Research

Presently, the group is involved in various research activities involving computational modeling of crystal dislocations, deformation simulation of nanocrystalline metals and metallic glasses, interfacial diffusion at heteroepitaxial semiconductors, and various aspects of material informatics. Following is a brief description of all the major themes presently being explored by our group.

Atomistic modeling of dislocations

Our group is actively involved in modeling the structure and behaviors of line defects in crystals by means of atomistically informed numerical modeling. Recently, we have explored the mechanism of nucleation of twinning dislocation loops in fcc metals. Using the strain-dependent generalized stacking fault energies, we have highlighted the differences between the nucleation mechanisms of twinning loops and Shockley loops. In addition, we also implement computational models to examine the core structures of dislocations.

Molecular dynamics simulations of metallic glasses

Our interests also involve understanding the structural and mechanical properties of metallic glasses. One of the major outcomes of our efforts has been the discovery of the mechanism of negative strain sensitivity in amorphous metallic alloys. The group is currently investigating the structural features of Cu-Zr-Al ternary glasses using MD simulations. In addition, we study the mechanism of shock-deformation of metallic glasses.

Fundamental aspects of grain boundary segregation

We use hybrid MD/MC computations to simulate the process of grain boundary segregation in alloys. Our studies in this direction include both bi-crystal and polycrystal setups. The effects of segregation on grain boundary mobility and critical stress of dislocation nucleations are of particular interest. We also use methods like Voronoi analysis and radial distribution functions to quantify the structural features of grain boundaries.

Cellular automata simulation of epitaxial growth

The mechanism of epitaxial growth on substrates is of key importance to the technology of fabricating modern semiconductor devices. In our group, we study the evolution of the surface morphology of deposited thin films by employing computations based on cellular automata. Parameters like mean thickness, roughness, and anisotropy are measured as functions of processing variables. The resulting dependencies are subsequently used for data-driven modeling.

Machine learning and soft computing applications

Our group uses machine learning and soft computing methods to perform data-driven modeling of various physical processes pertaining to the science and engineering of materials. Methods like Bayesian optimization are used to model dislocation core structures, design virtual nanocrystalline samples, and develop interatomic potentials for engineering materials. Techniques of symbolic regression and support vector machines are used to predict the hardness of alloys.