Conference Logo ProgramMaster Logo
Conference Tools for 2021 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract
Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title Bayesian Inference and Uncertainty Quantification of Grain Boundary Properties
Author(s) Sterling G. Baird, Brandon Snow, Alexia Bigelow, David T Fullwood, Eric R Homer, Oliver Johnson
On-Site Speaker (Planned) Sterling G. Baird
Abstract Scope The development of structure-property models for grain boundaries (GBs) has been historically challenging due to a combination of the high cost (both monetary and temporal) of bicrystal synthesis/characterization/property measurement, and the high-dimensionality of the space. Consequently there are few models that exist, and most of those that do are restricted to high-symmetry subspaces (i.e. they do not consider the full 5D GB character space). We demonstrate the use of Bayesian inference techniques to infer a fully 5D structure-property model for GB energy using published databases. The approach naturally handles underdetermined systems (i.e. limited data), indirect measurements, and yields quantified uncertainty for the inferred structure-property model.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Bayesian Optimization Framework for Exploring the Grain Boundary Manifold
A Machine Learning Investigation of Crystallographic Parameters for Abnormal Grain Growth
A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data
A Sensitivity Analysis of Microstructure-Based Model for U-10Mo Hot Rolling and Annealing
Accelerating High Throughput Materials Simulation Studies Using Machine Learning Based Application Programming Interface (API)
Accelerating Phase-field Predictions via Machine Learning Trained Surrogate Models
Accelerating the Discovery of Self-Reporting Redox-active Materials Using Quantum Chemistry Guided Machine Learning
Accuracy, Uncertainty, Inspectability: The Benefits of Compositionally-restricted Attention-based Networks
AI Guided Discovery of Self-assembly Peptide Sequences using Monte Carlo Tree Search and Coarse-grained Simulations
AI Guided High-throughput Exploration of Potential Energy Surfaces
Are We Making Progress on ML Algorithms for Structure-property Relationships? Using MatBench as a Test Bed
Bayesian Inference and Uncertainty Quantification of Grain Boundary Properties
Building a Better Database to Learn From; Application to Interatomic Potentials
Coupling Machine Learning and Global Structure Optimization in GASP 2.0
Data Science Approaches to Develop Predictive Models for Energy-relevant Materials
Decision Trees in Continuous Action Space for High-throughput Exploration of Potential Energy Surfaces
Discovery and Classification of Double Spinel Chemical Space
Exploring Metastability and Mapping Metastable Phase Diagrams Using Machine Learning
Fast Crystal Structure Reconstruction and Prediction Method: Based on X-ray Diffraction Dataset and Neural Network
Finding and Sharing Atomistic Materials Data and Software with the NIST Materials Resource Registry
Harnessing Materials Data and Simulation Capabilities for the Accelerated Discovery of Photocathode Materials
Inverse Design of Energy Storage Materials via Active Learning
Machine Learning Approach of Molecular Dynamics Simulations for Body-Centered Cubic Zirconium
Machine Learning for Predicting Grain Boundary Properties
Machine Learning Guided Discovery of Novel Oxide Perovskites for Scintillator Applications
Machine Learning Prediction of Defect Formation Energies
Microstructure-driven Parameter Calibration for Mesoscale Simulation
Mining Structure-property Linkages in Nonporous Materials Using Interpretative Deep Learning Approach
Model Comparison and Uncertainty Prediction for ML Models of Crystalline Solids Material Properties
Multi-fidelity Machine-learning with Uncertainty Quantification and Bayesian Optimization for Materials Design: Application to Random Alloys
Neural Network Reactive Force Field for C, H, N, O Systems
Parsimonious Neural Networks Learn Classical Mechanics and an Accurate Time Integrator
Predicting Adsorption Energies and Surface Pourbaix Diagram of Metal NPs by GCNN Method
Quantifying RAMPAGE Interatomic Potentials for Metal Alloys
Simultaneous Development and Robust Optimization of a Microstructure Dependent Material Model: Leveraging Sequential Monte-Carlo Methods to Enhance Symbolic Regression Analysis
Solving Stochastic Inverse Problems for Structure-Property Linkages Using Data-Consistent Inversion
Uncertainty Quantification in Computational Thermodynamics - From the Atomistic to the Continuum Scale
Uncertainty Quantification of Microstructures with a New Technique: Shape Moment Invariants
Use of Atomistic Based Informatics to Model Ionic Bombardment to Synthesize Boron Carbides
De Novo Design of Therapeutic Agents Against COVID-19 Using Artificial Intelligence
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement