DMREF: Discovery, Development, Design and Additive Manufacturing of Multi-Principal-Element Hexagonal-Close-Packed Structural Alloys

目前，冶金领域正在经历一场复兴，这是因为人们意识到，通过将许多不同类型的原子按大致相等的比例混合，可以形成有用的结构合金。这些材料被称为“多主元素”合金(MPEA)，这种方法已经导致了新的强韧性合金的发现。然而，这些例子只基于元素周期表中的一小部分原子，以及有限数量的潜在晶体结构。最初的发现令人鼓舞，但MPEA的真正潜力还有待挖掘。在这个设计材料革命和设计我们的未来(DMREF)项目中，先进的材料理论和高通量计算和实验与机器学习工具相结合，加速了一类相对未知的MPEA的发现和发展，其中合金的原子以六边形的方式排列。这一研究领域已经成熟，可以针对目标应用进行变革性的发现，包括本项目的重点：受空间探索中遇到的条件启发，用于低温结构应用的更坚固和更轻的合金。该项目强调可以通过添加制造来制造的合金，通常被称为金属3D打印。因此，合金将可用于直接的技术应用，因为添加制造提供了在微观尺度上快速制造具有复杂几何形状和定制结构的部件的巨大机会。这些研究目标将通过利用材料数据的力量来实现，同时培养下一代材料研究人员，因此，该项目与材料基因组倡议的目标很好地结合在一起。更详细地说，该项目专注于发现和开发在六方紧密堆积(HCP)结构中结晶的MPEA。最初的焦点是由钛、钪、Y、锆和Hf元素形成的合金。通过计算一组已识别的描述符，包括形成能、晶格参数、弹性常数、层错和孪生能，从理论上探索组成空间。该方法将利用新的通用原子间势进行初始筛选，使用基于密度泛函理论的方法结合旨在对MPEA中遇到的各种组成排列进行平均的方法，对候选系统进行更定量的详细研究。同时，将使用定向能沉积激光系统和浓度梯度方法合成MPEA，这种方法允许在一个样品中合成广泛的组成范围。延展性将通过快速纳米压痕筛选和低温微拉伸测试来表征。由此产生的数据将被用于开发和改进机器学习模型，这反过来将导致对新材料的建议。然后这些材料将被合成，并重复这一过程。这一迭代过程最终将在可计算数据和可观察到的机械性能之间建立关联，从而能够发现和开发用于低温应用的添加制造的HCP MPEA。该奖项反映了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。

Presently, the field of metallurgy is undergoing a renaissance spurred on by the realization that useful structural alloys can be formed by mixing many different types of atoms in roughly equal proportions. These materials are referred to as “multi-principal-element” alloys (MPEAs), and this approach has already given rise to the discovery of new strong and ductile alloys. However, these examples have been based on only a small subset of the atoms in the periodic table, and only a limited number of underlying crystal structures. The initial discoveries are encouraging, but the true potential of MPEAs is yet to be tapped. In this Designing Materials to Revolutionize and Engineer our Future (DMREF) project, advanced materials theory and high-throughput computation and experiments are combined with the tools of machine learning to accelerate the discovery and development of a relatively unexplored class of MPEAs in which the atoms of the alloy are arranged in a hexagonal pattern. This research area is ripe for transformative discoveries for targeted applications including the focus of this project: stronger and lighter alloys for low temperature structural applications inspired by the conditions encountered in space exploration. The project emphasizes alloys that can be fabricated through additive manufacturing, commonly referred to as metal 3D printing. Hence the alloys will be available for immediate technological applications because additive manufacturing offers great opportunities for rapid fabrication of components with complex geometries and tailored structures at the microscopic scale. These research goals will be achieved by harnessing the power of materials data while educating the next generation of materials researchers, and accordingly, the project is well aligned with the goals of the Materials Genome Initiative.In more detail, the project focuses on discovering and developing MPEAs crystallizing in the hexagonal-close-packed (HCP) structure. The initial focus is on alloys formed from the elements titanium, scandium, yttrium, zirconium, and hafnium. The composition space will be explored theoretically through computation of a set of identified descriptors including formation energies, lattice parameters, elastic constants, stacking fault and twin energies. The approach will leverage new classes of universal interatomic potentials for initial screening, with candidate systems investigated in more quantitative details using density functional theory based methods coupled with approaches designed to average over the wide variety of compositional arrangements encountered in MPEAs. Simultaneously, MPEAs will be synthesized using directed energy deposition laser systems and a concentration gradient approach that allows synthesis of a broad composition range within one sample. Ductility will be characterized using rapid nanoindentation screening and cryogenic micro-tensile testing. The resulting data will be used to develop and improve machine learning models that will, in turn, lead to suggestions for new materials. These materials will then be synthesized, and the process repeated. This iterative process will, ultimately, establish correlations between computable data and observable mechanical properties that enable the discovery and development of additively manufactured HCP MPEAs for low temperature applications.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.