Collaborative Research: A Metamodeling Machine Learning Framework for Multiscale Behavior of Nano-Architectured Crystalline-Amorphous Composites

协作研究：纳米结构晶体非晶复合材料多尺度行为的元建模机器学习框架

• 批准号: 2331482
• 负责人: Lin Li
• 金额: $ 21.47万
• 依托单位: Arizona State University
• 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: 2023-05-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331482&HistoricalAwards=false
• 关键词: Collaborative; Research; Metamodeling; Machine; Learning

Nanostructured composites may achieve the preferred combination of strength, ductility, toughness, and irradiation tolerance through microstructure tailoring and chemistry; thus, they are highly desired structural materials in harsh environments. In accelerating the design of nanostructured composites, the need is for models that can capture microstructure-dominated deformation mechanisms from the atomic scale to the structural scale. This award supports the development of a meta-modeling framework that enables the incorporation of atomic-level knowledge of microstructure-dominated deformation behaviors to predict structural response. This framework will be applied to design amorphous ceramic reinforced nickel alloys used in advanced nuclear reactors and high-temperature environments. The interdisciplinary nature of the research will provide graduate students a diverse training in computational and experimental mechanics, collaborative teamwork experience, as well as research experience at Los Alamos National Laboratory. Research opportunities and mentorship programs will be created at the University of Nebraska–Lincoln and the University of Alabama for undergraduate students, especially for women and underrepresented minorities. Additionally, outreach activities at university museums will attract local K-12 students towards STEM careers.The accelerated design of nanostructured composites with desired properties needs sophisticated models that can capture microstructure-dominated deformation mechanics at multiple scales. In this project, a metamodeling machine learning based framework that enables the incorporation of microstructure-dominated deformation mechanics into a predictive macroscale model will be developed though an integrated characterization, experimental, and computational approach. Nanocrystalline nickel with high crystallization temperature amorphous ceramic SiOC at the grain boundaries will be the model material. Microstructures, deformation mechanisms, and mechanical properties of the Ni-SiOC nanocomposites will be characterized using advanced microscopes and in situ micromechanical testing. The atomic details of amorphous boundaries-mediated deformation in the nanostructures will be revealed through the combination of density functional theory calculations and large-scale molecular dynamics simulations. A dual-phase micromechanics model will be developed by coarse-graining the key deformation mechanisms identified at the atomic level. Surrogate models of the activation functionals for various mechanisms in the micromechanics model will be derived by training machine learning models from atomistic simulation data. At the macroscopic scale, a physics-informed neural network model in which the hybridized machine learning models will be used as surrogates to bridge scales will complete the metamodeling framework.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.

纳米结构复合材料可以通过显微组织裁剪和化学处理实现强度、延展性、韧性和辐照耐受性的理想组合；因此，它们是恶劣环境中非常需要的结构材料。为了加速纳米结构复合材料的设计，需要能够捕捉微观结构主导的变形机制的模型，从原子尺度到结构尺度。该奖项支持元建模框架的开发，该框架能够结合微观结构主导的变形行为的原子级知识来预测结构响应。该框架将应用于设计先进核反应堆和高温环境中使用的非晶陶瓷增强镍合金。该研究的跨学科性质将为研究生提供计算和实验力学方面的多样化培训，协作团队经验，以及在洛斯阿拉莫斯国家实验室的研究经验。内布拉斯加大学林肯分校（University of Nebraska-Lincoln）和阿拉巴马大学（University of Alabama）将为本科生，尤其是女性和未被充分代表的少数族裔，创造研究机会和导师项目。此外，大学博物馆的外展活动将吸引当地K-12学生从事STEM职业。加速设计具有理想性能的纳米结构复合材料需要复杂的模型，可以在多个尺度上捕捉微观结构主导的变形力学。在本项目中，将通过综合表征、实验和计算方法，开发基于元建模机器学习的框架，将微观结构主导的变形力学纳入预测宏观尺度模型。在晶界处具有高结晶温度的非晶陶瓷SiOC的纳米晶镍将成为模型材料。Ni-SiOC纳米复合材料的微观结构、变形机制和力学性能将使用先进的显微镜和原位微力学测试进行表征。通过密度泛函理论计算和大尺度分子动力学模拟相结合，揭示纳米结构中非晶态边界介导变形的原子细节。通过粗粒化在原子水平上确定的关键变形机制，建立了双相微观力学模型。微力学模型中各种机制的激活函数的代理模型将通过从原子模拟数据中训练机器学习模型来导出。在宏观尺度上，一个物理信息的神经网络模型将完成元建模框架，其中混合机器学习模型将被用作桥接尺度的代理。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。

项目成果

A Hall-Petch-Like Relationship Linking Nanoscale Heterogeneity to Yield Stress of Heterogeneous Metallic Glasses

• DOI: 10.1016/j.ijplas.2023.103759
• 发表时间: 2023-09
• 期刊: International Journal of Plasticity
• 影响因子: 9.8
• 作者: Yucong Gu;Jonathan Cappola;Jian Wang;Lin Li