Conference Logo ProgramMaster Logo
Conference Tools for 2024 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 2024 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Cluster Expansion Approximation Accelerated by a Graph Neural Network Regressor.
Author(s) Guillermo Vazquez Tovar, Daniel Sauceda, Raymundo Arróyave
On-Site Speaker (Planned) Guillermo Vazquez Tovar
Abstract Scope The CE Method is an efficient approximation for the energetics of a solid solution but for some multi-component systems, the cost of generating training data via DFT calculations is too expensive. Therefore, the main drive in CE research is cluster and structure selection. We propose a shortcut where the cost of calculating fitting parameters is decreased exponentially. We first fine-tune a GNN to a subset of DFT calculations at each ionic step. After we train this model, so it returns accurate values for the final structure’s volume and energy for a relaxation run, we sample thousands of structures. We then fit a CE model to the GNN-relaxer obtained energies. As expected, we find that by generating enough training structures, we overcome overfitting and obtain a CE with an RMSE lower than 10 meV/atom. We present the following test cases for this framework: (Hf,Ti,Zr)B2 diboride system and the MnSn(Co,Ni) Heusler system.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Data-driven Active Learning Paradigm to Model Dislocation Mobility From Atomistics
A Data-driven Approach for Predicting the Stress-strain Curves of FCC Polycrystalline Metals
A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs
A Deep Learning Framework for Designing BCC Refractory Multi-principal Element Alloys with Optimized Strength
A Needed Bridge Between the Microscopy and Data Science Communities: Electron Backscatter Diffraction and Machine Learning Case
A Practical Deep Learning Fiber Segmentation Approach in a Manufacturing Setting
Accelerating Defect Predictions in Semiconductors Using Crystal Graphs
Active Learning for Inverse Problems: Bridging Anisotropy to Materials Structure
An Explanatory Model for Microhardness in Friction Stir Processing of 316L Stainless Steel
Automated Analysis of Crystal Structures in X-ray Diffraction Data Using Deep Learning
Automation of Void Identification in Microstructure With Computer Vision
Autonomous Learning of Atomistic Structural Transitions via Physics-inspired Graph Neural Networks
Biases and Limitations in Reported Data of Laser Powder Bed Fusion: Implications for the Learning
Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets
Calibration of RAFM Steel Micro Mechanical Model for Creep Using Bayesian Optimization and Design of Experiments
Cluster Expansion Approximation Accelerated by a Graph Neural Network Regressor.
Data-Driven Modeling of Performance Degradation in Optoelectronic and Electronic Materials in a High Performance Computing Environment
Enhancing Corrosion Resistant Alloy Design Through Natural Language Processing and Deep Learning
Enhancing Materials Discovery in Vast Composition Spaces: Integrating ML Techniques with FUSE
Experimentally Validated High-dimensional Bayesian Optimization of Dental Adhesives via Adaptive Design
Finding “Trigger Sites” of Reactions Among Heterogeneous Materials From X-ray Microscopic Big Data Using Persistent Homology
Generalizable Graph Neural Network Surrogate Models for Microstructure Analysis
Generalizable Graph Neural Network to Describe the Local Atomic Environment in High Entropy Alloys
Generative Model for Closed-loop Multi-property Materials Predictions and Discovery
Genetic Programming Derived Stress-rupture Model for Lifetime Estimation of Alloy 617
Global Uncertainty Reduction Through Efficient Acquisition Function Candidate Selection in Predefined Design Spaces for Predicting NMR Peak Positions
Harnessing Equivariant Neural Networks for High-throughput Screening of Novel Superconductors
High-throughput Micromechanical Simulations Framework and Its Applications to Predict Microstructure-property Relationships Using a Machine Learning Approach
High-throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Graph Neural Networks
How Solid is Your Ground Truth? Interdisciplinary Application of Uncertainty Quantification to Experimental Indentation Testing
Improving Prediction of Microstructures Using Physics-informed Machine Learning
Inferring Defect Distributions in Additive Manufacturing - A Stochastic Inverse Approach to Multiscale Direct Numerical Simulations
Invertible Temper Modeling Using Normalizing Flows
Investigation of In-liquid Ordering Mediated Transformations in Al-Sc via Ab Initio Molecular Dynamics and Unsupervised Learning
J-1: Accelerating Materials Discovery Using Conditional Generative Adversarial Networks
J-4: Development of Machine Learning Interatomic Potentials for Complex Ceramics
J-5: Modeling the Precipitation of Ni4Ti3 in Near-equiatomic NiTi Alloys
J-6: Annotating Materials Science Text: A Semi-Automated Approach for Crafting Outputs with Gemini Pro
Learning Incremental Forging Policies for Robotic Blacksmithing
Leveraging Machine Learning to Increase Computational Efficiency in Electrochemical Systems: An Application to Galvanic Corrosion
Machine Learning-guided Investigation of the Impacts of Grain Geometry on Twin Formation in MgY Alloys
Machine Learning Guided Friction Stir Welding of AA7075-T6 Aluminum Alloy
Machine Learning Guided Selection of High Temperature High Entropy Refractory Ceramics
Machine Learning Towards Predicting Hot Crack Susceptibility
Mapping Anisotropic Yield Surface Models to Surrogate Isotropic Models Using Strongly Typed Interpretable Machine Learning
Microstructural Analysis of Stainless Steel Backscatter Electron Images by Combining EBSD Data and Deep Learning
Modeling the Microstructure Evolution of a 3D Polycrystal Using a Recurrent Neural Network With Physics Informed Loss Functions
Natural Language Processing and Large Language Models for Automated Extraction of Materials Chemistry Data from Literature
Not as Simple as We Thought: A Rigorous Examination of Data Aggregation in Materials Informatics
Optimizing the Thermal Management of Hot Metal Ladle Cars Through Artificial Intelligence
Physics-constrained Bayesian Neural Networks to Predict Grain Evolution
Physics-Informed Convolutional Neural Networks for Modeling Structure-property Relationships of Fiber-reinforced Composite Materials
Physics-informed Machine Learning Model for Plasticity-mediated Void Growth in FCC Single Crystals
Physics-Informed Machine Learning Prediction of Fe-C Solidification
Predicting Fracture Toughness with Microstructure Sensitivity Using an Elasto-viscoplastic Fast Fourier Transform Model
Reduced-Dimension Surrogate Modeling for Microstructure Prediction
Refractory Oxidation Database (RefOxDB): A FAIR Approach to Analyzing Oxidation Kinetics and Enhancing Oxidation Resistance
Research on the Model of Matching Inventory Plates with Order Contracts of Steel Enterprises
Role of Training Dataset on Machine Learning Based Grain Growth Model
Simulating Castable Aluminum Alloy Microstructures With AlloyGAN Deep Learning Model
Size Estimation of Sintered Alumina by Deep Leaning
Stochastic Inverse Microstructure Design
Temperature Prediction of Continuous Casting Slab Based on Improved Extreme Learning Machine
Thermal Conductivity Homogenization of Composites via Deep Material Network
Thermodynamics and Kinetics of Point Defects in Alloys: A Physics-informed Machine Learning Approach
Towards Rapid Validation and Dynamic Standardisation of Advanced Manufactured Parts
Uncertainty Quantification and Propagation in Modeling Hierarchy for Solidification of Metals and Alloys
Uncertainty Quantification for Accelerated Production of ChIMES ML Force-fields
Understanding the Effects of Environment Gas and Sample Properties on Sample Temperature Distribution in an Optical Floating-zone Crystal-Growth Furnace through Modeling of Heat Transfer
Universal Machine Learning System for Material Properties Prediction
Using Deep Learning to Predict Microstructurally Small Crack Growth Behavior in Three-dimensional Microstructures
Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement