Fangzheng Chen, IMSE Doctoral Candidate

The transition from a liquid to a crystal or a glass is a fundamental process that determines many properties of the final product. Although numerous experiments have been conducted to investigate this transition, the atomistic scale of such behavior remains elusive. The properties of a high-temperature liquid could play an important role during solidification. For example, it has been argued that the ordering of the structure factor in a liquid reveals the glass-forming ability.

In this dissertation, computational and experimental methods were used to study the structure, dynamics, nucleation properties, and glass-forming ability of metallic liquids and silica glasses. Several Embedded Atom Method (EAM) potentials were investigated and compared with experimental results. The structure factor of metallic liquids was studied and compared with experimental results. Possible parameters such as the first peak height of the structure factor were tested to reveal the glass-forming ability. The motion of atoms during nucleation was studied, and a unique behavior, namely cooperative motion, was identified. Additionally, classical nucleation theory was carefully examined. A novel technique combining molecular dynamics and reverse Monte Carlo was used to investigate the structural evolution of BaO·2SiO2 glass and the development of a machine learning neural network potential. Inelastic neutron scattering was used to study the link between structural and dynamical properties.

Dissertation Examination Committee:

Prof. Kenneth Kelton, Chair
Prof. Katharine Flores

Prof. Rohan Mishra
Prof. Zohar Nussinov
Prof. Li Yang