Research Target
This is an overview of my research plan, where molecular simulation techniques and AI-based algorithms are highlighted. Based on the progress I have made up to now, I intend to leverage quantum computation and molecular dynamics simulation to characterize polymer entanglement at equilibrium and non-equilibrium conditions. Further more, I plan to make advantage of machine learning algorithms to advance research on relationship of chain entanglements and mechanical properties under different conditions. Generally, I will be committed to applications of polymer materials and macromolecule-based composites in additive manufacturing, energy transportation, biomechanical systems and even new material discovery.
Polymer Model Generation
The workflow of polymer models, I would use established toolkits like CHARMM-GUI and Pysimm to build all-atom models, and deploy Moltemplate to construct coarse-grained models. Specifically for united atomic models, I have developed python scripts to directly generate LAMMPS data file of polyethylene. Some useful codes are saved in my Github. Inspired by the method, I could modify and extend the scripts to multiply and combine various sorts of polymers into a larger and more complicated system. I could also make use of the script to build graphite or composite materials. New scripts are coming soon!
