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Use of Networks, Graphs and Topology in Materials Modeling


Atomistic simulations have become a prominent tool in chemistry, physics, and materials science for its capability interpreting experimental measurement, predicting material properties and designing new compounds from atomic-level perspective, for its capability helping visualize the topology of materials and their change during a process or reaction. It has been noted that atomic simulations are providing new data and exciting insights into various phenomenon that cannot be obtained readily in any other way—theory, or experiment. Having a precise image of the structure or the dynamical process at this scale provides a brand-new approach understanding how the properties of a material is determined. The most common simulation approaches are density functional theory (DFT) and molecular dynamic (MD), based on which the research is focusing on discussing mechanisms and properties of certain types of energy storage material by their topology.

This thesis mainly focuses on four project. To explain the enlarged d-spacing of hard carbon comparing to graphite, with classic MD, we simulated the atomic structure of rippled graphene based on the experimental finding that hard carbon is composited by curved graphene platelets. Similarly, from the experiments, a higher battery performance of LiCl battery electrolyte are found at lower temperature with a reason of local environment change at different temperature, which is proven by the atomic topology modeled by classic and AIMD simulations. Despite simply constructing materials, a further utilization of the atomic model is to design new materials by altering their structure. We discussed the possibility of high proton conductivity by analyzing the water hydrogen bonding network in Prussian Blue analogues with graph theory. Another project is built to find flexible organic ligands for Metal-Organic Frameworks (MOFs). It is a time-consuming work to calculate ligands' flexibility by either experimentally synthesizing and characterize or simulate deforming process, which is solved by forecasting the stiffness of molecules from its geometry with Neural Network.

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