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: 2415119
• 负责人: Wei Chen
• 金额: $ 50万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2415119&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.

非技术摘要该职业奖支持研究和教育活动，以开发量子力学和机器学习方法，以在原子水平上理解和设计复杂的多元素合金。该项目重点关注复杂浓缩合金（CCA），这是一类混合原子的新型合金。研究 CCA 的科学动力是了解和利用与多种元素相关的巨大化学和结构设计空间，以寻找新材料特性的当前理解。合金仅限于多元素空间的角落和边缘，例如二元或稀释合金，对于具有四种或更多元素的系统来说，接近成分空间中心的 CCA 信息实际上不存在。通过（i）通过结合量子力学计算和统计力学方法建立对 CCA 中原子结构的准确预测理解，以及（ii）整合量子力学计算、经验模型和闭环机器学习预测 CCA 结构和缺陷特征的方法，以加速 CCA 结构或功能应用的设计。该项目的多学科性质将多个学术领域的观点带入材料研究的前沿。美国在基础合金研究方面处于领先地位。该项目的教育和推广活动包括五个综合部分，涉及学习工具创新、扩大参与、青年材料教育、暑期研究接触和研究职业发展。创建一个具有互补专业知识的多学科团队加强科学、技术、工程和数学教育，提高材料科学意识。将与亚马逊合作开发基于云的学习应用程序，以移植 PI 的研究成果，并向公众介绍材料和数据科学。将与 SMASH 伊利诺伊州合作，为代表性不足的学生提供学术和社会项目，以扩大材料教育的参与范围，同时，与 North Central College 和 Questek 合作的夏令营以及与 Adlai E. Stevenson 高中合作的高中研究项目也将扩大。揭露PI 还将与本科生和研究生密切合作，通过基于项目的研究计划促进多学科职业发展。技术摘要该职业奖支持研究和教育活动，以开发第一原理和数据驱动方法，以实现材料科学的发展。了解复杂浓缩合金 (CCA) 中短程有序 (SRO) 的原子性质，以及这种化学有序如何影响晶格畸变、动力学和缺陷结构，从而为设计新型先进合金创造机会。晶格畸变是与 CCA 中的各种物理和化学性质相关的重要现象，但是，人们对 CCA 中严重晶格畸变的性质知之甚少，特别是与 SRO 耦合时，PI 将研究 SRO 和相关的晶格畸变。该研究将阐明 SRO 对 CCA 晶格畸变结构的影响，并将其用于量化 CCA 的影响。 CCA 声子特性的扭曲晶格将应用于建立将 CCA 中的缺陷特性与局部环境联系起来的预测映射，为 CCA 的卓越机械性能的计算设计奠定基础。该项目的教育和推广活动包括五个综合部分，涉及学习工具创新、扩大参与、青年物质教育、暑期研究该项目汇集了国家和地方合作伙伴，创建一个具有互补专业知识的多学科团队，以加强科学、技术、工程和数学教育，并与亚马逊合作，提高人们对材料科学的认识。将开发基于学习的应用程序，以移植 PI 的研究并向公众介绍材料和数据科学。 PI 将与 SMASH 伊利诺伊州合作，为代表性不足的学生提供学术和社会项目，以扩大对材料教育的参与。与中北学院和Questek 以及与 Adlai E. Stevenson 高中的高中研究项目将得到扩展，让年轻一代接触材料科学。PI 还将与本科生和研究生密切合作，通过基于项目的研究项目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。该奖项反映了 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，认为值得支持。