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.

难治性复合物浓缩合金（RCCA）是一种新的材料，具有巨大的高温结构应用潜力。这些合金表现出高温强度超过NI超合金，即当前的最新作品，但不幸的是，它们的耐腐蚀性远非理想。这种设计材料来彻底改变和设计我们的未来（DMREF）项目，旨在优化RCCA的组成，以实现高温下强度和氧化耐药性的无与伦比组合。这些属性将使旋转爆炸发动机能够实现国防上感兴趣的高超音速车辆，并在陆基燃气轮机为电网供电的终生中大大减少燃油消耗和污染。除了向研究生提供动手培训外，该计划还将支持将在材料科学，计算机模拟和机器学习中接触尖端研究工具的本科生。研究团队将与普渡大学的现有计划合作，并记录吸引包括代表性不足的人群在内的多样化和才华横溢的学生。 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不完全理解。将氧化测试变量（温度，O2的部分压）添加到成分的变量，鉴于高温氧化实验涉及的时间和成本，要探索的空间是17维的空间。原则上，基于物理学的建模可能有助于减少实验试验的数量，但是，预测复杂合金中氧化的能力是有限的。因此，该团队将开发一种迭代方法，该方法结合了多保真性和多成本实验和基于物理的建模，以用于加速材料发现（ML-AMD）框架。 ML-AMD将使用深层神经网络（DNN）的顺序学习来开发基于不同信息源（考虑到不确定性的范围）的模型，并确定要进行的模拟和实验，以最大程度地提高信息为设计目标。该奖项反映了NSF的法定任务，并通过评估了CR CRAINIAL IFFICTIAL和BRODICAIL的支持，并通过基金会的范围进行了评估和宽广的支持。

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