Invitation to MS - Recent Trends in Data-Driven and Computational Modeling of Materials Across Scales: From First Principles Calculations to Mesoscale Physics, at USNCCM18, 2025

Dear Colleagues, 

We are organizing a mini-symposium 801 - Recent Trends in Data-Driven and Computational Modeling of Materials Across Scales: From First Principles Calculations to Mesoscale Physics, at USNCCM18, July 20–24, 2025, Chicago. We invite you to submit an abstract at this symposium. The abstract submission deadline is January 15, 2025. Please find the submission portal here:  https://usnccm18.usacm.org/abstract-submission        

This symposium aims to bring together researchers focused on advancing theoretical, computational, and data-driven approaches to uncover the fundamental electronic, atomistic, and mesoscopic behavior of materials. The symposium will specifically explore emerging methodologies — from the realm of first-principles calculations to mesoscale techniques — that push the boundaries of materials modeling, with applications spanning functional materials, energy technologies, and quantum information science.

Key areas of interest include, but are not limited to:

1) The development and application of numerical methods for electronic structure and atomistic calculations (ab initio, empirical, and machine learning-based approaches).

2) Innovations in spatio-temporal coarse-graining, multiscale, and multiphysics computational methods, particularly for materials exhibiting complex microstructure and behaviors like plasticity, electro-magneto-mechanics, and chemo-mechanics.

3) Mathematical frameworks for phase transitions, microstructure evolution, and pattern formation using techniques such as variational methods, cluster Hamiltonian approaches, and phase field models.

4) Mesoscale modeling techniques, including dislocation dynamics, statistical approaches, and classical density functional theory.

5) The integration of data-driven strategies and uncertainty quantification of data-driven models, particularly machine learning, to enhance multiscale simulations and enable predictive capabilities for materials behavior.

6) Specific applications of these methods to the development and characterization of energy, quantum or other functional materials.

Thank you

Amartya Banerjee (University of California, Los Angeles),

Susanta Ghosh (Michigan Technologial University),

Vikram Gavini (University of Michigan)