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