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

纳米结构复合材料可以通过微结构定制和化学实现强度、延展性、韧性和耐辐照性的优选组合;因此，它们是恶劣环境中高度期望的结构材料。在加速纳米结构复合材料的设计中，需要能够从原子尺度到结构尺度捕获微观结构主导的变形机制的模型。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。该框架将被应用于设计用于先进核反应堆和高温环境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，协作团队经验以及洛斯阿拉莫斯国家实验室研究经验的多样化培训。内布拉斯加大学林肯分校和亚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创造研究机会和导师计划。此外，大学博物馆的外展活动将吸引当地K-12学生走向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。在这个项目中，一个基于元建模机器学习的框架，使微观结构为主的变形力学纳入预测宏观尺度模型将通过一个综合的表征，实验和计算方法开发。纳米晶镍与高晶化温度的非晶陶瓷SiOC在晶界将是模型材料。Ni-SiOC纳米复合材料的微观结构，变形机制和力学性能的特点是使用先进的显微镜和原位微机械测试。通过密度泛函理论计算和大规模分子动力学模拟相结合，揭示了纳米结构中无定形边界介导的变形的原子细节。一个双相微观力学模型将通过粗粒化在原子水平上确定的关键变形机制。微观力学模型中各种机制的激活泛函的代理模型将通过从原子模拟数据训练机器学习模型来推导。在宏观尺度上，一个基于物理信息的神经网络模型，其中混合机器学习模型将被用作桥梁尺度的代理，将完成元建模框架。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.