GOALI: Understanding Nucleation and Growth of Solute Clusters and GP Zones to Facilitate Industrial Fabrication of High-Strength Al Alloys

本项目将研究高强度铝（Al）合金中析出相的形核，以促进其在汽车工业中的应用。金属材料中被称为“溶质”的合金元素可以以不同的形式或相存在。如果溶质作为单个原子随机分布在合金中，则溶质处于固溶相；如果一些溶质通过典型的成核和生长过程聚集在一起形成特定的结构，它们也可以处于某些小颗粒或“沉淀”相。合金的力学性能取决于溶质的相。一般来说，固溶相合金具有柔软和延展性，便于低成本的机械成形和制造；但是，与固溶体相的合金相比，具有某些析出相的合金可能更硬，因此难以进行机械成形，因此它们通常在最终产品阶段在合金部件中实现。以锌（Zn）和镁（Mg）为主要合金元素的7000系列铝合金，经过适当的沉淀硬化后，具有较高的强度-重量比（强度与传统钢相当，但重量只有传统钢的一半）。它们作为结构部件在汽车工业中的广泛应用，可以实现汽车的轻量化和高燃油效率。然而，即使在室温下，这些合金中的溶质溶相也可以迅速（~30分钟）转变为沉淀相，从而使合金硬化，这对使用当前汽车制造技术进行低成本成型和制造提出了重大挑战。密歇根大学和通用汽车合作的GOALI项目旨在应用综合计算、实验和统计方法来理解和控制7000系列铝合金固溶到沉淀转变动力学的早期阶段。关键目标是设计新的合金化学物质，以延缓室温下早期析出相的形核和生长。这些早期沉淀主要是溶质团簇和ginier - preston （GP）带，两者都是由少量（小于1000个）溶质原子聚集在一起形成的。然后，这些合金可以在较长的时间内保持在软固溶相，方便传统的汽车制造技术。此外，新的合金化学成分不应阻碍在较高温度下的最终沉淀硬化。拟议的研究将有可能使7000系列铝合金在汽车工业中的实施，有助于汽车轻量化，并对节能，可持续性和竞争力产生有利影响。所产生的计算-实验-统计框架和新知识将适用于一般的合金设计，从而加速满足未来需求的材料开发。拟议的教学和培训要素将使综合计算-材料-工程（ICME）方法能够广泛传授给材料专业的高年级本科生和研究生，并为K-12学生提供支持外展/教育活动，同时为代表性不足的群体的学生提供参与最先进材料研究的机会。技术概述：固体中析出相的形核和生长理论是指导先进时效硬化组织合金开发和应用的关键基本原理。然而，传统的理论无法为多组分工业合金的发展和加工提供定量指导，在缺陷的影响下，析出相的形核和生长可以在多个步骤中发生，并发生实质性的结构-成分转变。这种理论与实践之间的差距限制了许多需要特殊制造和制造工艺的商业合金的工业应用。例如，高强度al - zn - mg基7000系列合金，如果在固溶处理后冲压超过30分钟，则具有严重的成形性限制。这些限制来自于室温下的快速沉淀动力学（“自然时效”），主要是溶质团簇和ginier - preston （GP）带的成核和生长，这些区域可以作为后续沉淀的核。了解和控制这些成核和生长过程，可以减缓自然时效，从而扩大适合7000系列铝合金和其他轻量化高强材料在汽车工业中可持续制造的室温成形窗口，这对汽车减重具有重要影响。在这个产学研合作的GOALI项目中，申请者计划运用综合的理论、计算、实验和机器学习方法来理解和控制al - zn - mg基合金中溶质团簇和GP带的成核和生长动力学。建立基于第一性原理计算、原子模拟和现象硬化模型的多尺度模拟框架，定量描述溶质团簇和GP区动力学及其对硬度增量的影响。将合成具有上述溶质的合金，并对其进行热处理和压痕硬度测试，以验证其自然时效动力学。将采用高分辨率透射电子显微镜、电子能量损失光谱和计算机图像模拟相结合的方法来表征溶质团簇和细沉淀，以验证成核和生长机制。通过进一步的实验验证，构建统计机器学习代理模型，以加快对合金化学成分的搜索，以延缓自然老化。在这个项目中，研究小组提出了一种革命性的合金设计概念，通过搜索微量溶质元素来控制固体团簇和GP区的结构和组成，从而调整复杂商业合金的早期沉淀动力学，而不仅仅是微量溶质元素的单个原子的作用。研究小组还提出了一种有效的常规方法，通过综合计算，实验和统计机器学习方法来设计具有大参数空间的先进合金。利用新开发的计算和实验工具定量了解7000系铝合金的析出相形核和生长动力学，将促进广义多组分合金体系的先进形核和生长理论的发展。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 will study the nucleation of precipitates in high-strength aluminum (Al) alloys to facilitate their application in the automobile industry. Alloying elements called 'solutes' in metallic materials can exist in different forms, or phases. The solutes are in solid solution phases if they are randomly distributed in alloys as individual atoms; they can also be in certain small particles or 'precipitate' phases if some solutes agglomerate together to form specific structures through typical nucleation and growth progresses. Mechanical properties of alloys depend on the phases of solutes. Generally, alloys in solid solution phases are soft and ductile, convenient for low-cost mechanical forming and fabrication; but alloys with certain precipitate phases can be much harder and, therefore, difficult for mechanical forming compared with their counterparts in solid solution phases, so they are usually achieved in alloy components at final product stages. 7000 series aluminum alloys with zinc (Zn) and magnesium (Mg) as the major alloying elements have a high strength-to-weight ratio after proper precipitation hardening (similar strength but half of the weight compared with conventional steel). Their widespread implementation in the automotive industry as structural components can achieve vehicles with lightweight and high fuel efficiency. However, the solute solution phases in these alloys can quickly (~30 minutes) transform into precipitate phases to harden the alloys even at room temperature, making significant challenges of their low-cost forming and fabrication using current automobile manufacturing techniques. This University of Michigan-General Motors collaborative GOALI project aims to apply an integrated computational, experimental and statistical approach to understand and control the early stages of solid-solution-to-precipitate transformation kinetics of 7000 series aluminum alloys. The key objective is to design new alloy chemistries to retard the nucleation and growth of early-stage precipitate phases at room temperatures. These early-stage precipitates are mainly solute clusters and Guinier-Preston (GP) zones, both of which are made of a small number (less than 1000) of solute atoms agglomerated together. Then these alloys can stay in soft solid-solution phases for a longer time, convenient for conventional automobile manufacturing techniques. In addition, the new alloy chemistries should not impede the final precipitation hardening at a higher temperature. The proposed research will potentially enable implementation of 7000 series aluminum alloys in the automobile industry, contributing to vehicle light-weighting and favorably impacting energy savings, sustainability, and competitiveness. The generated computational-experimental-statistical framework and new knowledge will be applicable to alloy design in general and thus accelerate material development for meeting future needs. The proposed teaching and training elements will enable an integrated-computational-materials-engineering (ICME) approach to be widely imparted to senior undergraduate and graduate students in materials major and champion outreach/education activities of K-12 students as well as opportunities for students of underrepresented groups to be engaged in start-of-the-art materials research.Technical Summary: Nucleation and growth theories of precipitates in solids are key fundamental principles to guide the development and application of advanced age-hardenable structural alloys. However, the conventional theories fail to provide quantitative guidance for the development and processing of multicomponent commercial alloys, where nucleation and growth of precipitates can occur in multiple steps with substantial structural-composition transformations under the influence of defects. This gap between theory and practice limits the industrial applications of many commercial alloys that require special fabrication and manufacturing processes. For example, high-strength Al-Zn-Mg-based 7000 series alloys have severe formability limitations if stamped more than ~30 minutes after the solutionizing treatment. These limitations result from fast precipitate kinetics at room temperature ('natural aging'), mainly the nucleation and growth of solute clusters and Guinier-Preston (GP) zones that can act as nuclei for subsequent precipitates. Understanding and controlling these nucleation and growth processes can slow down natural aging, and thereby expand the room temperature forming window amenable to the sustainable manufacturing of 7000 series Al alloys and other lightweight high-strength materials in the automobile industry, which has a significant impact on vehicle mass reduction.In this industry-university collaborative GOALI project, the applicants plan to apply an integrated theoretical, computational, experimental and machine learning approach to understand and control the nucleation and growth kinetics of solute clusters and GP zones in Al-Zn-Mg-based alloys. A multi-scale simulation framework based on first-principles calculations, atomistic simulations, and phenomenological hardening model will be constructed to quantitatively describe the solute clusters and GP zone kinetics and their effects on hardness increments. Alloys with the proposed solutes will be synthesized and subjected to thermal processing and indentation hardness tests to verify their natural aging kinetics. A combination of high-resolution transmission electron microscopy, electron energy loss spectroscopy and computer image simulations will be used to characterize the solute clusters and fine precipitates to verify the nucleation and growth mechanisms. A statistical machine learning surrogate model will be constructed to speed up the search of alloy chemistries to retard natural aging with further experimental confirmations. In this project, the research team proposes a transformative alloy design concept to tune the early-stage precipitation kinetics of complex commercial alloys by searching the trace solute elements to control the structures and compositions of solid clusters and GP zones beyond the role of individual atoms of trace solute elements. The research team also proposes an efficient routine to design advanced alloys with a large parameter space by applying the integrated computational, experimental and statistical machine learning methods. Quantitative understanding of precipitate nucleation and growth kinetics in 7000 series Al alloys using the newly developing computational and experimental tools will facilitate the development of advanced nucleation and growth theories for generalized multicomponent alloy systems.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.