A Data-driven Laplacian-penalized Non-rigid iterative Closest Point Reverse Deformation Model for Net-shape Investment Castings

A FAIR-framework for Integrating Advanced Manufacturing Multimodal Data Sets

A FAIRification Framework for Synchrotron High Energy X-ray Diffraction Datasets

A Manufacturing Technology Roadmap for AI-enhanced Multimodal Sensing of Materials and Processes for Complete Product Lifecycle Performance

A Materials Data Segmentation Benchmark (MDSB)

A Materials Data Segmentation Garden for Benchmarking Segmentation Models

A Surrogate-assisted Uncertainty Quantification and Sensitivity Analysis of a Ni-base Superalloy Hot Isostatic Pressing Finite Element Mode

A Texture Synthesis Approach for Generating Synthetic Microstructural Images for Training ML Models in a Low-data Regime

A Unified Microstructure Segmentation Approach Through Incorporating Domain Knowledge Into Machine Learning

Accelerated Development of Materials Using High-throughput Strategies and AI/ML

AI-driven Topology Optimization of Photonic Structures With Manufacturing Constraints

AI-simulation Workflow to Accelerate Computational Screening of Metal-organic Framework Structures

AI for Science: Data-centric AI by Utilizing D/HPC and FAIRified Scientific Analysis Workflows

An Insight Into Predictive Modelling of NiTi Shape Memory Alloys

Analyzing the Impact of Design Factors on Solar Module Thermomechanical Durability Using Interpretable Machine Learning

Application of Data-driven Digital Twins in Advanced Manufacturing

Assessing the Performance of Machine Learning Universal Interatomic Potentials on Intermetallic Systems

Assessment of an Intelligent System for Additive Manufacturing Product Evaluation

Autonomous Learning of Atomistic Structural Transitions via Physics-inspired Graph Neural Networks

Bayesian SegNet for Semantic Segmentation With Improved Interpretation of Microstructural Evolution During Irradiation of Materials

Capturing AM Process Defects on Fatigue Fracture Surfaces Through Machine Learning Segmentation

Classification of 2D Diffractograms Into “Spotty” and “Continuous” Patterns Using Deep Neural Networks Trained By ab-Initio Simulations

Closing the Loop in Direct-chill Casting of Aluminium Alloys, a Deep Learning Approach

CRowdsourced Materials Data Engine for Unpublished X-ray Diffraction

Data Driven Modeling for Yield Improvement in Gas Atomization Process

Dataset Generation and Verification for Additive Manufacturing Using Explainable AI

Deep Material Network Trained With Local Field Information: Predictions of Homogenized and Local Field Distribution

Development for an Intelligent System for Controlling the Peripheral Temperature of the Blast Furnace

Development of a Machine Learning Based Tool for Defect Detection in Cold Spray Aluminum

Digital Twin for In-situ Process Monitoring and Control of Aerosol Jet Printing

Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning

Empowering Non-Destructive Powder Evaluation with Accessible AI Tools

Enhancing Machine Learning Classification of Microstructures: A Workflow Study on Joining Image Data and Metadata in CNN

Equivariant Neural Networks for Controlling Dynamic Spatial Light Modulators

Exploring Graph Neural Network Surrogates for Microstructure Analysis

Extreme Value Statistics Analysis of Process Defects in Additive Manufacturing Materials

FAIRification of Data-centric AI: Programmatic JSON-LD Creation and OWL Generation

Federated Learning Approaches: Data-decentralized Analysis on Synchrotron X-ray Diffraction Data

Flexible Robotic Assembly through Human-Interpretable State Machine Synthesis

Fluoroelastomers Genome: Analysis of Fluoroelastomers Growth Behavior Based on Spatio-temporal Scene Graphs

Fracture Toughness and Fatigue Life Prediction of Additively Manufactured Al 2024 Alloy Using Machine Learning Models

Generative Super-resolution for Inexpensive In-situ Layerwise Optical Imaging

Graph-based Machine Learning to Assess Particle Growth Kinetics From Image Sequences

High-throughput In-silico Multi-objective Materials Screening for Accelerated Polymer Design and Discovery

High-throughput Microstructural-based Remnant Life Assessment of High-temperature Steels

High Performance Computing and Artificial Intelligence Enabled Materials Characterization and Experimental Automation

HotSpotNet: A Deep Learning Approach to Predicting Stress Hot Spots in Materials Based on Microstructural Features

Hybrid Denoising Diffusion Models for Statistically Conditioned Generation

Identification of Binder Jet Spreading Anomalies Through Semantic Segmentation

Image Analysis of Fractography: Defect Feature Comparisons

Impact of Different Training Datasets on Machine Learning Based Grain Growth Model and Grain Growth Kinetics

Improved Deep Learning Image Classification of Rare Material Defects in Non-destructive-testing Processes by Utilizing Data Imbalance Methods and Synthetic Data

Improved Methods to Predict the Mixing Enthalpy of Liquid Alloys for CALPHAD Databases With Artificial Neural Networks

In-situ Melt Pool Morphology Estimation From Thermal Imaging via Vision Transformers

Integrating Machine Learning Into Constitutive Material Modeling for the Creep Age Forming Process

Intelligent Data Sampling for Autonomous Parameterization: A Gaussian-Process-Ensemble Approach

Intrinsic Dimensionality Estimates for Microstructural Data

Inverse Design of High-temperature Al-alloys Using Hybrid CALPHAD-based ICME Techniques

Learning a Reliable Compression of In-situ, High-speed Camera Data for Additive Manufacturing

Learning Full-rank Elastic Tensors With Equivariant Neural Networks

Leveraging Segmentation Models for Platinum Particle Identification on BWR Nuclear Reactor Components

Machine Learning-enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data

Machine Learning Approach to Phase Recognition and Prediction of Mechanical Properties

Machine Learning Assisted Discovery of Deposition Conditions for Binary Metallic Alloys

Machine Learning Customized Novel Metals for Energy-efficient 4D Printing

Machine Learning to Identify Composition and Heat Treatment Schedule of Low-alloyed TRIP-aided Steel Sheets With the Strength-ductility Trade-off

Managing Scientific Data in Characterization Investigations With FAIR

MatGPT™ - Accelerated Alloy Development by Combining LLMs, Machine Learning, Simulation & Validation

MICRO2D: Statistically Conditioned Deep Generative Models for Curating Big Microstructure Datasets

Microstructural Diffusional Variational Autencoder for Generation of Microstructure Ensembles

MIPAR Spotlight: Integrating Zero-Shot, Deep Learning, and Conventional Processing for Advanced Micrograph Analysis

Multiaxial Fatigue Life Prediction of Additively Manufactured Ti6Al4V Alloy Using Machine Learning Techniques

Neural Networks as Surrogate Models for Real-time Optimization of Additive Manufacturing

Not as Simple as We thought: A Rigorous Examination of Data Aggregation in Materials Informatics

Optical to Scanning Electron Microscopy Style Transfer of Steel Micrograph Using Machine Learning

Optimizing the Microstructure of Additively Manufactured Al Alloy Using Deep Learning

Overcoming Integration Barriers for Multivariate Big Geospatiotemporal Data

Patch-wise Canonical Correlation Analysis in SEM: Advancing 3D Serial Sectioning Image Registration

Persistent Homology for Microstructure Manifold Construction

Physics-constrained, Inverse Design of High-temperature, High-strength, Printable Al Alloys Using Machine Learning Methods

Physics Inspired Modelling of the Milling Process Using a Combined Deep Learning and Symbolic Regression Approach for an Efficient Production of Battery Materials

Predicting Interfacial Solute Segregation in Nanocrystalline Alloys Using Advanced Atomic Descriptors and Machine Learning Schemes

Predicting Microstructure From Process Conditions Using Multi-modal Machine Learning

PV-VISION: A Deep Learning Based Package for Automated Solar Module Inspection

Pyrometry Mapping of Segmented Porosity in Computed Tomography

Realtime In-process Monitoring of Porosity via Convolutional Neural Networks During Additive Manufacturing and Laser Welding

Reinforcement Learning Approaches to Developing Policies for Incremental Robotic Forging

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

Rethinking Materials Simulations: Blending Direct Numerical Simulations With Neural Operators

Semantic Segmentation of Scanning Electron Microscopy Images for Contact Degradation Analysis in Field-aged Photovoltaic Modules

Simulation of Spray Processes to Train Machine Learning Algorithm for Autonomous Path Generation

Space Launch System Weld Process Optimization Using Informatics and Machine Learning

Spatiotemporal Scene Graph Representations for Terabyte Scale X-ray Computed Tomography Datasets of AlMg

Thermodynamically Consistent Neural Networks for Modeling of Inelastic Material Responses

Training Requirements of a Deep Learning Network With Physics-based Regularization Functions Enforcing Stress Equilibrium

Uncertainty Quantification in Machine-learning Models for Predicting β-phase Volume Fraction From Synchrotron X-ray Diffraction Patterns

Unraveling the Mechanisms of Stability in CoMoFeNiCu High Entropy Alloys via Physically Interpretable Graph Neural Networks

Unveiling Metal Additive Manufacturing Microstructure Through Data-driven Unsupervised Clustering of Crystallographic Texture

Using Large Language Models to Aid Materials Design Workflows

Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics

Utilizing Machine Learning to Generate Representative Euler Angles for Large EBSD Datasets

Virtual Inspection of Advanced Manufacturing via Process-scale Digital Twins

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