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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Exploring Molecular Dynamics Descriptors to Improve Machine Learning Predictions of Glass Forming Ability
Author(s) Benjamin Thomas Afflerbach, Lane Schultz, Izabela Szlufarska, Dane Morgan
On-Site Speaker (Planned) Benjamin Thomas Afflerbach
Abstract Scope Molecular dynamics simulations can give extensive information on atomic behavior in both the supercooled liquid and glassy state. In this work we explore the use the descriptors from molecular dynamics simulations for predicting critical cooling rate, Rc, a key quantity for assessing glass forming ability. To assure that the Rc values and descriptors are consistent, both are calculated from the same interatomic potential, where the Rc values are assessed by identifying the onset of crystallization during rapid cooling simulations. We have generated a database of ~100 Rc values from various compositions of 11 binary metallic alloys and have calculated a range of descriptors previously proposed in literature. We explore how these descriptors relate to the Rc values using machine learning regression models that predict Rc as a function of the descriptors.


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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