Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys

合作研究：DMREF：基于人工智能的超强和超弹性金属合金的自动化设计

• 批准号: 2323765
• 负责人: Liang Qi
• 金额: $ 96.61万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323765&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; AI; enabled

The traditional trial-and-error approach for discovering new alloys has become increasingly expensive and time-consuming. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to leverage the power of artificial intelligence to enable the rapid and automated design of metallic alloys capable of withstanding both extreme stress and recoverable elastic deformation before permanent plastic deformation. The potential candidate alloys are complex concentrated alloys that are consisted of multiple high-concentration chemical elements. These alloys contain intricate fluctuations of both chemical elements and atomic positions within metallic crystals. The tremendous degrees of freedom in these fluctuations obstruct the efficient search for alloys with peak strength and peak elastic deformation limit. To overcome this barrier, the research team will employ artificial intelligence, computational modeling, and experimental tools to design, synthesize, and test ultrastrong and ultraelastic metallic alloys. A unique two-stage automated research workflow that transits from a data-driven approach to a physics-based approach will be constructed based on integrations of artificial intelligence techniques and physical models. Such integrations will enhance the understanding of deformation mechanisms in complex materials, enabling their use in structural and functional applications. This research team with diverse backgrounds will provide incorporative opportunities for undergraduate and graduate students to learn both materials science and artificial intelligence. Moreover, this project is committed to promoting diversity, equity, and inclusion in research and education. The research team will actively engage underrepresented minority students in research projects through education and outreach activities. The innovative strategies developed through this research, enabled by artificial intelligence, will have transformative impacts not only on metallic alloy design but also on the development of multifunctional materials and manufacturing processes.The research team is devoted to developing an artificial intelligence-enabled automated research workflow to revolutionize the design and manufacturing processes of ultrastrong and ultraelastic metallic alloys, which have extremely high yield strengths and elastic limits simultaneously. The general strategy is to manipulate and precisely tailor the local lattice distortions and chemical concentration fluctuations for impeding deformation defect motions in complex concentrated alloys. To achieve this goal, the automated research workflow will seamlessly integrate each step of material design aided by physical principles and artificial intelligence. Specifically, iterative design steps will involve atomistic simulations of deformation defects, depositing thin films of refractory metals-based complex concentrated metallic alloys using automated co-sputtering and in-situ characterization feedback, followed by comprehensive mechanical and structural characterizations using advanced nanomechanical measurements, spectroscopic techniques, and cutting-edge electron microscopy. By leveraging low-rank matrix/tensor factorization and autoencoder neural networks, key features of material structures and defect properties will be extracted from simulations, deposition parameters, mechanical behaviors, spectra, and chemical/structural characterization results. These key features facilitate the construction of a two-stage automated research workflow that transitions from a data-driven approach to a physics-based approach for designing and validating alloy candidates. This project aims to advance both the scientific understanding of deformation mechanisms under extreme loading conditions and manufacturing technologies of complex concentrated alloys and other chemically complex materials. The research team provides broad education opportunities for students with diverse backgrounds, including those in materials science, computer science, and mechanical engineering majors. Also, this project promotes collaboration and innovation through the archiving and sharing of codes and data on Materials Commons, a public repository and collaboration platform for materials studies.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.

发现新合金的传统试验方法变得越来越昂贵且耗时。这种设计材料彻底改变和设计我们的未来（DMREF）项目旨在利用人工智能的力量，以使能够在永久性塑性变形之前既可以承受极端压力和可恢复的弹性变形的金属合金的快速和自动化设计。潜在的候选合金是复杂的浓缩合金，由多个高浓度化学元件组成。这些合金包含金属晶体内化学元件和原子位置的复杂波动。这些波动中的巨大自由度阻碍了具有峰值强度和峰值弹性变形极限的合金的有效搜索。为了克服这一障碍，研究团队将采用人工智能，计算建模和实验工具来设计，合成和测试Ultrastrong和超级金属合金。从数据驱动的方法转换为基于物理学的方法的独特的两阶段自动化研究工作流程将基于人工智能技术和物理模型的整合来构建基于物理的方法。这种整合将增强对复杂材料中变形机制的理解，从而使它们在结构和功能应用中使用。这个背景不同的研究团队将为本科生和研究生提供材料科学和人工智能的融合机会。 此外，该项目致力于促进研究和教育中的多样性，公平性和包容性。研究小组将通过教育和外展活动积极地使代表性不足的少数民族学生参与研究项目。通过这项研究制定的创新策略，由人工智能启用，不仅会对金属合金设计产生变革性的影响，还将对多功能材料和制造过程的开发产生变革性的影响。研究团队致力于开发具有人工智能的自动化研究工作流程彻底改变了超局势和超级金属合金的设计和制造工艺，这些金属合金具有极高的屈服强度和弹性限制。一般策略是操纵和精确调整局部晶格畸变和化学浓度波动，以阻止复杂浓缩合金的变形缺陷运动。为了实现这一目标，自动化的研究工作流将无缝整合材料设计的每个步骤，并在物理原理和人工智能的帮助下。具体而言，迭代设计步骤将涉及变形缺陷的原子模拟，将基于难治金属的复杂浓缩金属合金的薄膜使用自动化的共同示例和原位表征反馈，然后使用全面的机械和结构性表征，然后使用先进的纳米机械测量，光谱，光谱，光谱，光谱，光谱。技术和尖端电子显微镜。通过利用低级别矩阵/张量分解和自动编码器神经网络，将从模拟，沉积参数，机械行为，光谱和化学/结构表征的结果中提取材料结构和缺陷特性的关键特征。这些关键功能有助于构建两阶段的自动化研究工作流程，该研究工作流程从数据驱动的方法转变为基于物理的方法，用于设计和验证合金候选物。该项目旨在提高对极端负荷条件下的变形机制的科学理解，以及复杂浓缩合金和其他化学复杂材料的制造技术。研究团队为具有不同背景的学生提供了广泛的教育机会，包括材料科学，计算机科学和机械工程专业的学生。此外，该项目通过归档和共享材料Commons（公共存储库和合作平台的材料研究平台）的代码和数据来促进合作和创新。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子来评估的。优点和更广泛的影响审查标准。