GPU-Accelerated Molecular Dynamics Models for studying Soft Matter systems
University of Michigan
Many properties of materials are determined by micro- to nano-scale features. And materials at the scale of tens to hundreds of atoms evidence radically different physical properties than their bulk counterparts. Already, a new class of nano-engineered materials is being developed whose properties are carefully controlled at the molecular level. In the future, nano-engineered materials will not be constructed top-down, component by component, but, rather, self-assembled from the bottom-up through a careful choice of specifically designed nanoscale components. Even coarse-grained models of nanoparticle self-assembly can be computationally expensive. To observe the critical features, many systems require thousands of particles and tens of millions of time steps, and have large parameter spaces to explore. Developed in my research group, the GPU accelerated HOOMD-Blue, Highly Optimized Object-oriented Many-particle Dynamics -- Blue Edition, performs general-purpose particle dynamics simulations on a single GPU-enabled workstation, but achieves the performance of dozens of processor cores. In my research in soft matter self-assembly, I use GPU-accelerated molecular dynamics to rapidly generate and explore phase diagrams of polymer-tethered nanoparticles. I am investigating new computationally efficient models for capturing the self-assembly behavior of heterogeneous and anisotropic nanoscale particles found in soft matter systems. These particles will be the building blocks of novel materials designed to self-assemble spontaneously.