Research
Strain Engineering in 2D Heterostructures
The rapidly expanding palette of semiconducting, insulating, and metallic 2D materials can be assembled into vertically stacked or laterally-integrated heterostructures with desired physical properties. In conventional bulk semiconductors, the quality of the interface is limited by lattice matching between the different materials. This limitation can be overcome in 2D materials where the interaction between the layers is weak so that a wide variety of materials can be vertically stacked
Genome Organization
The nucleus has a diameter of 5-20 micrometer making it the largest organelle in eukaryotic cells and contains most of the genetic material. Regulation of gene expression can be ultimately traced to physical contact between distal regions of DNA in the cell nucleus. If we stretch the DNA , the length of the straight DNA inside a cell can go up to 2 meters making us wonder about the twisting of these genetic material inside such a small space of nucleus. Recent chromosome conformation capture studies have shown that DNA in the nucleus has well defined 3D topologies, which ultimately determines the phenotypic state of the cell. Our group is trying to understand the fundamental principles of genome folding and their role in transcription regulations. We use a combination of molecular dynamics, high throughput data analysis, machine-learning and kinetic modelling to achieve this goal.
Energy Materials
The slow diffusion of the ions in electrodes and the limited capacity of electrodes to store intercalated ions are the main bottlenecks in the advancement of batteries. Two-dimensional layered materials provide distinct two-dimensional ion diffusion pathways enabling facilitated ion intercalation and movement, which may permit high power and high capacity energy storage devices. The focus of our research is the investigation of the above-stated issues utilizing multiscale modelling methods and taking an integrative approach to characterize electrochemical performance with appropriate modelling techniques including DFT for electronic and adsorption properties, molecular dynamics for diffusion and interface properties, and finite-element methods for electro-chemo-mechanical coupling at large length scales.
Functional Materials Design
While the combinatorics approach is an excellent tool to create novel structures and predict their performance and provide fundamental understanding, high-computational cost prevents exploration of a diverse range of structures. To circumvent this issue, and explore large numbers of which can potentially be synthesized directly, we use DFT-informed machine-learning approach to predict the new materials with targeted properties.