CAREER: First-Principles Predictions of Solute Effects on Defect Stability and Mobility in Advanced Alloys

职业：溶质对先进合金缺陷稳定性和迁移率影响的第一性原理预测

• 批准号: 1847837
• 负责人: Liang Qi
• 金额: $ 50万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847837&HistoricalAwards=false
• 关键词: CAREER; First; Principles; Predictions; Solute

NONTECHNICAL SUMMARYThis CAREER award supports research and education activities in developing and applying computational methods for understanding the behavior of crystalline defects in metal alloys, which are metallic materials composed by more than one chemical element. Atoms in metals and alloys are arranged in crystal structures with almost perfect periodic patterns, but these structures still contain a small number of imperfections or "defects", variations from perfect periodicity. Many mechanical and physical properties depend on how defects are generated and move under the influences of alloying elements, or solutes, in crystal structures. For example, the strength and ductility of metals and alloys are usually controlled by a type of defect called dislocations, and some solute atoms can slow down or speed up dislocation motions to make alloys stronger and more brittle, or softer and more ductile. This project will focus on defects in advanced alloys based on certain transition-metal elements, such as tungsten, molybdenum, titanium, and zirconium. These alloys can have excellent mechanical properties at high temperatures and are critical for many energy, transport, and aerospace applications, such as structural components of nuclear reactors, turbine engines and electric generators. To develop these advanced alloys with enhanced performance would require the understanding of solute effects on defect behaviors and the corresponding variations of mechanical properties.The objective of this project is to develop and apply computational methods to predict how solute atoms affect the stability and motion mechanisms of defects in advanced transition-metal alloys. Conventionally, accurate quantum mechanical calculations are applied to study metals and alloys in perfect crystal structures or containing simplified defect structures, but defect behaviors in realistic alloys depend on the evolution of complex structures on the nanoscale and mesoscale, lengths comparable with one-thousandth of a millimeter. The PI aims to discover intrinsic, universal and quantitative mechanisms that determine defect-solute interactions based on analyses from quantum mechanical calculations. Using these results, the PI will develop the models to predict the solute effects on defect behavior and mechanical properties by bridging the knowledge gaps between electronic, atomistic and mesoscale levels. The research will provide generalized models, computational methods, software tools, and an open access data repository for both the scientific and industrial communities to speed up the development of novel advanced alloys in large compositional space. The PI also proposes educational and outreach activities by integration of research and innovative teaching methods. The PI plans to apply virtual reality techniques in teaching complex crystal and defect structures to undergraduates, and to utilize the tools of computational materials science in teaching alloy designs to graduate students. Proposed outreach activities include the education of K-12 students with a diverse background on the topics of crystal structures and thermodynamics based on their interest and familiar subjects such as food processing to increase the public awareness of materials science and engineering. The PI will also participate in the high-school research projects by the Center for Engineering Diversity and Outreach at University of Michigan. All education modules based on virtual reality and simulation tools will also be shared through the public data repository.TECHNICAL SUMMARYThis CAREER award supports an integrated research and education project to develop new computational approaches to study defect-solute interactions and their effects on the mechanical properties of advanced transition metal alloys by bridging the gaps across electronic, atomistic and mesoscale length scales. Interactions between solute atoms and crystalline defects, including dislocations, and twin and grain boundaries, play essential roles in determining the mechanical and physical properties of many advanced alloys. First-principles theory is ideal for investigating such interactions. However, first-principles calculations are limited by increasing computational intensity with system size, making it difficult to predict the solute and impurity effects on the stability and mobility of defects involving complex atomistic structures, such as dislocation kinks, twin nuclei, and grain boundary complexions.Recently, the PI discovered a series of strong correlations between defect energetics and local electronic structures in several types of advanced transition metal alloys. These new findings suggest a path to predict accurately complex defect-solute interactions by understanding chemical bonding mechanisms at the electronic level. Based on this approach, the PI aims to: (i) identify generalized and quantitative correlations between local electronic/atomistic structures and defect-solute interactions for multiple types of defects and solutes based on chemical bonding models, first-principles calculations and machine learning methods, (ii) apply the above correlations to construct mesoscale simulation methods and phenomenological models to predict the solute/impurities effects on defect stability and mobility, and (iii) employ the above methods and models to evaluate the mechanical behavior, such as solid-solution hardening/softening, twinability, and grain boundary embrittlement. The proposed research aims to advance fundamental understanding of the intrinsic physical mechanisms of defect-solute interactions that are critical for advanced transition metal alloys to achieve excellent mechanical performance under varying environmental conditions. It will explore the application of machine learning methods for alloy design based on physical models at electronic and atomistic levels. The investigated defect structures will be incorporated into the virtual reality tools to enhance the undergraduates' understanding of lattice and chemical defects and their effects on materials properties; the generated data and computational tools will be applied in education modules to help graduate students to learn the state-of-the-art materials design approaches. These data and tools will also be utilized in outreach education and research activities for K-12 students with diverse backgrounds.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.

项目成果

Composition & predicted values (surface energy, stacking fault energy and ductility parameter) for 1184 screened alloys.

作品

• DOI: 10.13011/m3-kptn-e839
• 发表时间: 2022
• 期刊: Materials Commons
• 作者: Sundar, Aditya

Automated hierarchical screening of refractory multicomponent alloys with high intrinsic ductility and surface passivation potency

• DOI: 10.1557/s43579-022-00241-1
• 发表时间: 2022-09
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: A. Sundar;David Bugallo Ferron;Yong-Jie Hu;L. Qi

Local electronic descriptors for solute-defect interactions in bcc refractory metals

• DOI: 10.1038/s41467-019-12452-7
• 发表时间: 2019-10-02
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Hu, Yong-Jie;Zhao, Ge;Qi, Liang
• 通讯作者: Qi, Liang

Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys

• DOI: 10.1038/s41524-023-00993-x
• 发表时间: 2023-04
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Chris Tandoc;Yong-Jie Hu;L. Qi;P. Liaw

Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

• DOI: 10.1038/s41524-023-01046-z
• 发表时间: 2022-03
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Huijin Zheng;Lauren T. W. Fey;Xiang-Guo Li;Yong-Jie Hu;L. Qi;Chi Chen;Shuozhi Xu;I. Beyerlein;S. Ong