CAREER: Emergence of in-liquid structures in metallic alloys by nucleation and growth

职业：通过成核和生长在金属合金中出现液态结构

• 批准号: 2333630
• 负责人: Deep Choudhuri
• 金额: $ 59万
• 依托单位: New Mexico Institute of Mining and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-15 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333630&HistoricalAwards=false
• 关键词: CAREER; Emergence; liquid; structures; metallic

NONTECHNICAL SUMMARYCrystallization is a well-known phenomenon where solid regions start forming within a liquid on very small length scales. This phenomenon is foundational to natural phenomena and engineering applications, including ice formation, hydrocarbon clathrates in natural gas pipelines, bio-mineralization of calcium phosphate during bone formation, the synthesis of molecular crystals for drug design and production, and solidification of metallic alloys to achieve desirable mechanical properties. The formation of solid regions is highly dependent on the underlying liquid phase structure. In the liquid, a few atoms and/or molecules can self-organize themselves into geometric or non-geometric structures that exist for a very short time. These structures, which are called emergent structures, can sometimes convert themselves into new nanoscale structures that trigger the crystallization process. Depending on the material, the conversion may involve multiple steps or can even be a cascading process, where one structure leads to another structure, all happening within the liquid phase. An understanding of the in-liquid emergent structures and the multistep crystallization process is critical because the structure at each step may hold useful information and reveal properties that can be leveraged for desired applications. Unfortunately, physical interrogation of the liquid structure is often based on expensive custom-built instrumentations, i.e., in situ X-ray synchrotron diffraction, that tend to be limited by temporal and spatial resolution. On the other hand, atomistic simulations are, in principle, able to provide a detailed, spatially and temporally resolved, characterization of the in-liquid emergent structure-to-crystal conversion mechanisms. Yet, these simulations can be prohibitively time-consuming because they require computing quantum-mechanical interactions between a large number of atoms over multiple iterations. Modern machine-learning and artificial intelligence approaches promise to circumvent limitations currently faced by atomistic simulations. The team of the PI will combine atomistic simulations with machine-learning and artificial intelligence approaches to gain unprecedented insights into in-liquid emergent structures and their influence on crystallization.This project will integrate research and education to establish a pathway of undergraduate and graduate students from underrepresented communities to join New Mexico Tech. This effort recognizes that New Mexico is a state with a Hispanic majority population and a significant Native American community that currently face limitations in gaining access to STEM programs. To this end, the PI will pursue two projects: (1) the development of two month-long summer camps, named Camp PyMatter, for 10-11th grade students and teachers from local high schools, which will introduce the basics of Materials Science using the Python programming language, and (2) outreach to Navajo Technological University’s School of Engineering, Math & Technology by developing inter-university faculty collaborations and engaging their students with alloy fabrication techniques and computational Materials Science-based topics.TECHNICAL SUMMARYRecent studies have revealed that intricate structures that emerge within the liquid state catalyze crystallization via multi-step nucleation processes. These structures bear surprisingly little resemblance to the final equilibrium solid. However, extant classical nucleation and growth theories assume a direct transformation from the liquid phase to equilibrium solid via a single-step process and do not account for such structures. The project seeks to overcome this fundamental limitation by developing a thermodynamically integrated, mechanism-based modeling framework to predict muti-step nucleation and growth pathways and quantify their energetics and kinetics for a wide-range of crystallization problems. The PI’s research team will pursue the following three research objectives by studying model metallic alloys: (1) develop a robust methodology to detect emergent structures within a liquid phase and correlate them with energy-structure landscape, (2) utilize that landscape to quantify activation energy and nucleation rates associated with multi-step nucleation processes, and (3) leverage energy-structure landscape to quantify growth kinetics of a solids. Atomistic simulations will be employed to detect structures and determine their energies, and establish structure-thermodynamics relationship by using unsupervised, supervised, and generative machine learning methods. This project will directly impact the discovery of novel metallic alloys used in load-bearing applications. It will guide the selection of appropriate alloying elements that effectively influence the nucleation energetics and rates during solidification from a liquid melt. By exerting control over the nucleation process, one can precisely manipulate the grainsize distribution and mechanical properties of the solidified microstructure. The framework used in this study is robust, adaptable, and scalable, as it allows for the examination of the effects of novel elemental additions on solidification of current alloys and, crucially, pave way for discovering next-generation materials with transformative mechanical properties. One such example is the development of high entropy alloys, which emphasize the role of complex crystallization mechanisms due to their multi-element environment.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.

非技术总结结晶是一种众所周知的现象，其中固体区域在液体尺度很小的液体内开始形成。这种现象是自然现象和工程应用的基础，包括冰形成，天然气管道中的碳氢化合物网状物，骨形成过程中磷酸钙的生物矿化，用于药物设计和生产的分子晶体的合成以及金属合金的固化以实现所需的机械性能。固体区域的形成高度取决于潜在的液相结构。在液体中，一些原子和/或分子可以自组织成几何或非几何结构，这些结构在很短的时间内存在。这些称为新兴结构的结构有时可以将自己转换为触发结晶过程的新的纳米级结构。根据材料的不同，转换可能涉及多个步骤，甚至可能是级联过程，其中一种结构导致另一个结构，所有结构都发生在液相内。对液体中的新兴结构和多步晶过程的理解至关重要，因为每个步骤的结构都可以容纳有用的信息，并揭示了可用于所需应用的属性。不幸的是，对液体结构的物理询问通常基于昂贵的定制仪器，即原位X射线同步加速器衍射，这些衍射往往受到临时和空间分辨率的限制。另一方面，原则上的原子模拟能够提供详细的，空间和临时的解决，对液体内的结构到晶体转换机制的表征。但是，这些模拟可以禁止耗时，因为它们需要在多个迭代中计算大量原子之间的量子力学相互作用。现代的机器学习和人工智能方法有望规避原子模拟当前面临的局限性。 PI团队将将原子模拟与机器学习和人工智能方法结合起来，以获得对液体内的新兴结构的前所未有的见解及其对结晶的影响。该项目将整合研究和教育，以建立一门不足的社区的学生和研究生的途径，以加入新墨西哥的技术。这项努力认识到，新墨西哥州是一个拥有西班牙裔多数人口的州，也是一个重要的美国原住民社区，目前在获得STEM计划方面面临限制。为此，PI将追求两个项目：（1）开发两个月的夏令营，名为Pymatter营地，为10至11年级的学生和当地高中的老师开发，这将使用Python编程语言介绍材料科学的基础知识，以及（2）通过工程学大学的工程学和技术来开发纳瓦霍（Navajo基于材料的基于科学的主题。技术摘要研究表明，液态在液态内出现的复杂结构通过多步成核过程催化结晶。这些结构与最终平衡固体几乎没有相似之处。但是，广泛的经典成核和生长理论假设通过单步过程从液相转变为平衡固体，并且不考虑这种结构。该项目试图通过开发基于热力学的，基于机制的建模框架来克服这一基本限制，以预测相互键入的成核和生长途径，并量化其能量和动力学，以解决大量的结晶问题。 PI的研究团队将通过研究模型金属合金来追求以下三个研究目标：（1）开发一种可靠的方法来检测液相内的新兴结构，并将其与能量结构景观相关联，（2）利用该景观来量化活化的能量，并量化与量化量的量化量相关的量化量和量化量的量化量，并量化了量子量的范围（3）。将使用原子模拟来检测结构并确定其能量，并通过使用无监督，监督和通用的机器学习方法来建立结构 - 热力学关系。该项目将直接影响在承载应用中使用的新型金属合金的发现。将指导选择合适的合金元件，从而有效地影响液体熔体固化过程中的成核能量和速率。通过对成核过程的控制，可以准确操纵固化微结构的晶格分布和机械性能。这项研究中使用的框架是可靠的，适应性的和可扩展的，因为它允许研究新型元素添加对当前合金固化的影响，并且在至关重要的是，铺平了发现具有变换机械性能的下一代材料。一个这样的例子是高熵合金的开发，该合金强调了由于其多元素环境而引起的复杂结晶机制的作用。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来审查标准的评估，被认为是宝贵的支持。