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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Machine Learning Applied to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Author(s) Zach Jensen, Elsa Olivetti
On-Site Speaker (Planned) Zach Jensen
Abstract Scope Zeolites are porous, aluminosilicate ceramics with many industrial and “green” applications. Despite their industrial relevance, the relationship between the synthesis and resulting zeolite structure remain poorly understood requiring expensive and time-consuming trial and error. To realize the full potential of zeolites, alternatives to trial and error synthesis need to be developed. We present an alternative, modeling the synthesis pathway with machine learning. We create natural language processing techniques to automatically extract synthesis information and trends from journal articles. We test this method by examining a data set of germanium zeolites. We create a regression model for a zeolite’s framework density as a function of synthesis parameters. This model has a cross-validation error of 0.98 T/1000 3, and the decision boundaries match many of the known synthesis heuristics. We propose that this methodology can be applied to many problems in zeolite synthesis and will enable synthesis of novel zeolite morphologies.


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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