ProgramMaster Logo
Conference Tools for MS&T21: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Author(s) Shariq Haseen, Peter Kroll
On-Site Speaker (Planned) Shariq Haseen
Abstract Scope Polymer-derived ceramics (PDCs) are materials that exhibit many desirable properties such as enhanced mechanical properties at high-temperatures, oxidation resistance, and creep resistance. We develop a reactive force field (ReaxFF) for large-scale simulation of the polymer-to-ceramic transformation of select Si-based PDCs with density functional theory-like (DFT) accuracy. Using our extensive library of hypothetical crystalline and amorphous structures, we first aim to reproduce DFT energy differences within ReaxFF. We further optimize parameters by matching ReaxFF molecular dynamics energies and forces with those of DFT ab initio molecular dynamics. In order to evaluate and improve our parameters, we generate melt-quench (MQ) ReaxFF models and optimize these models within DFT in a “self-learning” process that is used to augment the training set. We use our final set of ReaxFF parameters to investigate the thermal conversion of polymers to PDCs.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

Questions about ProgramMaster? Contact programming@programmaster.org