CAREER: Cyberinfrastructure for Printable Multifunctional Microstructural Materials

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.