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