Zwitterionic conducting materials
Zwitterionic materials possess cationic and anionic groups. This unique structure gives them special chemical and physical features that can be used to design advanced functional materials for biological, chemical, medical and energy applications.
Our group is designing zwitterionic conducting materials using computational approaches
Natural deep eutectic solvents
Natural deep eutectic solvents are composed of organic molecules from nature. They could be used to design separation processes and enhance the catalytic performance of enzymes. Our group is developing molecular principles for designing deep eutectic solvents for chemical processes using computer simulations.
Binders for next-generation baterries
Binders connect active materials in electrodes, ensuring the electrochemical and mechanical stability of batteries. Scientists are developing new active materials that can enhance the energy density of batteries by several orders. However, current binders such as PVDF are not compatible with these new active materials. We are investigating molecular-level mechanisms that control macroscopic performance of binders and designing new binders that enable the utilization of these high-energy-density active materials.
Nanomaterials have been widely used for various medical and biological applications. Many applications rely on their interactions with biomolecules such as proteins, DNAs and sugars. A long-standing question is how nanomaterials and biomolecules interfere with each other and how this interference relate to the chemicophysical properties of nanomaterials, the features of biomolecules and their solvent environment. We are seeking pieces that can fill this knowledge gap using computational approaches.
Allosteric effect of nanoparticles on proteins
Shao Q.; Hall C. K., Allosteric Effects of Gold Nanoparticle on Human Serum Albumin, Nanoscale 2017, 9(1):380-390
selective binding of molecules on nanoparticles
Shao Q.; Hall C. K., Selectivity of Glycine for Facets on Gold Nanoparticles, J. Phys. Chem. B 2017, 122 (13), 3491-3499
Machine learning force fields
A long-standing issue in molecular simulations is to balance the accurate description of molecular forces and the speed of simulations. Quantum mechanics is accurate but too slow, molecular simulations are fast but not so accurate for some complex and important systems. We are solving this issue by using the ability of machine learning algorithms to describe the complex relationship.