Fundamental Understanding of Chemical Complexity on Crack Tip Plasticity of Refractory Complex Concentrated Alloys

摘要：本项目旨在研究新型难熔复合浓缩合金（RCCAs）裂纹尖端塑性，以促进其在高温应用中的应用。例如，高超音速、更安全的核聚变能源和节油飞机都需要使用金属合金，这些合金可以在极高的温度（1200℃）下保持高机械强度。以难熔金属(如铌（Nb）、钼（Mo）、钽（Ta）、钨（W)和铼(W)）为基础的合金提供了这种可能性，这些金属的熔化温度都高于2000℃。典型的合金依赖于单一难熔金属作为其主要（50%）化学成分。然而，这些材料不符合高温结构应用的标准，因为随着温度的升高，它们的强度会急剧降低。复杂浓缩合金（CCAs），涉及高浓度的多种耐火元素，可能满足极其严格的规格要求。耐火CCAs (RCCAs)，如NbMoTaW，由于多种化学元素之间的独特相互作用和这些合金内部的变形缺陷，可以在很宽的高温范围内保持高强度。然而，这些rcca在室温下通常是脆的，这使得它很难弯曲，形成和塑造这些金属成适合使用的形式。本项目通过综合计算、实验和统计方法，对如何在室温下控制rcca裂纹尖端的机械变形产生了新的认识。主要目标是激活各种形式的塑性变形，以钝化裂纹尖端和减缓快速裂纹扩展。基于量子力学和统计方法的计算机模拟被用来筛选可能的RCCA成分。激光定向能量沉积也被用于候选组合物的快速生产。先进的机械测试和纳米级结构表征工具也被用于分析裂纹尖端的变形。最后，机器学习技术被应用于分析实验测试和表征结果，以识别新的合金化学成分和减缓裂纹扩展所需的机制。该研究项目能够实现具有高室温延展性和成型性的rcca，对能源可持续性、航空航天和其他需要极端热机械环境下结构材料的关键领域产生积极影响。与此相关，该项目的教学和培训要素是：1)使综合计算材料工程（ICME）方法和人工智能（AI）概念在高年级本科生和研究生中广泛分享，2)为K-12学生的学生提供推广活动，以及3)为代表性不足的群体的学生提供参与最先进工程材料研究的机会。技术概述：这种新型材料被称为单相体心立方（BCC）耐火复合浓缩合金（RCCAs），它含有高浓度的多种耐火元素，在高于镍基高温合金熔点的温度下表现出很高的屈服强度。然而，rcca缺乏室温延展性，导致在制造和机械加载过程中过早失效。提高其室温延展性的挑战在很大程度上源于缺乏对其裂纹尖端变形机制决定的内在延性的理解。常用的Rice准则基于经典Rice- thomson模型，使用特定滑移系统的能量学和特定解理面取向来预测裂纹尖端的韧性与脆性行为的一般趋势。然而，rcca裂纹尖端的化学和应力复杂性可能激活多种变形模式，但多种变形模式是否以及如何对rcca裂纹尖端塑性产生协同效应，以提高其延性和韧性，仍然是一个知识空白。为了缓解这一知识差距，该项目采用系统的方法，基于高通量合成、快速纳米/微力学表征技术、物理建模和机器学习（ML）方法，测量和分析单相rcca中裂纹尖端塑性的充分和代表性数据。基于第一性原理计算、ML方法和断裂力学的物理建模结果指导了高通量激光定向能沉积增材制造（ed - am）的RCCAs合成。这种方法使用一种独特的能力来快速筛选多达六种不同元素的组合物。在扫描电子显微镜下进行高通量纳米压痕、原位直接拉张和缺口悬臂断裂梁测试等纳米力学表征，将用于快速评估RCCA样品裂纹尖端的拉伸延展性、韧性和变形机制。最后，将物理建模、微观结构表征图像的深度学习和延性测量相结合，开发出一种超越经典的浓缩合金Rice标准的延性标准，该标准包含多种化学元素和变形机制的协同效应。更广泛的影响活动包括：1)使综合计算材料工程（ICME）方法和人工智能（AI）概念与高年级本科生和研究生广泛分享，2)支持K-12学生的外展活动，以及3)支持代表性不足群体的学生有机会参与最先进的工程材料研究。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。

NON-TECHNICAL SUMMARY:This project studies the crack tip plasticity in novel refractory complex concentrated alloys (RCCAs) to facilitate their applications in high-temperature applications. For example, hypersonics, safer nuclear fusion energy and fuel-efficient airplanes all require the use of metallic alloys that can retain high mechanical strength at extremely high temperatures ( 1200 ℃). Alloys based on refractory metals (such as Niobium (Nb), Molybdenum (Mo), Tantalum (Ta), Tungsten (W), and Rhenium (W), all of which have melting temperatures higher than 2000 ℃) provide such possibilities. Typical alloys rely on a single refractory metal as its major ( 50%) chemical component. These however do not meet the criteria for high-temperature structural applications because their strengths are dramatically reduced as temperatures rise. Complex concentrated alloys (CCAs), involve high concentrations of multiple refractory elements and may meet extremely strict specifications. Refractory CCAs (RCCAs), such as NbMoTaW, may sustain high strengths across a wide range of high temperatures due to unique interactions between multiple chemical elements and the deformation defects inside these alloys. However, these RCCAs are usually brittle at room temperatures, making it difficult bend, form and shape these metals into forms suitable for use.This project is generating new understanding of how to control mechanical deformation at the crack tip of RCCAs at room temperature by applying an integrated computational, experimental, and statistical method. The main goal is to activate various forms of plastic deformation in order to blunt the crack tips and slow fast crack propagations. Computer simulations based on quantum mechanics and statistical methods are being used to screen for possible RCCA compositions. Laser-directed energy deposition is also being carried out for fast production of the candidate compositions. Advanced mechanical testing and structural characterization tools at nanoscales are also being conducted to analyze deformation at crack tips. Finally, machine learning techniques are being applied to analyze the experimental testing and characterization results for the purpose of identifying novel alloy chemistries and the mechanisms needed to slow down crack propagations. This research project is enabling the implementation of RCCAs with high room-temperature ductility and formability, favorably impacting energy sustainability, aviation/aerospace, and other critical areas that require structural materials under extreme thermomechanical environments. Relatedly, the teaching and training elements of this project are: 1) enabling integrated-computational-materials-engineering (ICME) approaches and artificial intelligence (AI) concepts to be widely shared with senior undergraduate and graduate students, 2) championing outreach activities for students in K-12 students as well as 3) supporting opportunities for students of underrepresented groups to engage in state-of-the-art engineered materials research.TECHNICAL SUMMARY:The novel materials known as single-phase body-centered cubic (BCC) refractory complex concentrated alloys (RCCAs), which contain multiple refractory elements in high concentrations, exhibit high yield strengths at temperatures above the melting point of Ni-base superalloys. However, RCCAs lack room-temperature ductility, resulting in premature failure during manufacturing and mechanical loading. The challenges to enhance their room-temperature ductility to a large extent originate from the lack of understanding of their intrinsic ductility determined by deformation mechanisms at their crack tips. The commonly employed Rice criterion based on the classical Rice-Thomson model uses the energetics of a specific slip system and a particular cleavage plane orientation to predict the general trends of the ductile versus brittle crack tip behavior. However, chemical and stress complexity at crack tips in RCCAs may activate multiple deformation modes, but there is still a knowledge gap on whether and how the synergistic effects of multiple deformation modes on crack tip plasticity emerge in RCCAs to enhance their ductility and toughness. To alleviate this knowledge gap, this project is applying a systematic approach to measuring and analyzing sufficient and representative data of crack tip plasticity within single-phase RCCAs based on high throughput syntheses, rapid nano/micromechanical characterization techniques, physical modeling, and machine learning (ML) methods. Physical modeling results based on first-principles calculations, ML methods, and fracture mechanics are guiding the synthesis of RCCAs via high-throughput laser-directed energy deposition additive manufacturing (DED-AM). This approach uses a unique capability to enable rapid screening of compositions of up to six different elements. Nanomechanical characterization involving high-throughput nanoindentation and in situ direct pull tensile and notched cantilever fracture beam tests in a scanning electron microscope will be used for rapid assessment of tensile ductility, toughness, and deformation mechanisms at crack tips of RCCA samples. Finally, the physical modeling, deep learning of microstructural characterization images, and ductility measurements are being integrated to develop a ductility criterion beyond the classical Rice criterion for concentrated alloys that incorporates synergistic effects of multiple chemical elements and deformation mechanisms. Broader impact activities include: 1) enabling integrated-computational-materials-engineering (ICME) approaches and artificial intelligence (AI) concepts to be widely shared with senior undergraduate and graduate students, 2) championing outreach activities for students in K-12 students as well as 3) supporting opportunities for students of underrepresented groups to engage in state-of-the-art engineered 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.