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，由于先进的机器学习工具和高性能计算的综合计算材料设计的进步和​​综合计算材料设计的快速进步，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特性；但是，实验室到市场的发展受到计算限制的阻碍，必须解决这一问题，以维持美国在全球材料市场中的竞争力。这个NSF职业项目通过开发和部署一种新型的网络基础设施来设计具有可取多功能属性的可打印材料，解决了效率低下，对数据的过度依赖以及制造不确定性的四个关键计算挑战。这种方法改变了当前材料发育的范式，从而允许新的微观结构几何形状，精确，有效的数值方法来进行材料表征以及强大的物理学感知通用模型。除了在添加剂制造方面的实际进步外，该项目还通过预测新材料的微观结构状态为从机器人技术和航空航天到高频通信，传感器，电源，热管理，能源收获，能源收获和医疗不屑一顾的微观结构状态。该项目在多学科环境中培训各个级别和专业人士的学生，他们准备在机器学习，高性能计算，材料科学，计算机械师和增加的制造业的交汇处为问题做出解决方案。该研究结果将作为开源软件公开提供给更广泛的社区，并提供有关设计和用法的全面文档，以帮助所有域中的用户。该项目将大大增强四个关键领域的集成计算材料工程（ICME）领域。第一个研究推力在硅素化微结构发生器中发展了通用的，跨平台的，并行，提供了各种形态的数据集，这些数据集导致了不同的特性和制造。与常规数值方法相比，第二个推力建立了两种用于材料表征的数值方法，旨在提高计算效率。对于压电特性，提出了一种新的能量公式，用于通过快速的傅立叶变换数值方法来求解耦合的机电均质化。对于机械性能，引入了耦合的Peridyanics物理学神经求解器。第三个推力设计TransVnet，这是一种独特的架构，将各种自动编码器与卷积神经层结合在一起，通过视觉变压器增强，用于双向结构 - 质量映射学习。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结构。 The research is integrated into the Iowa State University curriculum by implementing Material Microstructure Explorer (PyMME) Cyber​​infrastructure in the ANSYS Ecosystem, developing a Project-Based Learning (PBL) module for the Make To Innovate (M:2:I) undergraduate program, a graduate material informatives course, a Virtual Material Explorer Lab for K-12 and engaging students in innovative projects and product development.该项目由OAC共同资助和启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为通过评估被认为是宝贵的支持。