Relating glass forming ability and mechanical behavior to the structure of metallic liquids and glasses

非技术概述金属玻璃是一类独特的金属，其结构与更常见的晶体材料非常不同。大多数金属中的原子都是以规则有序的方式排列的，而金属玻璃则具有无序的原子结构，类似于液态。这种原子级的差异通常会导致大规模的显着不同的性能，包括更高的强度，更好的耐腐蚀性，以及令人惊讶的容易和廉价地模塑成复杂形状的能力。然而，并非所有金属都能形成玻璃。最近的研究表明，形成玻璃的能力可能与高温液体的原子结构有关。为了预测金属合金的玻璃形成能力而定量描述无序结构是具有挑战性的，因为原子位置没有长程模式。以前的尝试，以确定常见的多面体形状的原子结构是有益的，但提供有限的洞察这些形状之间的相似程度，因此，结构的整体程度的秩序，特别是合金组成的多种元素的不同原子大小。该项目使用基于人工智能的算法来自动识别计算机模拟液体中结构“相似”的原子簇。假设具有低程度的这种结构相似性的液体组合物是良好的玻璃形成剂。模拟结果将使用紧凑的材料库在广泛的组合物上进行实验验证，这些材料库使用先进的基于3D打印的方法快速合成。这项工作将加速发现用于各种应用的新型高性能金属材料，并有助于培养受过计算和高效制造方法培训的科学和工程人才。最后，它将通过针对来自不同学区的初中和高中教师的研讨会影响K-12科学和工程教育。技术概述金属玻璃具有优异的机械性能和独特的热塑性成型复杂形状的能力，因此作为潜在的结构材料具有巨大的前景。虽然可能具有变革性，但关于玻璃结构与性能之间的关系仍存在一些基本问题。特别是，液体的结构和它可以被淬火以形成玻璃的容易程度之间的关系仍然知之甚少。该项目的动机是最近的研究表明，合金系统内的组合物的相对玻璃形成能力可以通过表征液体中最近邻原子团簇的人口分布的简单参数来预测，远高于玻璃化转变温度。这项工作集成了分子动力学模拟，模式识别和机器学习聚类算法，以及最先进的高通量合成和表征方法，以研究模拟液体和玻璃中原子团簇的几何形状和种群与实验观察到的特性之间的相关性，在广泛的合金成分和家族中。基于激光沉积的合成技术用于快速构建合金库进行评估。在这些库中的玻璃形成区域的机械性能映射为通过纳米压痕组合物的函数。玻璃形成能力和机械性能的组成趋势将与模拟液体和玻璃中原子团簇的人口分布趋势进行比较。对于每个合金家族，将使用点模式匹配和机器学习聚类算法来识别一组独特且强大的原子图案，这些图案包括模拟液体和玻璃的结构。通过将模拟结构与实验性质测量相结合，这项工作将大大加强对金属玻璃结构-性质关系的理解，并使具有理想性质组合的新合金的合理设计成为可能。该奖项反映了NSF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 SummaryMetallic glasses are a unique class of metals with structures very different from the more common crystalline materials. Whereas atoms in most metals are arranged in a regular, ordered pattern, metallic glasses have a disordered atomic structure, similar to the liquid state. This difference at the atomic-level often results in dramatically different properties at the large scale, including higher strength, better corrosion resistance, and a surprising ability to be easily and inexpensively molded into complex shapes. Not all metals are able to form glasses, however. Recent studies suggest that the ability to form a glass may be related to the atomic structure of the high-temperature liquid. Quantitatively describing the disordered structure in order to predict the glass-forming ability of a metal alloy is challenging, since there is no long-range pattern to the atomic positions. Previous attempts to identify common polyhedral shapes in the atomic structure are instructive, but offer limited insight into the degree of similarity among those shapes and therefore the overall degree of structural order, particularly for alloys consisting of multiple elements of different atomic sizes. This project instead uses artificial-intelligence-based algorithms to automatically identify structurally ‘similar’ atomic clusters in computer-simulated liquids. Liquid compositions with a low degree of such structural similarity are postulated to be good glass formers. The results of the simulations will be experimentally verified over a wide range of compositions using compact material libraries, rapidly synthesized with an advanced 3D-printing-based method. This work will accelerate the discovery of new, high-performance metallic materials for a variety of applications, as well as contribute to the development of a science and engineering workforce trained in computational and efficient manufacturing methods. Finally, it will impact K-12 science and engineering education through workshops targeting middle- and high-school teachers from diverse school districts.Technical SummaryMetallic glasses hold tremendous promise as potential structural materials, due to their unique combination of excellent mechanical properties and unusual ability to be thermoplastically formed into complex shapes. While potentially transformative, several fundamental questions remain about the relationship between glass structure and properties. In particular, the relationship between the structure of the liquid and the ease with which it can be quenched to form a glass remains poorly understood. This project is motivated by recent studies suggesting that the relative glass-forming ability of compositions within an alloy system may be predicted by a simple parameter characterizing the population distribution of nearest-neighbor atomic clusters in the liquid, well above the glass transition temperature. The work integrates molecular dynamics simulations, pattern-recognition and machine-learning clustering algorithms, and state-of-the-art high-throughput synthesis and characterization methods to investigate correlations between the geometry and population of atomic clusters in simulated liquids and glasses and experimentally observed properties, over a wide range of alloy compositions and families. A laser-deposition-based synthesis technique is used to rapidly construct alloy libraries for evaluation. Mechanical properties of the glass-forming regions in these libraries are mapped as a function of composition via nanoindentation. Compositional trends in glass-forming ability and mechanical properties will be compared with trends in the population distribution of atomic clusters in simulated liquids and glasses. For each alloy family, point-pattern matching and machine-learning clustering algorithms will be used to identify a set of unique and robust atomic motifs that comprise the structure of the simulated liquids and glasses. By integrating the simulated structures with experimental property measurements, this work will dramatically strengthen the understanding of structure-property relationships in metallic glasses, and enable the rational design of new alloys with desirable combinations of properties.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.