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

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。该项目的重点是复杂浓缩合金（CCAs），这是一类新型合金，它将不同种类的原子以几乎相等的比例混合在一起。研究CCAs的科学驱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，以寻找新的材料特性。目前对合金的稳定性、结构和性能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，接近组合空间中心的cca信息实际上是不存在的。该项目旨在通过(i)通过量子力学计算和统计力学方法的结合，建立对cca原子结构的准确预测理解，以及（ii）整合量子力学计算，填补这些复杂合金系统在合金理论方面的知识空白。经验模型和闭环机器学习方法来预测cca的结构和缺陷特征，以加速cca的结构或功能应用设计。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。技术相关cca的重点将加强美国在基础合金研究方面的领导地位。该项目的教育和推广活动包括五个组成部分，即学习工具创新、扩大参与、青年材料教育、暑期研究机会和研究职业发展。该项目将国家和地方合作伙伴聚集在一起，组建一支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，提高对材料科学的认识。与亚马逊合作，将开发基于云的学习应用程序，以移植PI的研究成果，向公众介绍材料和数据科学。PI将与SMASH Illinois合作，为未被充分代表的学生提供学术和社会项目，以扩大材料教育的参与。与此同时，将扩大与北中央学院和Questek的夏令营，以及与Adlai E. Stevenson高中的高中研究项目，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。该职业奖支持研究和教育活动，以开发第一性原理和数据驱动方法，以了解复杂浓缩合金（cca）中短程有序（SRO）的原子性质，以及这种化学顺序如何影响晶格畸变、动力学和缺陷结构，从而为设计新的先进合金创造机会。严重的晶格畸变是一个重要的现象，与CCAs的各种物理和化学性质有关。然而，CCAs中严重晶格畸变的性质尚不清楚，特别是与SRO的耦合。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和相关晶格畸变。本研究将阐明CCAs中晶格畸变结构的SRO，这将用于量化扭曲晶格对CCAs声子特性的影响。大量cca的结果和方法将应用于建立连接cca中缺陷特征与局部环境的预测映射，为具有优越机械性能的cca的计算设计提供基础。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-属性关系。该项目的教育和推广活动包括五个组成部分，即学习工具创新、扩大参与、青年材料教育、暑期研究机会和研究职业发展。该项目将国家和地方合作伙伴聚集在一起，组建一支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，提高对材料科学的认识。与亚马逊合作，将开发基于云的学习应用程序，以移植PI的研究成果，向公众介绍材料和数据科学。PI将与SMASH Illinois合作，为未被充分代表的学生提供学术和社会项目，以扩大材料教育的参与。与此同时，将扩大与北中央学院和Questek的夏令营，以及与Adlai E. Stevenson高中的高中研究项目，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.