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

结晶是一种众所周知的现象，在非常小的长度尺度上，液体内部开始形成固体区域。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笼形物，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获得理想的机械性能。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/或分子可以自我组织成几何或非几何结构，这些结构存在的时间很短。这些结构被称为紧急结构，有时可以将自己转化为触发结晶过程的新的纳米级结构。根据材料的不同，转化可能涉及多个步骤，甚至可能是一个级联过程，其中一个结构导致另一个结构，所有这些都发生在液相中。了解液体中的紧急结构和多步骤结晶过程是至关重要的，因为每一步的结构都可能包含有用的信息，并揭示可以用于所需应用的特性。不幸的是，对液体结构的物理分析通常是基于昂贵的定制仪器，即原位x射线同步加速器衍射，这往往受到时间和空间分辨率的限制。另一方面，原子模拟原则上能够提供液体中涌现的结构到晶体转换机制的详细、空间和时间解析的表征。然而，这些模拟可能非常耗时，因为它们需要在多次迭代中计算大量原子之间的量子力学相互作用。现代机器学习和人工智能方法有望绕过原子模拟目前面临的限制。PI团队将原子模拟与机器学习和人工智能方法相结合，以获得对液体中紧急结构及其对结晶影响的前所未有的见解。该项目将整合研究和教育，为来自代表性不足的社区的本科生和研究生建立一条加入新墨西哥理工学院的途径。这项工作认识到，新墨西哥州是一个以西班牙裔人口为主的州，也是一个重要的美洲原住民社区，目前在获得STEM项目方面面临限制。为此目的，PI将执行两个项目：(1)为当地高中10-11年级的学生和教师举办为期两个月的夏令营，名为Camp PyMatter，该夏令营将使用Python编程语言介绍材料科学的基础知识；(2)通过开展校际教师合作，让学生参与合金制造技术和基于计算材料科学的主题，向纳瓦霍理工大学工程、数学和技术学院拓展。最近的研究表明，在液态中出现的复杂结构通过多步成核过程催化结晶。令人惊讶的是，这些结构与最终的平衡固体几乎没有相似之处。然而，现有的经典成核和生长理论假设通过单步过程从液相直接转变为平衡固体，并没有考虑到这种结构。该项目旨在通过开发一个热力学集成的、基于机制的建模框架来克服这一基本限制，以预测多步成核和生长途径，并量化它们的能量学和动力学，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)开发一种强大的方法来检测液相中的紧急结构，并将它们与能量结构景观相关联；(2)利用该景观来量化与多步骤成核过程相关的活化能和成核速率；(3)利用能量结构景观来量化固体的生长动力学。原子模拟将用于检测结构和确定它们的能量，并通过使用无监督、有监督和生成机器学习方法建立结构-热力学关系。该项目将直接影响用于承重应用的新型金属合金的发现。它将指导适当的合金元素的选择，有效地影响液态熔体凝固过程中的成核能量和速率。通过控制成核过程，可以精确地控制凝固组织的晶粒分布和力学性能。本研究中使用的框架是强大的，适应性强的，可扩展的，因为它允许检查新的元素添加对当前合金凝固的影响，至关重要的是，为发现具有转变机械性能的下一代材料铺平道路。其中一个例子是高熵合金的发展，由于其多元素环境，强调复杂结晶机制的作用。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。

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.