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微观结构定制和化学来实现强度、延展性、韧性和耐辐照性的优选组合；因此，它们是恶劣环境下非常理想的结构材料，在加速纳米结构复合材料的设计时，需要能够实现的模型。捕获从原子尺度到结构尺度的微观结构主导的变形机制该奖项支持元建模框架的开发，该框架能够整合微观结构主导的原子级知识。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、协作方面的多样化培训。内布拉斯加大学林肯分校和阿拉巴马大学将为本科生（特别是女性和代表性不足的少数族裔）提供团队合作经验以及在洛斯阿拉莫斯国家实验室的研究经验。大学博物馆的活动将吸引当地 K-12 学生从事 STEM 职业。具有所需性能的纳米结构复合材料的加速设计需要能够在多个尺度上捕获微观结构主导的变形力学的复杂模型。在该项目中，基于元建模机器学习的框架可以实现。通过集成表征、实验和计算方法，将微结构主导的变形力学纳入预测宏观模型中。晶界处的陶瓷 SiOC 将成为模型材料，将使用先进的显微镜和原位微观机械测试来表征 Ni-SiOC 纳米复合材料的微观结构、变形机制和机械性能。将通过结合密度泛函理论计算和大规模分子动力学模拟来揭示双相微观力学模型，该模型将通过粗粒度确定的关键变形机制来开发。微观力学模型中各种机制的激活函数的代理模型将通过在宏观尺度上训练机器学习模型来导出，其中混合机器学习模型将是基于物理的神经网络模型。用作桥梁尺度的替代品将完成元建模框架。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，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

将纳米级异质性与异质金属玻璃的屈服应力联系起来的类 Hall-Petch 关系

• DOI: 10.1016/j.ijplas.2023.103759
• 发表时间: 2023-11
• 期刊: International Journal of Plasticity
• 影响因子: 9.8
• 作者: Gu, Yucong;Cappola, Jonathan;Wang, Jian;Li, Lin
• 通讯作者: Li, Lin

Crystalline–Amorphous Nanostructures: Microstructure, Property and Modelling

结晶-非晶纳米结构：微观结构、特性和建模

• DOI: 10.3390/ma16072874
• 发表时间: 2023-04-04
• 期刊: Materials
• 影响因子: 3.4
• 作者: Wei B;Li L;Shao L;Wang J
• 通讯作者: Wang J