Conference Logo ProgramMaster Logo
Conference Tools for 2023 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 2023 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Presentation Title Uncertainty and Domain Quantification in Machine Learning Regression Models for Materials Properties
Author(s) Dane Morgan, Glenn Palmer, Lane Schultz, Yiqi Wang, Ryan Jacobs
On-Site Speaker (Planned) Dane Morgan
Abstract Scope In this talk we discuss our recent work on assessing uncertainties and domain for machine learning regression models that predict materials properties. We demonstrate that a simple calibrated ensemble model approach is quite accurate for predicting the standard deviation of a target value prediction for new data reasonably close to the training data (Palmer et al, npj Computation Materials, 2022). We further demonstrate that a simple distance metric on feature space can be used in conjunction with these error bars to predict when a new data point will be within the domain of a model. These approaches can be applied in a fully automated way to almost any materials property prediction model providing practical guidance on model errors and domain.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Data Facilitation Platform for Materials Science Literature Mining
A Deep Neural Network Formulation for Anisotropic Yield Prediction
A Framework to Solve the Inverse “Process-Structure” Problem of Identifying Process Parameters to Produce a Target Microstructure
A Generative AI Framework for Designing Nanoporous Silicon Nitride Membranes (NPM) with Optimized Mechanical Properties
A Hybrid Gaussian Random Field – Deep Learning Model for Statistically Controllable Synthetic Microstructure Generation
A Physics-based Machine Learning Study of the Hot Cracking Phenomenon in the Processes of Additive Manufacturing
A Quantitative Approach to Explainable AI in DIW AM
Accelerated Discovery of Ultra-high Temperature High Entropy Ceramics by Machine Learning and High Throughput Experiments
Accelerating Microstructurally Small Crack Growth Predictions in Three-dimensional Microstructures Using Deep Learning
Active Learning of Powder Milling Machine for Optimized Silicon Particle Size Control
Adaptive Latent Space Embedding for Real-Time 3D Diffraction Data Analysis
Adaptive Learning from Scarce and Multi-Fidelity Data
Addressing Semantic Challenges towards Data Mining using Natural Language Processing
Adversarial Hierarchical Variational Autoencoder: A Novel Autoencoder Architecture for Microstructure Synthesis and Feature Extraction
An Information Theory Based Approach for Training Machine Learned Potentials
Annular Metallic Nuclear Fuel Informatics at 50-nm Resolution
Anomaly Detection in Composite Manufacturing Using Zero-bias Deep Neural Network
Applications of Machine Learning Techniques for Materials Discovery
Automated Classification of Powder X-ray Diffraction Data Using Deep Learning
Autonomous Path Planning in Additive Processes Using Semi-supervised Machine Learning
Closed-loop Discovery of Materials with Simultaneous Electronic and Mechanical Property Targets
Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces
Comparing Microstructure Representations for Machine Learning Models Predicting Material Properties
Comparison of U-Net and Mask R-CNN Neural Network for Detection of Helium Bubbles and Voids in Nuclear Reactor Materials
Computer Vision Assisted Automated Grain Segmentation and High-Throughput Composition Analysis with Scanning Electron Transmission Microscopy
Data-driven Surrogate Models for Predicting Microstructural Evolution
Data-enhanced Hybrid Machine Learning Model for Solid-state Friction Surfacing Process
Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo
Denoising of Electron Back Scatter Patterns for Improved EBSD Characterization Using Deep Learning
Designing High-Temperature Multicomponent NiTiHfPd SMAs Using Machine Learning
Developing a Physics-informed Machine Learning Model to Predict Melt-pool Shape in Additive Manufacturing
Effective Bulk Properties and Structure-property Relationships in Additively Manufactured Metal with Micron- and Nanometer-scale Structural Complexity
Examining the Effects of Grain Boundary Structure Variability, Solute Atoms, and Interatomic Potential on the non-Arrhenius Migration of Incoherent Twin Grain Boundaries in Nickel
Extraction of Creep Parameters from Indentation Creep Experiment: An Artificial Neural Network-Based Approach
Gaussian Process Ensemble Active Learning for Autonomous Parameterization of Direct Ink Write 3D Printing
Generation of 3D Synthetic Polycrystalline Microstructures using Gaussian Random Fields and Two Point Spatial Correlations
Glass Forming Ability of Silica Glasses with Machine Learning Based Prediction Technique
High-dimensional Formulation-based Bayesian Optimization of Dental Composite Resins
How Do You Optimize Your Parameters? Realistically Complex Hyperparameter Optimization of 23 Parameters of a Black Box Function over a Realistically Low Budget of 100 Iterations
How to Lead R&D Digital Transformation in a Chemical Corporation
Inferring Topological Transitions in Pattern-forming Processes via Self-supervised Learning
Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration
Interrelated Process-Geometry-Microstructure Relationships for Wire-feed Laser Additive Manufacturing
Intrinsic Dimensionality Estimates for Microstructural Data
Large-scale Search of High-strength Aluminum Alloys at High Temperature Using Bayesian Learning for Neural Networks
Laser Powder Bed Fusion Process Design Via Machine Learning Augmented Process Modeling
M-10: Modeling the Phase Transition of 2-D Magnetic Materials under the Effects of External Parameters Uncertainty
M-11: Optimized Print Parameter Prediction by Machine Learning
M-12: Scaling Microstructure-dependent Mechanical Properties to Bulk Material Properties Using 3D Convolutional Neural Networks
M-13: Synthetic Data-assisted Unsupervised Domain Adaptation for Hierarchical Microstructure Reconstruction
M-4: A Deep-learning Enabled Reliability Enhancement System for the Fused Deposition Modelling Process
M-5: Advanced Analytics on 3D X-ray Tomography of Irradiated Silicon Carbide Claddings
M-6: Design of Casting-friendly TiAl Alloy by Artificial Neuron Network
M-7: How Can I Use Machine Learning to Predict all the Process Parameters that will lead to a Specific Material Property in my Advanced Manufacturing Process?
M-9: Loss Curvature-informed Multi-property Prediction for Materials and Chemicals via Graph Neural Networks
Machine Learning-based Multi-objective Optimization for Efficient Identification of Crystal Plasticity Model Parameters
Machine Learning Assisted Candidate Search For Niobium Alloy
Modelling Nucleation in Crystal Phase Transition from Machine Learning Metadynamics
Physics-Based Deep Learning Methods for Enforcing Stress Equilibrium in GAN Generated Stress Fields
Predicting Grain Boundary Properties Using Strain Functional Descriptors and Supervised Machine Learning
Prediction of Glass Transition Temperature by Machine Learning Method with Soft Constraint
Prediction of Slip Localization and Transmission in Polycrystalline HCP Metals via Incorporation of Micromechanical Modeling and Machine Learning
Robust and Efficient Method for Calibration of Thermal Models for Additive Manufacturing
RVE, SERVE and Digital Material Volumes for Design and Engineering
Simulation of Mechanical Properties of TPMS-based Osteoporotic Bone by the Neural Network-Enhanced Finite Element Method
Statistical Generation of Three-Dimensional Dislocation Microstructures with Graph Neural Networks
Synthetic Data Development towards Automated Defect Detection of Irradiated Materials
The interp5DOF Matlab Toolbox: Grain Boundary Energy Models and Uncertainty Quantification
Training Material Models Using Gradient Descent Algorithms
Uncertainty and Domain Quantification in Machine Learning Regression Models for Materials Properties
Using Categorical Structures in Model Analysis & Development
Utilizing and Understanding Deep Learning for 3D Microstructure Synthesis
Weakly-Supervised Segmentation of Microstructure Images with Deep Convolutional Neural Networks
What Does a Computer Vision Model Trained to Classify Material Microstructure Images Actually Understand?
XenonPy: An Open Source Platform for Data-driven Materials Design with Small Data
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement