ProgramMaster Logo
Conference Tools for Materials Science & Technology 2020
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Symposium
Meeting Materials Science & Technology 2020
Symposium Ceramics and Glasses Simulations and Machine Learning
Sponsorship ACerS Glass & Optical Materials Division
Organizer(s) Mathieu Bauchy, University of California, Los Angeles
Peter Kroll, University of Texas at Arlington
N. M. Anoop Krishnan, Indian Institute of Technology Delhi
Scope This symposium will provide a forum to identify current achievements and existing challenges in advanced and accelerated materials discovery and characterization of ceramic and glassy materials enabled by theory, simulation, and machine learning approaches. Computational techniques ranging from atomistic simulations to mesoscopic and continuum modeling, and innovative approaches in experimental data analysis for the study of ceramic materials, crystalline or amorphous, of all compositions will be considered. Contributions involving informatics approaches, machine learning, data-­mining, and design/optimization for materials discovery are also encouraged.

Topics include, but are not limited to:
• Informatics, machine learning, data­ mining approaches,
• First-principles studies of the structure and properties of ceramic materials,
• Molecular dynamics and Monte Carlo studies, and forcefield development,
• Upscaling techniques and mesoscale modeling,
• Continuum modeling of ceramic materials,
• Breakthroughs in computational methods for ceramic materials.

Abstracts Due 05/31/2020
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Ab-initio and Reactive MD Simulations of Polymer Pyrolysis and Formation of Silicon-based Ceramics
Application of Natural Language Processing to Zeolites and Cementitious Materials
Beyond the Average: Fluctuations in Glass-forming Systems
Data, Materials and Disorder
De Novo Discovery of Nanoporous Structures with Tailored Sorption Isotherm by Machine Learning
Defect Formation and Self-diffusion in Alumina: Computational Approaches
Introductory Comments: Ceramics and Glasses Simulations and Machine Learning
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python
The Energy Landscape Governs Brittle-to-Ductile Transitions in Glasses
The Role of Pore Pattern on The Ductility Enhancement of Crystalline Silicon Nitride Nanoporous Membranes
Theoretical Calculation of Formation Energies and Site Preference of Substitutional Divalent Cations in Carbonated Apatite
Verification of Mn Local Structure in Manganese Lithium Borate-based Glass by Computer Simulations and X-ray Absorption Spectroscopy


Questions about ProgramMaster? Contact programming@programmaster.org