EAGER: Reconstruction and Optimal Design of Multi-scale Material Systems through Deep Networks

这项早期概念探索性研究资助 (EAGER) 资助支持基础研究，以开发可扩展的计算设计工具，以实现高效且有效的材料设计。计算材料设计 (CMD)，例如确定最佳材料微观结构以实现理想的性能，受到越来越多的关注，因为随后可以使用增材制造等先进加工技术来实现复杂的材料设计。从概念上讲，解决 CMD 问题涉及在问题空间中迭代搜索最佳解决方案。由于解决方案搜索的成本对空间大小敏感，缺乏成本效率阻碍了现​​有 CMD 方法在复杂材料系统中的应用，其中材料设计的优劣取决于多个长度尺度上微观结构的众多细节。 CMD 工具的使用将能够发现关键的微观结构模式并减少问题空间的维数。该研究将为具有卓越耐用性和结构健康的高性能结构材料带来高效的微观结构设计和验证。因此，这项研究的结果将惠及美国各行业、经济和社会。材料科学、工程设计、制造和数据科学所需的无缝集成将有助于扩大学生的参与并对工程教育产生积极影响。将开发易于处理的 CMD 的两个核心技术推动因素。其中包括（1）生成统计模型，该模型可以在多个长度尺度上学习重要的局部微观结构模式，并提供微观结构与其低维设计表示之间的双向细节保留转换； (2) 使用基于物理的模拟和主动学习的加工-结构-性能映射的经济高效的建模框架，以促进有效的加工和微观结构级解决方案空间探索和优化设计。通过考虑所学习特征的物理可解释性，将解决关键挑战，以实现可扩展的微结构特征学习。为此，我们将研究新颖的深度网络架构和学习技术。加工结构映射的统计建模将通过基于物理的模拟工具来实现，其中明确考虑相形态和晶体学信息；结构-性质映射的建模是通过一种新颖的晶格-粒子模拟方法实现的。

This EArly-concept Grant for Exploratory Research (EAGER) grant supports fundamental research to develop scalable computational design tools to enable efficient and effective materials design. Computational material design (CMD), such as identifying optimal material microstructures to achieve desirable performance, receives a growing interest as sophisticated material designs can be subsequently realized using advanced processing techniques such as additive manufacturing. Conceptually, solving CMD problems involves iterative search for the best solutions in a problem space. Since the cost of solution searching is sensitive to the size of the space, the lack of cost-efficiency hampers the application of existing CMD approaches to complex material systems, where the goodness of the material design depends on numerous details of the microstructure on multiple length scales. The use of CMD tools will enable the discovery of critical microstructure patterns and the reduction of the dimensionality of the problem space. The research will lead to efficient microstructure design and validation for high performance structural materials with superior durability and structural health. Therefore, results from this research will benefit various U.S. industries, and its economy and society. The required seamless integration of material science, engineering design, manufacturing, and data science will help to broaden student participation and positively impact engineering education.Two core technical enablers for tractable CMD will be developed. These include (1) a generative statistical model that learns important local microstructure patterns at multiple length scales, and provides a two-way detail-preserving conversion between microstructures and their low-dimensional design representations; and (2) a cost-effective modeling framework for processing-structure-property mapping using physics-based simulations and active learning, to facilitate effective processing- and microstructure-level solution space exploration and optimal design. Key challenges will be addressed to enable scalable microstructure feature learning by taking into account the physical interpretability of the learned features. Novel deep network architectures and learning techniques will be investigated to this end. The statistical modeling of the processing-structure mapping will be achieved by a physics-based simulation tool where both phase morphology and crystallographic information are explicitly considered; the modeling of the structure-property mapping is achieved by a novel lattice-particle simulation method.