CAREER: Microstructure Formation in Chemically-Modified Eutectics: Bridging Real-Time Imaging, Machine Learning, and Problem-Based Instruction

自然界中的非技术性SUMMARYPatterns通常是当系统从物质的一个阶段变化到另一个阶段时形成的--例如，在凝固过程中从液态转变为几何图案的固相。由此产生的图案类似于大都市地区迷宫般的街道，尽管它们大约小1亿倍，而且是三维的，称为晶体。排列在图案中的AS凝固晶体的结构与材料的性能密切相关，即使在随后的加工步骤之后也是如此。对我们有利的是，这些晶体的形状和大小可以通过控制母液相的化学成分来满足技术要求。例如，已知溶解在液体中的微量金属杂质会将固体硅(Si)从粗大的块状颗粒转化为细小的网状纤维。这大大提高了硅基合金的强度和延展性，并扩大了其在新应用领域的潜力，包括空间框架和电动汽车。这一职业奖的目标是通过利用阿贡国家实验室世界上最明亮的硬X射线源之一，了解在存在微量金属杂质的情况下如何以及为什么会发生这种转变。入射的X射线可以穿透原本不透明的金属，使人们能够实时捕捉到凝固的细节。实验结束后，PI和他的团队将使用最先进的机器学习算法从凝固的时间推移视频中提取信息。预计他们的新愿景将把合金凝固领域从冶金炼金术推进到预测科学。最终，了解合成过程中固体图案的演变是自下而上控制先进材料制造的关键。该计划产生的新发现将与底特律地区大学预科工程计划(DAPCEP)合作，整合到面向代表不足的中学生的以问题为基础的学习单元。PI将使用通过DAPCEP组织分发的年度和多年调查来评估这些活动的影响。技术总结在过去的50年里，人们对多组分金属合金的凝固过程越来越感兴趣--无论是基本的还是应用的。一个长期存在的问题涉及到共晶合金系统中的组织形成。虽然具有非刻面界面的二元共晶相对较好地被理解，但对于具有刻面界面和/或两个以上组分的共晶的凝固行为知之甚少。在微量金属物种(所谓的化学变质剂)存在的情况下凝固的Al-Si共晶就是这种情况。单变量反应(即三组分两固相)的较高自由度导致了在不变共晶中看不到的形态转变。例如，化学变质剂将硅相的形态从粗大的片状转变为细小的纤维，从而改善了铸造合金的力学性能。尽管我们在化学改性方面有技术经验，但微量金属物种影响凝固过程的潜在机制尚未得到令人满意的解释。为此，PI和他的团队将开发一个联合的实验计算研究计划，重点是化学变质合金中的共晶形核。在他在实时成像方面的记录的基础上，他将利用“快速”X射线层析成像的新能力来观察掺有不同改性剂种类和浓度的铝硅共晶的凝固过程。考虑到此类实验需要收集的数据量(数十TB)，手动处理数据是不可能的。相反，PI和他的团队将实施最先进的机器学习算法(卷积神经网络)，以从大数据集中提取信息，包括形核密度、速率和过冷度的细节。这些丰富的定量发现提供了一个无与伦比的机会，可以非常精确地验证动力学理论。最后，PI的团队将进行进一步的相关显微研究--覆盖长度超过六个数量级的尺度--以推进共晶合金化学改性的综合模型。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

NON-TECHNICAL SUMMARYPatterns in nature typically form when a system changes from one phase of matter to another - for example, from a liquid phase to a geometrically patterned solid phase during solidification. The resulting patterns resemble a labyrinth of streets in a metropolitan area, although they are approximately one-hundred million times smaller and in three dimensions, called crystals. The structures of the as solidified crystals arranged in the pattern closely relate to the properties of the material, even after subsequent processing steps. To our advantage, the shapes and sizes of these crystals can be tailored to meet technological demands by manipulating the chemical composition of the parent liquid phase. For instance, trace metal impurities dissolved in the liquid are known to transform solid silicon (Si) from coarse, blocky particles into fine, web-like fibers. This results in a dramatic enhancement of the strength and ductility of the Si-based alloy and expands its potential for new applications, including space-frames and electric vehicles. The objective of this CAREER award is to understand how and why such transformations occur in the presence of trace metal impurities, by harnessing one of the brightest sources of hard X rays in the world at Argonne National Laboratory. The incident X-radiation can penetrate through an otherwise opaque metal, allowing one to capture the details of solidification in real time. Following the experiments, the PI and his team will extract information from the time lapse videos of solidification using state-of-the-art machine learning algorithms. It is anticipated that their new vision will advance the field of alloy solidification from metallurgical alchemy to predictive science. Ultimately, understanding the evolution of solid patterns during synthesis is the key to controlling the manufacture of advanced materials from the bottom-up. The new discoveries generated by this program will be integrated into problem-based learning units for underrepresented middle school students, in partnership with the Detroit Area Pre-College Engineering Program (DAPCEP). The PI will assess the impact of these activities using annual and multi-year surveys distributed through the DAPCEP organization.TECHNICAL SUMMARYIn the past 50 years, there has been increasing interest - both fundamental and applied - in the process of solidification in multicomponent metal alloys. One longstanding problem concerns microstructure formation in eutectic alloy systems. While binary eutectics with non faceted interfaces are relatively well understood, comparatively little is known about the solidification behavior of eutectics with faceted interfaces and/or more than two components. Such is the case for Al-Si eutectics that are solidified in the presence of trace metal species (so called chemical modifiers). The higher degrees of freedom in the univariate reaction (i.e., three components and two solid phases) brings about morphological transitions not seen in nonvariant eutectics. For instance, the chemical modifiers transform the morphology of the Si phase from coarse flakes to fine fibers, thereby improving the mechanical properties of the cast alloy. Despite our technical experience with chemical modification, the underlying mechanisms by which the trace metal species influences the solidification pathway have not yet been satisfactorily explained. To this end, the PI and his team will develop a combined experimental computational research program that will focus on eutectic nucleation in chemically-modified alloys. Building on his track record in real-time imaging, he will harness the new capabilities in "fast" X-ray tomography to watch the solidification of Al-Si eutectics doped with various modifier species and concentrations. Given the volume of data to be collected in such experiments (tens of TB), it is impossible to process the data manually. Instead, the PI and his team will implement state-of-the-art machine learning algorithms (convolutional neural networks) to extract information from the large datasets, including details on the nucleation density, rate, and undercooling. The richness of these quantitative findings provides an unparalleled opportunity to verify kinetic theories with great precision. Lastly, the PI's team will conduct further correlative microscopy - covering over six orders-of-magnitude in length scale - to advance a comprehensive model of chemical modification in eutectic alloys.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.