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.

非技术总结这个职业奖项支持研究和教育活动，以开发量子力学和机器学习方法，在原子水平上理解和设计复杂的多元素合金。该项目的重点是复杂的浓缩合金(CCA)，这是一类以几乎相等的比例混合不同物种的原子的新型合金。研究CCA的科学动力是理解和利用与多种元素相关的巨大化学和结构设计空间，以寻找新的材料性能。目前对合金稳定性、结构和性质的了解仅限于多元素空间的角落和边缘，如二元或稀合金。对于具有四个或更多元素的系统，接近组成空间中心的CCA的信息实际上是不存在的。该项目旨在通过以下方式填补这些复杂合金系统在合金理论方面的知识空白：(I)通过量子力学计算和统计力学方法的结合，建立对CCA中原子结构的准确预测理解，以及(Ii)集成量子力学计算、经验模型和闭环机器学习方法来预测CCA中的结构和缺陷特征，以加速结构或功能应用的CCA的设计。该项目的多学科性质将来自多个学术领域的观点带入了材料研究的前沿。与技术相关的CCA的重点将加强美国在基础合金研究方面的领导地位。该项目的教育和宣传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。该项目汇集了国家和地方合作伙伴，创建了一个多学科团队，具有互补的专业知识，以加强科学、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，将开发一款基于云的学习应用程序，以移植PI的研究，并向普通公众介绍材料和数据科学。PI将与Smash Illinois合作，为代表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。与此同时，与北中学院和奎斯特克的夏令营以及与阿德莱·E·史蒂文森高中的高中研究项目也将扩大，以便让年轻一代接触材料科学。国际技术协会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的职业发展。技术总结这个职业奖项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扭曲、动力学和缺陷结构，从而为设计新的先进合金创造机会。严重的晶格畸变是CCA中的一种重要现象，它与CCA中的各种物理化学性质有关。然而，对于CCA中严重晶格扭曲的本质，特别是在SRO耦合的情况下，人们知之甚少。PI将通过机制研究、预测建模和方法开发的独特协同作用，研究CCA中的SRO和相关的晶格扭曲。这项研究将阐明随机共振对CCA中晶格扭曲结构的影响，这将被用来量化扭曲晶格对CCA声子特性的影响。将应用批量CCA的结果和方法来建立将缺陷特征与CCA中的局部环境联系起来的预测性映射，为CCA的计算设计提供基础，以获得优异的力学性能。该项目将由对分层数据驱动的计算框架的并行研究推动，该框架能够有效地预测CCA的结构-性质关系。该项目的教育和宣传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。该项目汇集了国家和地方合作伙伴，创建了一个多学科团队，具有互补的专业知识，以加强科学、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，将开发一款基于云的学习应用程序，以移植PI的研究，并向普通公众介绍材料和数据科学。PI将与Smash Illinois合作，为代表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。与此同时，与北中学院和奎斯特克的夏令营以及与阿德莱·E·史蒂文森高中的高中研究项目也将扩大，以便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计划促进多学科的职业发展。该奖项由材料研究部和NSF高级网络基础设施办公室联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。