|About this Abstract
||MS&T21: Materials Science & Technology
||Ceramics and Glasses Modeling by Simulations and Machine Learning
||Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
||Shariq Haseen, Peter Kroll
|On-Site Speaker (Planned)
Polymer-derived ceramics (PDCs) are materials that exhibit many desirable properties such as enhanced mechanical properties at high-temperatures, oxidation resistance, and creep resistance. We develop a reactive force field (ReaxFF) for large-scale simulation of the polymer-to-ceramic transformation of select Si-based PDCs with density functional theory-like (DFT) accuracy. Using our extensive library of hypothetical crystalline and amorphous structures, we first aim to reproduce DFT energy differences within ReaxFF. We further optimize parameters by matching ReaxFF molecular dynamics energies and forces with those of DFT ab initio molecular dynamics. In order to evaluate and improve our parameters, we generate melt-quench (MQ) ReaxFF models and optimize these models within DFT in a “self-learning” process that is used to augment the training set. We use our final set of ReaxFF parameters to investigate the thermal conversion of polymers to PDCs.