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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Role of Multi-state Hydrogen during Mayenite Electride Formation by First-principles Calculation
Author(s) Zheng Yu, Bu Wang
On-Site Speaker (Planned) Zheng Yu
Abstract Scope The crystalline sub-nanometer-cage structure of mayenite (12CaO•7Al2O3) is a playground for various anions, including oxygen and its radicals, hydrogen, and anionic electrons (a characteristic of electrides). These electrons are clathrated inside the nanocages like anions but with a low work function (~2.4 eV), bringing mayenite electride broad applications in transparent conductors, catalysis and electron emitters. The real electron donor has attracted much study since mayenite first exhibited persistent conductivity through hydrogenation and post-UV radiation, but still remains elusive. In this work, we systematically study the reaction paths of hydrogen into mayenite by first-principles simulations. Various hydrogen states, at different positions and valences, are investigated. According to thermodynamics calculations, we suggest a new mechanism of mayenite electride formation based on the competition between protons and hydrides. This research uncovers the role of multi-state hydrogen in electron localization and delocalization, which inspires a further understanding into electrides.
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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