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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
Author(s) Jake Graser, Steven Ka'ai Kauwe, Taylor D Sparks
On-Site Speaker (Planned) Jake Graser
Abstract Scope Predicting crystal structure has always been a challenging problem for physical sciences. Recently, computational methods have been built to predict crystal structure with success but have been limited in scope and computational time. We explored the breadth versus accuracy of building a model to predict across any crystal structure using machine learning. We extracted 24,913 unique chemical formulas existing between 290 and 310 K from the Pearson Crystal Database. Of these 24,913 formulas, there exists 10,711 unique crystal structures referred to as entry prototypes. Common entries might have hundreds of chemical compositions, while the vast majority of entry prototypes is represented by fewer than ten unique compositions. To include all data in our predictions, entry prototypes that lacked a minimum number of representatives were relabeled as “Other”. By selecting the minimum numbers to be 150, 100, 70, 40, 20, and 10, we explored how limiting class sizes affected model performance.
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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