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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Atomistic Modeling of Fundamental Deformation Mechanisms in MAX Phases
Author(s) Gabriel Plummer, Garritt Tucker
On-Site Speaker (Planned) Gabriel Plummer
Abstract Scope MAX phases are a large family of layered, ternary metal carbides and nitrides, which possess a unique combination of metallic and ceramic properties. While MAX phases have been recognized as remarkable materials and are utilized in a wide variety of applications, an understanding of their fundamental deformation mechanisms is still lacking. Atomistic modeling studies would contribute greatly to resolving this outstanding issue, but presently no appropriate interatomic potentials exist. Herein, an analytic bond order potential is developed for Ti3AlC2, via a fitting procedure transferable to other MAX phases. Preliminary results are presented to show the unique deformation behavior characteristic of layered materials. Deformation behavior in other applications of interest such as metal-MAX nanolaminates and radiation environments is also considered. The fundamental insight gained from these atomistic studies will allow for better engineering of MAX phases to fully take advantage of their unique properties.

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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