|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||P3-19: Thermo-mechanical Property Prediction of High-temperature Materials Using a Python Based Interface With Quantum Espresso
||Joseph Derrick, Michael Ira Golub, Jing Zhang
|On-Site Speaker (Planned)
The aim of this work is to provide engineers a framework and tool for evaluating thermo-mechanical properties of high-temperature materials through a python-based interface that harnesses Quantum Espresso, an open-source simulation package for materials simulation. Quantum Espresso is a predictive material properties code that is based on density-functional theory, plane waves, and pseudopotentials. Several open-source python packages were used to achieve the framework and perform calculations. As this work is to establish a baseline framework upon which further improvements and modifications will be integrated, only materials with well-established testing from external sources, such as silicon carbide and titanium carbide, were used to validate the results generated.