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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title The Thermophysical Properties of TcO2
Author(s) Hong Zhong, Jason M Lonergan, John S McCloy, Scott P Beckman
On-Site Speaker (Planned) Hong Zhong
Abstract Scope First-principles data is used within the quasiharmonic approximation to predict the thermophysical properties of TcO2. The Debye-Grűneisen approximation is applied along with empirical corrections to the well-known exchange-correlation error. The results indicate that TcO2 is a relatively stiff material with bulk modulus higher than most of other rutile-type oxides. The Debye temperature of TcO2 falls somewhere in between TiO2 (790 K) and Al2O3 (950 K). The volumetric thermal expansion coefficients of TcO2 at ambient conditions is around 1.48 10-5/K, which is close to those of other rutile-type oxides GeO2, SnO2, and SiO2. The room temperature constant pressure heat capacity of TcO2 is slightly higher than those of CaO, SiO2, and BaO but lower than those of GeO2, MnO2, TiO2, and ReO2. This work represents the first time many of the thermophysical properties of TcO2 have been reported either experimentally or computationally.
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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