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.

非技术摘要结晶是一种众所周知的现象，其中固体区域开始在非常小的长度尺度上形成，这种现象是自然现象和工程应用的基础，包括冰的形成、天然气管道中的碳氢化合物包合物、磷酸钙的生物矿化。在骨形成、用于药物设计和生产的分子晶体的合成以及金属合金的凝固以实现所需的机械性能期间，固体区域的形成高度依赖于底层的液相结构。在液体中，一些原子和/或分子可以自组织成存在很短时间的几何或非几何结构，这些结构被称为涌现结构，有时可以将自身转化为触发的新的纳米级结构。根据材料的不同，转化可能涉及多个步骤，甚至可以是一个级联过程，其中一种结构导致另一种结构，所有这些都发生在液相中的涌现结构和多步骤。结晶过程为至关重要的是，因为每个步骤的结构都可能保存有用的信息并揭示可用于所需应用的属性，不幸的是，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，另一方面，原子模拟原则上能够提供液体中涌现物的详细的、空间和时间分辨率的表征。然而，这些模拟可能非常耗时，因为它们需要多次迭代计算大量原子之间的量子力学相互作用，而现代机器学习和人工智能方法有望克服当前面临的限制。 PI 团队将原子模拟与机器学习和人工智能方法相结合，以获得对液态新兴结构及其对结晶的影响的前所未有的见解。该项目将整合研究和教育，建立一个来自代表性不足的社区的本科生和研究生加入新墨西哥理工学院的途径这项努力认识到新墨西哥州是一个以西班牙裔人口为主的州，并且有一个重要的美国原住民社区，目前在获得 STEM 项目方面面临限制。 PI 将开展两个项目：(1) 为当地高中 10 至 11 年级的学生和教师开发为期两个月的夏令营，名为 Camp PyMatter，将使用 Python 编程语言介绍材料科学的基础知识， 和(2) 通过发展大学间教师合作并让学生参与合金制造技术和计算材料科学主题，向纳瓦霍理工大学工程、数学与技术学院进行推广。 技术摘要最近的研究表明，在纳瓦霍理工大学工程、数学与技术学院中出现的复杂结构液态通过多步成核过程催化结晶，这些结构与最终的平衡固体几乎没有相似之处，但现有的经典成核和生长理论假设是直接的。该项目试图通过一个热力学集成的、基于机制的建模框架来预测多步成核和生长路径的发展，从而克服这一基本限制。并量化其能量学和动力学以解决广泛的结晶问题。 PI 的研究团队将通过研究模型金属合金来实现以下三个研究目标：(1) 开发一种可靠的方法来检测液相中的新兴结构并将它们关联起来。和能量结构景观，（2）利用该景观来量化与多步成核过程相关的活化能和成核速率，以及（3）利用能量结构景观来量化固体的生长动力学，以进行检测。该项目将直接影响用于承载应用的新型金属合金的发现，并指导其选择。适当的合金元素可以有效地影响液态熔体凝固过程中的成核能量和速率。通过对成核过程进行控制，可以精确地控制凝固微观结构的晶粒尺寸分布和机械性能。它具有适应性和可扩展性，因为它可以检查新型元素添加对当前合金凝固的影响，最重要的是，它为发现具有变革性机械性能的下一代材料铺平了道路。高熵合金的开发，强调由于其多元素环境而产生的复杂结晶机制的作用。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进行评估，被认为值得支持。