Date of Award


Degree Name

Master of Science


Computational Science


Ramon Ravelo


Accurate mapping of phase transitions boundaries is crucial in accurately modeling the equation of state of materials. The phase transitions can be structural (solid-solid) driven by temperature or pressure or a phase change like melting which defines the solid-liquid melt line. There exist many computational methods for evaluating the phase diagram at a particular point in temperature (T) and pressure (P). Most of these methods involve evaluation of a single (P,T) point at a time. The present work partially automates the search for phase boundaries lines utilizing a machine learning method based on convolutional neural networks and an efficient search algorithm and a shrinking enclosure. This neural network (NN) approach is applied to the prediction of the melt line of metals as a function of pressure. The proposed NN method is implemented using the so-called Z-method, a molecular-dynamics-based computational approach for determining upper bounds in the solid-liquid melt line of a material. In this method, the system is subjected to "jumps" in temperature until melting is achieved. The usefulness of our proposed NN search method is that it can be easily applied to a wide range of inter-atomic potentials and hence help test their accuracy and agreement with experiments. Future machine learning applications can be similarly applied for determining more subtle and complex phase diagrams.




Received from ProQuest

File Size

93 p.

File Format


Rights Holder

Christopher Alexis Ibarra