Organizers:
- Wenjie Xia, Iowa State University
- Sinan Keten, Northwestern University
- Frederick R. Phelan Jr., National Institute of Standards and Technology
- Jack F. Douglas, National Institute of Standards and Technology
- Zhaofan Li, Iowa State University
Description:
The Materials Genome Initiative (MGI) presents a grand challenge: the development of computational techniques and modeling frameworks that seamlessly bridge multiple length and time scales, while effectively organizing and interpreting complex computational data. A well-structured multiscale modeling approach is particularly essential for understanding and predicting the thermodynamic and rheological properties of polymers and soft matter systems. For these materials, atomistic simulations often face limitations in capturing dynamic and mechanical behaviors across the full range of relevant length and time scales. Additionally, computational resource constraints hinder the ability to simulate large-scale atomistic features. To address these issues, researchers have developed a diverse array of multiscale modeling techniques, each tailored to tackle the unique challenges at specific scales. A key aspect of advancing multiscale modeling is the development of a meta-theoretical framework that unifies diverse modeling approaches—spanning density functional theory, Monte Carlo, molecular dynamics, finite element methods, phase field models, and Lattice Boltzmann simulations, along with data-driven and machine learning modeling. Such modeling development is critical for addressing fundamental phenomena like glass formation, crystallization, polymer entanglement, and mechanics of networks, while providing a structured methodology for integrating simulation and experimental observations.
Topics of interest:
- Approaches for predictive coarse-graining and state-point transferability
- Scale-bridging: connecting data and models between scales
- Soft materials property and performance prediction
- Mechanics of polymers and network materials
- Theory and simulation to bridge length and/or timescales
- Tools, data and database representation
- DFT, Ab-initio, ReaxFF modeling approaches
- AI/ML and data-driven modeling for polymer and soft materials