Research
We use molecular simulation and machine learning methods to study the structures, properties and dynamics of soft matter materials such as polymers, glasses, colloids, and self-assembling nanoparticles.
Self-assembly of polymer-grafted nanoparticles
Mesoparticles consisting of a hard core and a soft corona like polymer-grafted nanoparticles (PGNPs) can assemble into various superlattice structures, in which each mesoparticle assumes the shape of the corresponding Wigner-Seitz (or Voronoi) cell. We use molecular simulation method to calculate the free energy cost to deform spherical PGNPs into Wigner-Seitz polyhedra, which are then relaxed in a certain crystalline superlattice. This method allows us to quantify the free energy differences between model BCC, FCC and A15 systems of PGNPs and to identify the most stable structure.
Machine-learning aided materials discovery
There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. We develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes.
Nanofiltration with regularly porous membranes
Membrane separation is an energy-efficient way to extract some substances from others with a multitude of industrial applications such as water desalination, ion exchange, carbon capture and protein purification. An intrinsic trade-off always exists between the pair of separation performance characteristics – permeability and selectivity. We simulate the transport and separation process of particles through regularly porous membranes with nonequilibrium event-driven molecular dynamics.
