Development of ab initio trained force fields for classical molecular dynamics using machine learning or physics-based approaches
Multiscale modeling of confined fluids bridging quantum, atomistic, and continuum scales
Statistical mechanical theory development for predictive modeling of thermodynamic and transport properties
Quantum chemistry software (e.g., CP2K, VASP, Psi4)
Molecular dynamics packages (e.g., LAMMPS, OpenMM)
Mesoscale or continuum-scale modeling, including theoretical framework development
Deep learning frameworks (e.g., TensorFlow, PyTorch) applied to neural networks or physics-informed architectures