ProgramMaster Logo
Conference Tools for Materials Science & Technology 2019
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Embedding Machine Learning in the Physics of Disordered Solids
Author(s) Ekin Cubuk
On-Site Speaker (Planned) Ekin Cubuk
Abstract Scope The study of crystalline solids benefitted from the low dimensional representation of atomistic structure and defects. In contrast, the study of disordered solids has been challenging due to the lack of such a representation. We have constructed a predictive, one-dimensional representation, using supervised machine learning, to study disordered configurations. Our approach can be used to study simulations as well as experiments, with systems ranging from supercool liquids, glasses, granular pillars, colloidal structures to grain boundaries in polycrystals. We embed the ML representation in theoretical models of several phenomena in disordered solids, e.g. fragility, fragile-to-strong transition, out-of-equilibrium dynamics, aging, glassy thin film dynamics, and grain boundary dynamics. This approach leads to a unified perspective on disordered particle arrangements, from atoms to macroscopic grains spanning seven orders of magnitude in particle size. Finally, I will discuss the potential applications of very recent innovations in computer science to the study of materials.


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

Questions about ProgramMaster? Contact