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

纳米复合材料可以通过微观结构的剪裁和化学作用实现强度、延展性、韧性和抗辐射性能的最佳结合，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过程中，需要能够捕捉从原子尺度到结构尺度的微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。该框架将用于设计用于先进核反应堆和高温环境的非晶态陶瓷增强镍合金。这项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，协作团队合作经验，以及洛斯阿拉莫斯国家实验室的研究经验。内布拉斯加-林肯大学和阿拉巴马大学将为本科生创造研究机会和指导计划，特别是为女性和代表不足的少数族裔。此外，大学博物馆的外展活动将吸引当地的K-12学生投身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复杂的模型，能够在多个尺度上捕捉以微结构为主的变形力学。在这个项目中，一个基于元模型机器学习的框架将通过一个集成的表征、实验和计算方法来开发，该框架能够将微观结构主导的变形力学结合到预测的宏观尺度模型中。纳米晶镍具有较高的晶化温度，晶界非晶态陶瓷SiOc将作为模型材料。利用先进的显微镜和原位微机械测试来表征Ni-SiOC纳米复合材料的微观结构、变形机制和力学性能。通过密度泛函理论计算和大规模分子动力学模拟相结合，将揭示纳米结构中无定形晶界介导的形变的原子细节。通过对原子水平上确定的关键变形机制进行粗粒化，将建立一个双相细观力学模型。微观力学模型中各种机构的激活泛函的代理模型将通过训练来自原子模拟数据的机器学习模型来获得。在宏观层面上，一个物理信息神经网络模型，其中混合的机器学习模型将被用作桥接尺度的代理，将完成元建模框架。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.