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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Machine Learning-aided Development of Empirical Force-fields for Glassy Materials
Author(s) Han Liu, Mathieu Bauchy
On-Site Speaker (Planned) Mathieu Bauchy
Abstract Scope The development of reliable, yet computationally efficient interatomic forcefields is key to facilitate the modeling of glasses. However, the parametrization of novel forcefields is challenging as the high number of parameters renders traditional optimization methods inefficient or subject to bias. Here, we present a new parameterization method based on machine learning, which combines ab initio molecular dynamics simulations and Bayesian optimization. By taking the examples of silicate and chalcogenide glasses, we show that our method yields new interatomic forcefields that offers an unprecedented agreement with ab initio simulations. This method offers a new route to efficiently parametrize new interatomic forcefields for disordered solids in a non-biased fashion.


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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