DMREF: Discovery of high-temperature, oxidation-resistant, complex, concentrated alloys via data science driven multi-resolution experiments and simulations

DMREF：通过数据科学驱动的多分辨率实验和模拟发现高温、抗氧化、复杂、浓缩合金

• 批准号: 1922316
• 负责人: Alejandro Strachan
• 金额: $ 173.88万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922316&HistoricalAwards=false
• 关键词: DMREF; Discovery; temperature; oxidation; resistant

Refractory complex concentrated alloys (RCCAs) are a new class of materials with an enormous potential for high-temperature structural applications. These alloys exhibit high-temperature strength surpassing Ni superalloys, the current state-of-the-art, but, unfortunately, their corrosion resistance is far from ideal. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to optimize the composition of RCCAs to achieve an unsurpassed combination of strength and oxidation resistance at high-temperatures. These properties would enable the realization of rotation detonation engines for hypersonic vehicles of interest in national defense and a significant reduction in fuel consumption and pollution over the lifetime of a land-based gas turbines that power the electric grid. In addition to providing hands-on training to graduate students, this program will support undergraduate students who will be exposed to cutting edge research tools in materials science, computer simulations and machine learning. The research team will partner with existing programs at Purdue with a track record of attracting a diverse and talented cadre of students, including underrepresented populations. To encourage widespread use of the technology and data developed, the products of this project will be made available via the nanoHUB open platform, where students, educators, and researchers can explore data and perform simulations online, using a web-browser.The design and optimization of RCCAs with the combination of properties sought after for high temperature structural applications is a daunting technical task due to the extremely large number of potential alloys, and because the oxidation behavior of these complex alloys is not fully understood. Adding oxidation testing variables (temperature, partial pressure of O2) to the compositional ones, the space to be explored is 17 dimensional, which is clearly out of reach to brute force approaches given the time and cost involved in high-temperature oxidation experiments. Physics-based modeling could, in principle, help reduce the number of experimental trials, however, the ability to predict oxidation in complex alloys is limited. Thus, the team will develop an iterative approach that combines multi-fidelity and multi-cost experiments and physics-based modeling within a machine learning for accelerated materials discovery (ML-AMD) framework. ML-AMD will use sequential learning with deep neural networks (DNNs) to develop models based on disparate sources of information (accounting for uncertainties) and identify simulations and experiments to carry out in order to maximize information gain towards the design goal.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.

难熔高浓度复合合金（RCCA）是一类在高温结构应用中具有巨大潜力的新型材料。这些合金的高温强度超过了目前最先进的镍超合金，但不幸的是，它们的耐腐蚀性远不理想。这个设计材料革命和工程我们的未来（DMREF）项目旨在优化RCCA的组成，以实现高温下强度和抗氧化性的无与伦比的组合。这些特性将使国防利益的高超声速飞行器实现旋转爆震发动机，并在为电网供电的陆基燃气轮机的寿命期间显著减少燃料消耗和污染。除了为研究生提供实践培训外，该计划还将支持本科生接触材料科学，计算机模拟和机器学习方面的尖端研究工具。该研究团队将与普渡大学现有的项目合作，吸引多样化和有才华的学生干部，包括代表性不足的人群。为了鼓励广泛使用所开发的技术和数据，该项目的产品将通过nanoHUB开放平台提供，学生，教育工作者和研究人员可以在线探索数据并进行模拟，使用Web-具有高温结构应用所追求的性能组合的RCCA的设计和优化是一项艰巨的技术任务，这是由于大量的潜在的合金，并且因为这些复杂合金的氧化行为还没有完全理解。将氧化测试变量（温度、O2分压）添加到组成变量，待探索的空间是17维的，考虑到高温氧化实验中涉及的时间和成本，这显然是蛮力方法无法达到的。原则上，基于物理的建模可以帮助减少实验试验的数量，但是，预测复杂合金氧化的能力有限。因此，该团队将开发一种迭代方法，将多保真度和多成本实验与基于物理的建模结合在机器学习加速材料发现（ML-AMD）框架内。ML-AMD将使用深度神经网络（DNN）的顺序学习，基于不同的信息源（考虑不确定性）开发模型，并确定要进行的模拟和实验，以最大限度地获得设计目标的信息。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Modeling environment-dependent atomic-level properties in complex-concentrated alloys

对复杂浓缩合金中与环境相关的原子级特性进行建模

• DOI: 10.1063/5.0076584
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Farnell, Mackinzie S.;McClure, Zachary D.;Tripathi, Shivam;Strachan, Alejandro
• 通讯作者: Strachan, Alejandro

High-temperature mechanical properties and oxidation behavior of Hf-27Ta and Hf-21Ta-21X (X is Nb, Mo or W) alloys

• DOI: 10.1016/j.ijrmhm.2020.105467
• 发表时间: 2021-01-13
• 期刊: INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS
• 影响因子: 3.6
• 作者: Senkov，O. N.;Daboiku，T.;Payton，E. J.
• 通讯作者: Payton，E. J.

Expanding Materials Selection Via Transfer Learning for High-Temperature Oxide Selection

通过高温氧化物选择的迁移学习扩大材料选择

• DOI: 10.1007/s11837-020-04411-1
• 发表时间: 2021
• 期刊: JOM
• 影响因子: 2.6
• 作者: McClure, Zachary D.;Strachan, Alejandro
• 通讯作者: Strachan, Alejandro

Comparing the accuracy of melting temperature prediction methods for high entropy alloys

• DOI: 10.1063/5.0101548
• 发表时间: 2022-11
• 期刊: Journal of Applied Physics
• 影响因子: 3.2
• 作者: Saswat Mishra;Karthik Guda Vishnu;A. Strachan

Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements

实验数据融合的分层贝叶斯方法：根据硬度测量预测高熵合金的强度的应用

• DOI: 10.1016/j.commatsci.2022.111851
• 发表时间: 2023
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Karumuri, Sharmila;McClure, Zachary D.;Strachan, Alejandro;Titus, Michael;Bilionis, Ilias
• 通讯作者: Bilionis, Ilias