Welcome to Xia Research Group in the Department of Aerospace Engineering at Iowa State University! Our research interest lies in understanding the complex behaviors of engineering materials via predictive multiscale modeling for material design, characterization, and prediction of their multifunctional performance in aerospace, structure, energy, and bioengineering applications. 

Our group aims to establish a multiscale materials-by-design framework – by integrating theories (i.e., soft matter physics, mechanics, continuum theories), computational techniques (i.e., molecular dynamics, coarse-grained modeling, and machine learning), and experiments – to facilitate design and development of high-performance structural materials.

Nanoscale science

Scaling up Materials Design with Computation

Our group has developed a suite of innovative scale-bridging computational techniques, greatly enhancing our ability to design materials with hierarchical structures across various systems. From thin films to nanocomposites, 2D materials, and even biological or bio-inspired materials, our methodologies offer unprecedented control and insight into material behavior and performance.

Aligned with the goals of the Materials Genome Initiative (MGI) and our University's Research Grand Challenges, these efforts represent a significant advancement in materials science. By seamlessly integrating computational approaches across multiple scales, we are at the forefront of accelerating materials discovery and design, addressing key challenges and unlocking new possibilities in diverse engineering fields.

Research Highlights

Our group has pioneered an Energy-Renormalization (ER) approach for coarse-graining polymers and soft materials, enabling the accurate prediction of their temperature-dependent behaviors by exploiting polymer physics, glass theories, and mechanics.

Employing multiscale modeling techniques, our research has elucidated how intricate structures and intermolecular interactions dictate the size-dependent behaviors of polymer thin films under nanoconfinement.

Our recent endeavors have integrated cutting-edge AI/ML methodologies into material modeling and design. This includes the development of predictive models for the properties and performance of diverse materials such as complex conjugated polymers, functional coatings, and crosslinked networks. 

By harnessing the power of computation techniques, we are pushing the boundaries of advanced materials and structures, paving the way for transformative innovations.


Research Features

Modeling of Graphene Melts
Thin film
CP modeling

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