Collaborative Research: Microscopic Mechanism of Surface Oxide Formation in Multi-Principal Element Alloys

非技术概述多主元素合金（MPEA）是由五种或更多种元素以相对相等的比例组成的组成复杂的合金。某些MPEA已经显示出优良的上级机械性能（例如，硬度，强度），这使得它们对于高温应用是理想的（例如，燃气涡轮机叶片和再入飞行器的表面涂层）。顺便说一句，氧化降解是一个关键的材料挑战，仍然必须克服，以提高这些应用中的性能。由于复杂的组成组合，制造抗氧化MPEAs是不平凡的。在该项目中，通过最先进的实验表征和计算机模拟，在人工智能（AI）辅助的创新数据自适应发现策略的指导下，建立了抗氧化MPEAs的组成-加工-结构-性质关系。从根本上说，从原子到微米尺度的氧化机制是通过实验发现和嵌入材料设计和表面工程框架中的机器学习来研究的。该项目的结果为发现和创建适用于高温操作的抗氧化MPEAs提供了先进的材料范例。技术概述多主元素合金（MPEAs）是典型地由五种或更多种元素以显著比例组成的浓缩的随机固溶体。虽然显著的机械性能（例如，硬度或强度）的提高促进了它们在高温下工作的部件的潜在应用，例如在燃气涡轮机叶片或再入飞行器的表面涂层中发现的那些部件。在这些组件的操作条件下，氧化降解仍然是一个关键的材料挑战。因此，在抗氧化MPEAs的合成过程中，这些复杂合金的元素组成的多样性转化为广泛范围的可能的氧化产物，许多对氧渗透到本体合金中的抗性差。为了应对这一挑战并探索相关的巨大组成-加工-结构-性质景观，通过创新和实验验证的数据指导自适应方法研究了从原子尺度氧化学吸附到微米氧化物尺度形成的氧化机制。正在开发用于合成和加工具有改进的抗氧化性的MPEAs的表面工程范例。该框架的核心是四个研究进展：（1）MPEA概念探索;（2）表面工程原子表征;（3）密度泛函理论计算;和（4）自适应发现。这项研究的结果预计将通过在能源，高超音速应用，国防和医疗保健等广泛行业的表面工程MPEA的创新对社会产生重大的经济影响。这项及时的研究属于基础材料物理，预测合成和数据科学领域，并为表面物理，材料科学，数据分析和计算材料领域的研究人员提供独特的协作和跨学科研究和培训经验。通过跨大学教学和评估，交流访问和共同建议的学生培训，加上技术创新和创业经验，研究与教育相结合。在这个项目中，PI还积极参与针对大学预科女性和代表性不足的少数民族学生的项目，以鼓励他们追求STEM职业。这个奖项反映了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。

NON-TECHNICAL SUMMARYMulti-principal element alloys (MPEAs) are compositionally complex alloys consisting of five or more elements in relatively equal proportions. Certain MPEAs have demonstrated superior mechanical properties (e.g., hardness, strength) unattainable from traditional alloys, making them ideal for high temperature applications (e.g., gas turbine blades and surface coatings for reentry vehicles). Incidentally, degradation by oxidation is a critical material challenge that must still be overcome to improve performance in such applications. Due to the complex compositional combinations, making oxidation resistant MPEAs is nontrivial. In this project, composition-processing-structure-property relationships for oxidation-resistant MPEAs are established through state-of-the-art experimental characterizations and computer simulations, guided by an artificial intelligence (AI) assisted innovative data-adaptive discovery strategy. Fundamentally, the oxidation mechanism¬ from the atomic to micro-meter scale is studied through both experimental discovery and machine learning embedded within a materials design and surface engineering framework. The results of this project enable an advanced materials paradigm for the discovery and creation of oxidation resistant MPEAs applicable for high temperature operations. TECHNICAL SUMMARYMulti-principal element alloys (MPEAs) are concentrated random solid-solutions typically consisting of five or more elements in significant proportions. While the remarkable mechanical properties (e.g., hardness, or strength) of certain MPEAs have encouraged their potential use for components operating at high temperatures such as those found in gas turbine blades or surface coatings for reentry vehicles. At the operation conditions for such components, degradation by oxidation remains a critical materials challenge. Consequently, during synthesis of oxidation resistant MPEAs, the versatility in elemental compositions for these complex alloys translates to an extensive range of possible oxidation products, many with poor resistance to the penetration of oxygen into the bulk alloy. To address this challenge and explore the associated enormous composition-processing-structure-property landscape, the oxidation mechanism¬ from atomic scale oxygen chemisorption to micro-meter oxide scale formation are studied by an innovative and experimentally validated data-guided adaptive approach. A surface engineering paradigm for the synthesis and processing of MPEAs with improved oxidation resistance are being developed. Central to the proposed framework are four research developments: (1) MPEA Concept Exploration; (2) Surface Engineering & Atomic Characterization; (3) Density Functional Theory Calculations; and (4) Adaptive Discovery. The results of this research are expected to have significant economic impact on society through innovations in surface-engineered MPEAs across a wide range of industries such as energy, hypersonic applications, defense and healthcare. This timely research lies within the domains of fundamental materials physics, predictive synthesis, and data science and offers unique collaborative and interdisciplinary research and training experiences for researchers across the fields of surface physics, material science, data analytics, and computational materials. Research is integrated with education through cross-university teaching and assessment, exchange-visits and co-advised student training coupled with technology innovation and entrepreneurial experiences. In this project, PIs also actively engage with programs for pre-college women and underrepresented minority students to encourage them to pursue STEM careers.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.