| Abstract Scope |
Amorphous silicon dioxide (a-SiO₂) is a promising anode material for lithium-ion batteries due to its high theoretical capacity and potential for improved structural stability. However, challenges such as large volume changes during charge and discharge hinder practical use. Addressing these issues requires an understanding of its atomic structure. Experimental characterization of amorphous structures is difficult, while computational models are often affected by artifacts like system size and simulation history. In this project, ab initio molecular dynamics (AIMD) simulations using the Vienna Ab initio Simulation Package (VASP) were performed to generate amorphous SiO₂ using the melt and quench process. A design of experiments (DOE) approach examined how temperature, density, simulation time, and thermostat settings influence the resulting structure. From this, an optimal setup for producing reliable amorphous models was identified. These structures were then used to train machine learning force fields (MLFFs), which dramatically reduce the computational cost compared to AIMD. |