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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Thermal Conductivity of a Glass Material by First-principles Molecular Dynamics: The Case of GeTe4
Author(s) Evelyne Martin, Carlo Massobrio
On-Site Speaker (Planned) Evelyne Martin
Abstract Scope Nanoscale thermal effects affect heat dissipation in electronic and photonic devices but increase the efficiency of thermoelectric modules. In particular, issues such as thermal cross talks are observed in modern non-volatile memories based on phase-change materials (PCMs). The comprehension of heat transport at short scale and the quantitative determination of the thermal properties (thermal conductivity of crystalline and amorphous PCMs; thermal interface resistances with their environment in the device) are still in their infancy due to the complexity of achieving quantitative characterizations. In the present work, we show that precise understanding of the thermal transport can be gained in a prototypical PCM, glassy GeTe4, by first-principles molecular dynamics. We make use of the approach-to-equilibrium molecular dynamics (AEMD) methodology and create a thermal transient in the simulation box to determine its thermal conductivity and discuss on the nature of the heat transport (propagons vs diffusons). See PCCP 19,9729(2017); JNCS 498,190(2018).
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
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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