CAREER: First-principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics

职业：复杂浓缩合金中化学顺序的第一原理预测性理解：结构、动力学和缺陷特征

• 批准号: 1945380
• 负责人: Wei Chen
• 金额: $ 50万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945380&HistoricalAwards=false
• 关键词: CAREER; First; principles; Predictive; Understanding

NONTECHNICAL SUMMARYThis CAREER award supports research and educational activities to develop quantum mechanical and machine learning methods to understand and design complex multi-element alloys at the atomic level. The project focuses on complex concentrated alloys (CCAs), a class of novel alloys that mix atoms of different species at nearly equal ratios. The scientific drive for studying CCAs is to understand and utilize the vast chemical and structural design space associated with multiple elements in search of new materials properties. Current understanding about the stability, structures, and properties of alloys is limited to the corners and edges of the multi-element space, such as binary or dilute alloys. The information for CCAs close to the center of the composition space is virtually non-existent for systems with four or more elements. The project intends to fill this knowledge gap in alloy theory for these complex alloy systems by (i) establishing an accurate predictive understanding of the atomic structures in CCAs through a combination of quantum mechanical calculations and statistical mechanics methods, and (ii) integrating quantum mechanical calculations, empirical models and close-loop machine learning methods to predict the structural and defect features in CCAs for accelerated design of CCAs for structural or functional applications. The multidisciplinary nature of the project brings perspectives from multiple academic fields into the forefront of materials research. The focus of the technologically relevant CCAs will strengthen the U.S. leadership in fundamental alloy research. The education and outreach activities of the project includes five integrated parts that address learning tool innovation, broadening participation, youth material education, summer research exposure, and research career development. The project brings together national and local partners to create a multidisciplinary team with complementary expertise to strengthen Science, Technology, Engineering, and Mathematics education and raise the awareness of materials science. In collaboration with Amazon, a cloud-based learning app will be developed to transplant the PI’s research and introduce materials and data science to the general public. The PI will collaborate with SMASH Illinois to offer academic and social programs to underrepresented students to broaden participation in materials education. In parallel, summer camps with North Central College and Questek, as well as high school research programs with Adlai E. Stevenson High School will be expanded to expose the younger generation to materials science. The PI will also work closely with undergraduate and graduate students to foster multidisciplinary career development via project-based research programs.TECHNICAL SUMMARYThis CAREER award supports research and educational activities to develop first-principles and data-driven methods to understand the atomic nature of short range order (SRO) in complex concentrated alloys (CCAs) and how such chemical order influences lattice distortion, dynamics, and defect structures, thus creating opportunities for designing new advanced alloys. Severe lattice distortion is an important phenomenon that is correlated to a variety of physical and chemical properties in CCAs. However, the nature of severe lattice distortions in CCAs is poorly understood, especially with the coupling of SRO. The PI will study SRO and related lattice distortions in CCAs with a unique synergy of mechanism investigation, predictive modeling, and methodology development. The research will elucidate SRO on the structures of lattice distortions in CCAs, which will be utilized to quantify the impact of the distorted lattices on the phonon characteristics of CCAs. Results and methodology from bulk CCAs will be applied to establish a predictive mapping linking defect characteristics with local environments in CCAs, providing the foundation for computational design of CCAs for superior mechanical properties. The project will be driven by the parallel research on a hierarchical data-driven computational framework that enables efficient predictions of structure-property relationships for CCAs. The education and outreach activities of the project includes five integrated parts that address learning tool innovation, broadening participation, youth material education, summer research exposure, and research career development. The project brings together national and local partners to create a multidisciplinary team with complementary expertise to strengthen Science, Technology, Engineering, and Mathematics education and raise the awareness of materials science. In collaboration with Amazon, a cloud-based learning app will be developed to transplant the PI’s research and introduce materials and data science to the general public. The PI will collaborate with SMASH Illinois to offer academic and social programs to underrepresented students to broaden participation in materials education. In parallel, summer camps with North Central College and Questek, as well as high school research programs with Adlai E. Stevenson High School will be expanded to expose the younger generation to materials science. The PI will also work closely with undergraduate and graduate students to foster multidisciplinary career development via project-based research programs.This award is jointly supported by the Division of Materials Research and the NSF Office of Advanced Cyberinfrastructure.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.

非技术摘要这一职业奖支持研究和教育活动，以开发量子机械和机器学习方法，以理解和设计原子水平的复杂多元素合金。该项目着重于复杂的浓缩合金（CCA），这是一类新型合金，它们以几乎相等的比率混合不同物种的原子。研究CCA的科学驱动器是了解和利用与多个元素相关的庞大化学和结构设计空间来寻找新材料。当前对合金的稳定性，结构和特性的理解仅限于多元素空间的角和边缘，例如二元或稀释合金。对于具有四个或多个元素的系统，靠近组成空间中心的CCA的信息实际上是不存在的。该项目打算通过（i）通过（i）通过量子机械计算和统计机械方法的结合来确定对CCA中原子结构的准确预测理解，以及（ii）整合量子机械计算，经验机械模型和近距离机器学习方法，以预测CC的结构和偏F型，以预测CCAS的功能，CCAS在CCAS中整合CCAS的ccas或CCAS，CCAS在CCAS中积累了CCAS的ccas或CCAS，CCAS在CCAS中建立了准确的预测理解。申请。该项目的多学科性质将多个学术领域的观点带入了材料研究的最前沿。技术相关的CCA的重点将加强美国在基本合金研究中的领导。该项目的教育和外展活动包括五个综合零件，这些零件可以解决学习工具创新，扩大参与，青年材料教育，夏季研究暴露和研究职业发展。该项目汇集了国家和地方合作伙伴，以建立一个具有完善专业知识的多学科团队，以增强科学，技术，工程和数学教育，并提高对材料科学的认识。与亚马逊合作，将开发一个基于云的学习应用程序，以移植Pi的研究，并向公众介绍材料和数据科学。 PI将与Smash Illinois合作，为代表性不足的学生提供学术和社会计划，以扩大参与材料教育的参与。同时，与北中央学院和Questek的夏令营以及Adlai E.史蒂文森高中的高中研究计划将被扩展，以使年轻一代暴露于材料科学上。 PI还将与本科生和研究生紧密合作，通过基于项目的研究计划来促进多学科职业发展。技术摘要这一职业奖支持研究和教育活动，以开发第一原理和数据驱动的方法，以了解复杂级别（CCAS）的短距离订单（SRO）的原子性质（CCAS）的构造（CCAS）的构造（CCAS）的构造（CCAS）的差异（CCAS），并具有化学性差异（CCAS）的范围（CCAS）的差异。设计新的高级合金。严重的晶格失真是一个重要的现象，与CCA中的各种物理和化学特性相关。然而，CCA中严重晶格扭曲的性质知之甚少，尤其是在SRO偶联的情况下。 PI将研究CCA中的SRO和相关晶格扭曲，具有独特的机制研究，预测建模和方法论的独特协同作用。这项研究将阐明CCA中晶格扭曲的结构的SRO，该结构将用于量化扭曲的晶格对CCA的声子特性的影响。将采用大量CCA的结果和方法来建立一个预测映射，将缺陷特征与CCA中的本地环境联系起来，从而为CCA的计算设计提供了用于上等机械性能的计算设计。该项目将由对层次数据驱动的计算框架的平行研究驱动，该研究能够有效地预测CCA的结构 - 培训关系。该项目的教育和外展活动包括五个综合零件，这些零件可以解决学习工具创新，扩大参与，青年材料教育，夏季研究暴露和研究职业发展。该项目汇集了国家和地方合作伙伴，以建立一个具有完整专业知识的多学科团队，以增强科学，技术，工程和数学教育，并提高对材料科学的认识。与亚马逊合作，将开发一个基于云的学习应用程序，以移植Pi的研究，并向公众介绍材料和数据科学。 PI将与Smash Illinois合作，为代表性不足的学生提供学术和社会计划，以扩大参与材料教育的参与。同时，与北中央学院和Questek的夏令营以及Adlai E.史蒂文森高中的高中研究计划将被扩展，以使年轻一代暴露于材料科学上。 PI还将通过基于项目的研究计划与本科和研究生紧密合作，以促进多学科职业发展。该奖项由材料研究部和NSF高级网络基础设施办公室共同支持。本奖反映了NSF的法规任务，并通过对基金会的智力效果进行评估而被视为珍贵。

项目成果

Highly Localized C–N2 Sites for Efficient Oxygen Reduction

• DOI: 10.1021/acscatal.0c00474
• 发表时间: 2020-07
• 期刊: ACS Catalysis
• 影响因子: 12.9
• 作者: Qiangjian Ju;Ruguang Ma;Yifan Hu;Beibei Guo;Qian Liu;Tiju Thomas;Tao Zhang;Minghui Yang;Wei Chen;Jiacheng Wang

Microstructure and mechanical behavior of additively manufactured CoCrFeMnNi high-entropy alloys: Laser directed energy deposition versus powder bed fusion

• DOI: 10.1016/j.actamat.2023.118884
• 发表时间: 2023-03-31
• 期刊: ACTA MATERIALIA
• 影响因子: 9.4
• 作者: Liu, Yanfang;Ren, Jie;Chen, Wen
• 通讯作者: Chen, Wen

Temperature dependence of elastic and plastic deformation behavior of a refractory high-entropy alloy

• DOI: 10.1126/sciadv.aaz4748
• 发表时间: 2020-09-01
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Lee, Chanho;Kim, George;Liaw, Peter K.
• 通讯作者: Liaw, Peter K.

Elastic behavior of binary and ternary refractory multi-principal-element alloys

• DOI: 10.1016/j.matdes.2022.110820
• 发表时间: 2022-06
• 期刊: Materials & Design
• 作者: R. Feng;George Kim;Dunji Yu;Yan Chen;Wei Chen;P. Liaw;Ke An

First-principles comparative study of Cr migration in O3 and O/P hybrid-phased NaCrO2

• DOI: 10.1103/physrevmaterials.6.095403
• 发表时间: 2022-09
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Jialiang Wei;L. Shaw;Wei Chen