Damage, fracture and healing processes are the chemo-mechanically coupled phenomena, e.g., reaction-diffusion-elasticity, in soft condensed matter. We develop a continuum constitutive theory that accounts for the chemo-mechanical processes in realistic polymeric networks and soft matter (and, maybe, biological, living materials), inspired by the statistical mechanical principles underlying molecular events. We aim to establish a unified framework that integrates deterministic and statistical mechanical approaches for the predictive modeling of large deformation and rupture in synthetic and natural soft materials.

Reference: Lee et at., Phys. Rev. Mat., 2024, Lee et al., Int. J. Solids Structures, 2023

We utilize a suite of statistical inference, machine learning and uncertainty quantification techniques for forecasting of extreme and rare "mechanical" events (e.g. damage, fracture) in materials and microstructures upon complex loading scenarios, e.g., ballistic impact, shock. Currently, we're focused on metallic single- and polycrystals (e.g., group V/VI refractory metals), but are looking ahead to applications in amorphous, soft materials.

Reference: Lee et al., Int. J. Plasticity, 2023, Zhang et al., J. Mech. Phys. Solids, 2024, Lee et al., arXiv, 2025

Bayesian inference accelerates the identification of non-unique model parameters with significant uncertainty from cosmology, nuclear physics  to solid mechanics