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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Tuning Electronic Properties in II-IV-V2 Semiconductors via Sub-lattice Configurational Disorder
Author(s) Jacob Cordell, Jie Pan, Stephan Lany, Garritt J Tucker
On-Site Speaker (Planned) Jacob Cordell
Abstract Scope Multinary semiconductors have attracted interest recently for use in diverse energy conversion technologies. Order-dependent II-IV-V2 materials show promise as a means of achieving specific band gaps with lattice parameters matched to their analogue III-Vs. Structure-synthesis-property relationships for these materials are not well understood and computational techniques are sought to reveal the underlying physics of these materials. We investigated the ZnSnN2 and ZnGeN2 systems using Model Hamiltonian-based Monte Carlo simulations to create structures with different degrees of disorder related to occupation of cation sites. Formation energies and electronic structures were estimated from first principles calculations. A motif-Hamiltonian captures the energetics of the ZnSnN2 system in the Monte Carlo simulation while, for the ZnGeN2 system, a cluster expansion is needed to capture long-range ordering effects. This approach shows the stability of structures with varying degrees of disorder associated with effective temperatures for understanding properties of ZnSnN2 and ZnGeN2 grown under non-equilibrium conditions.


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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