CAREER: Cyberinfrastructure for Printable Multifunctional Microstructural Materials

职业：可打印多功能微结构材料的网络基础设施

• 批准号: 2339764
• 负责人: Azadeh Sheidaei
• 金额: $ 55.37万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-15 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339764&HistoricalAwards=false
• 关键词: CAREER; Cyberinfrastructure; Printable; Multifunctional; Microstructural

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.

近年来，由于先进制造的出现以及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进步，材料发现发生了革命性的变化，然而，增材制造有望精确控制材料的微观结构和性能。 ，实验室到市场的开发一直受到计算限制的阻碍，为了保持美国在全球材料市场的竞争力，必须解决这个问题，这个 NSF 职业项目解决了低效率、费用、过度依赖数据和计算的四个关键挑战。通过部署新型网络基础设施来设计具有理想多功能特性的可打印材料来消除制造不确定性，这种方法改变了当前的材料开发范式，从而实现了新型微观结构几何形状、精确有效的材料表征数值开发方法以及强大的物理特性。 -感知生成模型。除了增材制造的实际进步之外，该项目还通过预测新材料的微观结构状态，应用于从机器人和航空航天到高频通信、传感器、电源、热学等领域，为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，帮助他们为机器学习、高性能计算、材料科学、计算力学和增材制造等交叉领域的问题提供解决方案。研究结果将作为开源软件向更广泛的社区公开，并提供有关设计和使用的全面文档，以帮助所有领域的用户。该项目将在四个方面显着增强集成计算材料工程（ICME）领域。第一个研究重点是开发一个普遍的、跨平台、并行的硅体微结构生成器，提供了各种形态的数据集，从而产生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，与传统的压电特性数值方法相比，这两种方法都旨在提高计算效率。提出了一种通过快速傅里叶变换数值方法求解耦合机电均匀化的新能量公式。对于机械特性，引入了耦合近场动力学物理信息神经求解器。第三个重点设计 TransVNet，这是一种将变分自动编码器与卷积神经层相结合的独特架构，通过视觉转换器增强，用于双向结构-属性映射学习。第四个重点通过制造和测试 3D 表示来验证材料设计网络基础设施。通过在 ANSYS 生态系统中实施材料微结构探索器 (PyMME) 网络基础设施，开发基于项目的学习 (PBL)，该研究被整合到爱荷华州立大学的课程中。 Make To Innovate (M:2:I) 本科课程模块、研究生材料信息学课程、K-12 虚拟材料探索实验室，让学生参与创新项目和产品开发。该项目由 OAC 和制定刺激竞争性研究计划 (EPSCoR)。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。