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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Machine Learning to Predict the Elastic Properties of Glasses
Author(s) N. M. Anoop Krishnan, Nishank Goyal, Divyarth Prakash Saxena, Sourabh Kumar Singh, Suresh Bishnoi, Ravinder Ravinder, Hariprasad Kodamana
On-Site Speaker (Planned) N. M. Anoop Krishnan
Abstract Scope Glasses are archetypical disordered materials that are used ubiquiitously in daily life applications. Due to their unique disordered structure, predicting the composition–property relationships in glasses are extremely challenging. Herein, using advanced machine learning (ML) methods, we predict the elastic properties of silicate glasses. In particular, we use deep neural networks (NN) and Gaussian process regression (GPR) to model the elastic properties such as Young's modulus, Poisson's ratio, and density by training against a large dataset obtained from previous experiments. We show that the ML models can predict elastic properties with high degree of accuracy and reliability. Development of such models is imperative to accelerate the design of novel functional glasses for practical applications.
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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