CAREER: Cyberinfrastructure for Printable Multifunctional Microstructural Materials

近年来，由于先进制造的到来和先进机器学习工具和高性能计算推动的集成计算材料设计的快速发展，材料发现发生了革命性的变化。添加剂制造有望精确控制材料的微观结构和性能；然而，由于计算限制，实验室到市场的发展一直受到阻碍，必须解决这一问题，以保持美国在全球材料市场的竞争力。这个NSF职业项目通过开发和部署用于设计具有理想多功能特性的可打印材料的新型网络基础设施，解决了低效率、费用、过度依赖数据和制造不确定性这四个关键的计算挑战。这种方法改变了当前材料开发的范式，允许新一代的微结构几何、精确和高效的材料表征数值方法，以及强大的物理感知生成模型。除了在添加剂制造方面的实际进展，该项目通过预测从机器人和航空航天到高频通信、传感器、电源、热管理、能量收集和医疗植入物等应用的新材料的微结构状态，对材料科学做出了重大贡献。该项目在多学科环境中培训各级学生和专业人员，使他们做好准备，为机器学习、高性能计算、材料科学、计算力学和加法制造等交叉领域的问题提供解决方案。研究成果将以开源软件的形式向更广泛的社区公开，并提供有关设计和使用的全面文档，以帮助来自所有领域的用户。该项目将在四个关键领域显著增强集成计算材料工程(ICME)领域。第一个研究方向是开发一种通用的、跨平台的、并行的硅体素微结构生成器，提供各种形态的数据集，从而导致不同的特性和可制造性。第二个推力建立了两种材料表征的数值方法，与传统的数值方法相比，这两种方法的目的都是为了提高计算效率。对于压电特性，提出了一种用快速傅立叶变换数值方法求解机电耦合均匀问题的新能量公式。对于力学性质，提出了一种耦合的动力学物理信息神经求解器。第三个推力设计了TransVNet，这是一种独特的结构，结合了变分自动编码器和卷积神经层，并通过视觉转换器增强，用于双向结构-属性映射学习。第四个推力通过制造和测试材料的3D表示来验证材料设计网络基础设施。这项研究通过在Ansys生态系统中实施材料微结构探索者(PyMME)网络基础设施、为制造到创新(M：2：I)本科课程开发基于项目的学习(PBL)模块、研究生材料信息学课程、K-12虚拟材料探索者实验室以及让学生参与创新项目和产品开发，将这项研究整合到爱荷华州立大学的课程中。该项目由OAC和既定的激励竞争性研究计划(EPSCoR)共同资助。该奖项反映了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。

In recent years, material discovery has undergone revolutionary change due to the advent of advanced manufacturing and rapid progress in Integrated Computational Material Design fueled by advanced machine learning tools and high-performance computing. Additive manufacturing promises precise control over materials’ microstructures and properties; however, lab-to-market development has been impeded by computational limitations, and this must be addressed to maintain US competitiveness in the global materials market. This NSF CAREER project solves the four critical computational challenges of inefficiency, expense, overreliance on data, and manufacturing uncertainties by developing and deploying a novel cyberinfrastructure for designing printable materials with desirable multifunctional properties. This approach transforms the current paradigm of material development, allowing for the novel generation of microstructural geometries, precise and efficient numerical methods for material characterization, and a robust physics-aware generative model. Beyond practical advancements in additive manufacturing, this project contributes significantly to materials science by predicting the microstructure status of new materials for applications ranging from robotics and aerospace to high-frequency communications, sensors, power sources, thermal management, energy harvesting, and medical implants. The project trains students at all levels and professionals in a multidisciplinary environment that prepares them to contribute solutions to problems at the intersection of machine learning, high-performance computing, materials science, computational mechanics, and additive manufacturing. The research results will be publicly available as open-source software to the broader community, with comprehensive documentation on the design and usage to help users from all domains.This project will significantly enhance the Integrated Computational Materials Engineering (ICME) field in four key areas. The first research thrust develop a universal, cross-platform, parallelized in silico voxelized microstructure generator, offering a dataset of various morphologies that lead to distinct properties and manufacturability. The second thrust establishes two numerical methods for material characterization both aimed at increased computational efficiencies compared with conventional numerical methods. For piezoelectric property, a new energy formulation for solving coupled electromechanical homogenization through a Fast Fourier Transform numerical method is presented. For mechanical property, a coupled peridynamics physics-informed neural solver is introduced. The third thrust designs TransVNet, a unique architecture combining a variational autoencoder with convolutional neural layers, enhanced by a vision transformer, for bi-directional structure-property mapping learning. The fourth thrust validates the material design cyberinfrastructure by fabricating and testing the 3D representation of the material. The research is integrated into the Iowa State University curriculum by implementing Material Microstructure Explorer (PyMME) Cyberinfrastructure in the ANSYS Ecosystem, developing a Project-Based Learning (PBL) module for the Make To Innovate (M:2:I) undergraduate program, a graduate material informatics course, a Virtual Material Explorer Lab for K-12 and engaging students in innovative projects and product development. This project is jointly funded by OAC and the Established Program to Stimulate Competitive Research (EPSCoR).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.