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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Reactive MD Simulations of Polysiloxanes: Modeling the Polymer-to-Ceramic Route towards Silicon Oxycarbide Ceramics
Author(s) Ilia Ponomarev, Peter Kroll
On-Site Speaker (Planned) Ilia Ponomarev
Abstract Scope Polymer-derived ceramics such as silicon oxycarbide (SiCO) are synthesized via thermal treatment of polymeric precursors. Selection of starting materials and processing conditions is crucial for obtaining optimized materials. Here we present a new reactive force field (ReaxFF) that facilitates simulations of pyrolysis reactions with high fidelity. The force field has been developed in a learning process, and with excellent agreement to quantum-chemical simulations of small models, we perform simulations ranging several nano-seconds for models extending several nano-meters. We apply the reactive force field to the synthesis of SiCO from polymethylhydridosiloxane (PMHS) cross-linked with divinylbenzene (DVB). Chemical species and total mass-loss observed during pyrolysis parallel experimental data. We obtain an amorphous composite of glass-like SiCO with embedded “free” carbon. The morphology and genesis of the free carbon phase changes characteristically during the pyrolysis. With the new force field, the possibility to study different precursors, cross-linkers, reactive atmospheres, and processing becomes feasible.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Atomistic Modeling of Fundamental Deformation Mechanisms in MAX Phases
Development of Boron Oxide Potentials for Computer Simulations of Multi-component Oxide Glasses
Embedding Machine Learning in the Physics of Disordered Solids
Exploring Molecular Dynamics Descriptors to Improve Machine Learning Predictions of Glass Forming Ability
Force-Enhanced Refinement of the Atomic Structure of Silicate Glasses
Genesis of “Free” Carbon in Silicon Oxycarbide Ceramics
Impact of Carbon Morphology on Mechanical Properties of SiCO Ceramics
Leveraging Machine Learning to Predict Microstructural and Macroscopic Properties of Alumina
Machine Learning-aided Development of Empirical Force-fields for Glassy Materials
Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
Machine Learning Applied to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Machine Learning to Predict the Elastic Properties of Glasses
Peridynamics Modeling of Impact-induced Crack Patterns in Glass
Physics-Based Machine Learning Models for High Throughput Screening of Novel Scintillator Chemistries
Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
Prediction of Compressive Strength and Modulus of Elasticity of Concrete Using Machine Learning Models
Reactive MD Simulations of Polysiloxanes: Modeling the Polymer-to-Ceramic Route towards Silicon Oxycarbide Ceramics
Role of Multi-state Hydrogen during Mayenite Electride Formation by First-principles Calculation
The Stability, Structure and Properties of the Zeta Phase in the Transition Metal Carbides
The Thermophysical Properties of TcO2
Thermal Conductivity of a Glass Material by First-principles Molecular Dynamics: The Case of GeTe4
Tuning Electronic Properties in II-IV-V2 Semiconductors via Sub-lattice Configurational Disorder

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