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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Author(s) Harrison Chaney, Kathy Lu
On-Site Speaker (Planned) Harrison Chaney
Abstract Scope Polymer derived ceramics are a promising class of materials. To establish relationships between the initial polymer composition and the end composition and structures of the ceramic, this work used LAMMPS in conjunction with the REAXFF module to simulate the polymer to ceramic conversion. Three different base polymer structures were selected based on the initial carbon compositions, consisting of polydimethylsiloxane (PDMS), polydiethylsiloxane (PDES), and commercially available SPR 684 polysiloxane (PSO). From these simulations, bonding information, final compositions, and microstructures were extracted. The final compositions correlate well with the initial compositions, but the relative carbon loss is higher for the higher carbon starting polymers especially that of PDES. The end structures for each have vastly different carbon domain sizes and carbon fractions. This work hopes to shed light on the effect that the starting polymer has on the final product and gain insight into what is happening on the atomic level.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces

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