Computational Materials Science
Within the Computational Materials Science theme, this track focuses on computational and theoretical approaches that deliver predictive insight and accelerate materials discovery and design. Submissions are encouraged that demonstrate methodological rigor, reproducibility, and meaningful validation against experiments or trusted reference data.
As materials challenges become more complex, robust computational workflows can reduce development cycles and guide targeted experiments. We invite contributions spanning electronic-structure calculations, atomistic simulations, statistical and multiscale modeling, and high-throughput discovery. Work emphasizing verification and validation, uncertainty quantification, workflow automation, and FAIR-oriented data practices is particularly welcome, as these elements increasingly define scientific credibility and impact.
Topics include (but are not limited to):
- Density Functional Theory (DFT) and electronic structure calculations
- Molecular modeling and atomistic simulations (MD)
- Monte Carlo methods and statistical modeling
- Thermodynamics, phase diagrams, kinetic modeling
- Multiscale modeling frameworks and workflow automation
- Defects, diffusion, interfaces, surface phenomena
- Phonons, thermal transport, thermomechanical behavior
- Catalysis modeling and reaction mechanism analysis
- High-throughput screening and database-driven discovery
- Reproducibility, verification and validation, uncertainty quantification
- Open workflows, benchmark sets, and computational data management