 成分稳定性的基本科学理解成熟，这些材料可以在极端环境中使用。(2) 强大的机器学习 APT 分析方法，扩展了解决高溶质问题的技术。 (3) 通过材料营培养研究生和中学教师的下一代 STEM 劳动力，指导如何将材料纳入物理、化学和物理科学课程。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。