UCLA

Glass is one of the most important and frequently used materials due to its special properties: its hardness and transparency makes it an ideal material for windows, and its stability makes it a great candidate for immobilizing radioactive nuclear waste, etc. As a result, almost all aspects of glass ranging from its fabrication, properties and application, characterization, to stability and destruction have been hot topics of material science research for a long time. Among all researching methods, molecular dynamics (MD) simulation is a new emerging technique that has been applied more and more to glass research in modern years thanks to its advantages over conventional experimental methods such as high efficiency, high accuracy and low cost. This thesis focuses on using MD simulations to evaluate glass properties from two main aspects: 1) the equivalence of glasses produced from modern methods such as vapor deposition, sol-gel condensation and irradiation and those fabricated from conventional melt-quenching, and 2) the effects of temperature, pH and glass composition on zeolite precipitation during the nuclear waste immobilization glass dissolution process. The thesis is thus divided based on these two topics. In Chapter I, we aim to compare the equivalence of SiO2 glasses obtained from MD-simulated vapor deposition, sol-gel condensation and irradiation processes and the melt-quenched glasses. That is, to evaluate whether these glasses can (available) or cannot (forbidden) be obtained by using the traditional melt-quenching method by changing the cooling rate. We will show that the availability of glasses can be determined and explained by observing the medium-range structural features and the energy landscape of the atoms. In Chapter II, we explore another important field of glass application: the nuclear waste-immobilization glass dissolution. Though vitrification: the process of melting and mixing radioactive nuclear waste and glassy materials, is widely considered the best way of treating nuclear waste due to the extraordinary stability of glasses, observation of continuous glass dissolution (alteration resumption process, or stage III of the nuclear waste-immobilization glasses dissolution process) is reported in many experimental cases which will lead to nuclear waste leakage. Though the exact reason of alteration resumption is still being discussed, it is generally believed that this phenomenon is closely related to the precipitation of secondary phases like zeolites. Based on ab initio MD simulations, we first construct a complete methodology to calculate the thermodynamic properties (enthalpy and entropy of formation, heat capacity, etc.) of any zeolites given its composition and lattice structure. Moreover, with these thermodynamic data of zeolites, we use Gibbs free Energy Minimization (GEM) simulation to build a database of zeolite precipitation under various temperatures, pHs and initial glass compositions. Finally, we manage to train a machine learning (ML) model using the precipitation database that can predict zeolite formation given the aforementioned conditions as inputs.