Shaofan Li

My current research interests are:

1. Multiscale crystal defect dynamics (MCDD): We have proposed a novel concept of geometrically-compatible dislocation pattern and apply it to study nanoscale plasticity. We have been developing an atomistic-informed dislocation pattern theory and its computational formulations to model crystal plasticity;

2. Con-current multiscale molecular dynamics: We have been developing a con-current multiscale molecular dynamics to simulate phase transformation of crystalline solids at nanoscale. We extended this theory to amorphous materials, and use it to model and simulate plasticity in amorphous materials;

3. Meshfree particle and Peridynamics modeling and simulation: We have applied meshfree particle methods  and peridynamics method to simulate material and structure failure at multiscale. We have used meshfree methods to simulate large scale ductile failure in steel structures, and we have applied peridynamics method to simulate soil fragmentation, fracture in metals, ceramics, concrete structures, and in ice sheets. We have developed a multiscape micromorphic peridynamics to simulate fracture and fragmentations of goetechnical materials under blast loads, and dynamic fracture;

4. Molecular modeling and simulation of various desalination mechanisms at molecular scales. We have conducted molecular dynamics simulations of waste water cleaning and salt water separation (Desalination);

5. Soft matter mechanics and cell mechanics: We use soft matter modeling and computational physics techniques to study cellular mechanotransduction and cell motility. We have working on a multiscale contact line theory and simulations of durotaxis. We have been developing a novel multiscale contact line theory under finite deformation, which is able to simulate droplet durotaxis and cell motility. We are working computational chiral fluid dynamics, and its applications to cell mechanics;

6. First-principle based modeling and simulation of cementitious material microstructures and their mechanical properties;

7. Artificial Intelligence and Machine-learning based method for inverse solution of failure analysis.