3rd Wave AI to Accelerate Materials Discovery

A Graph Based Workflow for Extracting Grain-Scale Toughness from Meso-Scale Experiments.

A Semi-Supervised Approach to Characterizing Multiple Morphological Features in Microstructure Images

A Walk Through Material Microstructures: Using Deep Learning and Geometry to Better Visualize Large Collections of Material Microstructure Images

Accelerating Phase-Field Predictions Via History-Dependent Neural Networks Learning Microstructural Evolution in a Latent Space

Additive Manufacturing of Aluminium: Alloy Design and Machine Learning Assisted Process Optimisation

Addressing Annotated Data Scarcity and Materials Diversity with Advanced Deep Learning Architectures

AI/ML for Intelligent Design and Processing of Metal Castings

AMPIS: Automated Materials Particle Instance Segmentation

An Exploratory Analysis of Low and High Temperature Tempered Steel Micrographs Using Machine Learning

Analysis of High-Speed Impact Behaviour of Al 2024 Alloy Using Machine Learning Techniques

Artificial Materials Intelligence (AMI) of Creep Indicator Model (CIM) in Single Crystal Super Alloys

Assessing the Robustness of an EBSD-Data-Based U-Net Model to Classify Phase Transformation Products in Steels

Automated and Intelligent Analysis of Extended X-Ray Absorption Fine Structure (EXAFS) and X-Ray Photoelectron Spectroscopy (XPS)

Automated Defect Identification in Electroluminescence Images of Solar Modules

Automated Microstructure Property Multi-Classification of Ni-Based Superalloys Using Deep Learning

Automated Probabilistic Finite Element Model Calibration Tool Based on Uncertainty Quantification and Machine Learning

Automating Solid-Liquid Interface Identification in Additive Manufacturing Simulator Experiments

Automating the Discovery of New Halide Perovskites with RAPID and ESCALATE

Autonomous Corrosion Resistant Coatings Development Using a Scanning Droplet Cell

Bayesian Deep Learning Methods for Microstructural Feature Characterization of LiAlO2 Pellets

Challenges and Opportunities in the Application of AI to Materials R&D

Classification of Material Defects in Ni-Base Superalloys Using Deep Learning

Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN

Combining Organic and Inorganic Descriptors for Predictions of Solubility and Volatility Across Vast Chemical Space

Comparing Transfer Learning to Feature Optimization in Microstructure Classification

Computer Vision and Machine Learning Methods to Characterize Recycled Powders for Additive Manufacturing

Convolutional Neural Networks for Image Classification in Metal Selective Laser Melting Additive Manufacturing

Correlation Between Additive Manufacturing Process Parameters and Microstructural Descriptors Via Automatic Feature Engineering

Data-Driven Learning of Constitutive Laws and Material Parameter: from Molecular Dynamics to Continuum Models

Data-Driven Modelling of Graphene Synthesis

Data-Driven Reduced-Order Multiscale Materials Modeling Under Inhomogeneous Porosity Distributions

Data Driven Microstructure Evolution: Adjusting Growth Speed and Anisotropy

Deep Learning Based Clustering Technique to Identify and Generation of RVE Models in Duplex Structure Stainless Steels

Deep Learning Surrogate Models for Multiscale Simulation of Advanced Materials

DeepTemp: Predicting Material Processing Conditions with Artificial Intelligence

Designing Thin Film Microstructures Using Genetic Algorithm Guided Time-Varying Processing Protocols

Developing Autonomous Spray Processes with Deep Reinforcement Learning Guided by Human Demonstration

Development of Recipe Optimization Method for Additive Manufacturing Process Parameter Determination

Discovery of New Periodic Inorganic Crystals Via GANs

Effect of Interlayer Delay Time on the Melt Pool Dimensions in Direct Energy Deposition Process using Machine Learning Techniques

Effects of Complex Die Cast Manufacturing Systems and the Critical Error Threshold on Applications of Machine Learning in Production

Efficient Microstructure Image Segmentation Using Deep Learning with Low-Cost Data Annotations

Efficient, Interpretable Atomistic Graph Neural Network Representation for Angle-Dependent Properties and its Application to Optical Spectroscopy Prediction

Employing Artificial Intelligence to Accelerate Development and Implementation of Materials and Manufacturing Innovations

Enabling Rapid Validation and Dynamic Standardisation of Advanced Manufactured Parts

Experimental Validation of Materials Discovered by Autonomous Intelligent Agents

Experimental Validation of Materials Discovered by Autonomous Intelligent Agents.

Feature Anomaly Detection System (FADS) for Intelligent Manufacturing

Finding Superhard Materials Through Machine Learning

Gaussian Process as a Flexible Machine Learning Toolbox with Uncertainty Quantification for Solving Inverse Problems in Process-Structure-Property Relationship

Gaussian Process Regression Modelling of Superalloy Microstructure

Generating Realistic Material Microstructures Using Conditional GANs for Advanced Manufacturing

High-Throughput Development of MPEAs by Combined Approach of Additive Manufacturing and Machine Learning Methods

Human-Robot Collaboration for the Application of Speckle Patterns Through Learning from Demonstration Techniques

Hybrid Approach Combining Machine Learning and Finite Element Simulation for Process and Material Optimization

Industry 4.0 - Creating the Foundation for Machine Learning in Production Manufacturing

Integrating Data-Driven and Experimental Techniques for the Design and Development of New Corrosion-Resistant Coating Alloys for Lightweight Automotive Steels

Interpretable Machine Learning-Assisted Phase Classification for High Entropy Alloys

Interpretation of Convolutional Neural Networks for Predicting Volume Requirements in Studies of Microstructurally Small Cracks

Latent Variable Rietveld Model for High Throughput Quantitative X-ray Diffraction Analysis

Machine-Learning-Guided Automated Training for Adaptive Printing: Automating Intuition for Unintuitive Toolpaths and Geometries

Machine-Learning Force-Field to Develop and Optimize Multi-Component Alloys

Machine Learning-Based Thermal Analysis for Laser-Based Powder-Bed Fusion Additive Manufacturing

Machine Learning-Guided Materials Discovery Enabled by Automated Experimentation

Machine Learning Based Prediction of Corrosion Behavior in Additively Manufactured Titanium Alloy

Machine Learning Based Prediction of Fatigue Crack Growth Rate in Additively Manufactured Ti6Al4V Alloy

Machine Learning Enabled Directed Energy Deposition of Functionally Graded Materials

Machine Learning Enabled Model to Predict Mechanical Properties of Refractory Alloys

Machine Learning from Large and Sparse Data for Novel Materials Discovery

Machine Learning Guided Design of Aluminium Alloys

Machine Learning Guided Prediction of Thermal Properties of Rare-Earth Disilicates and Monosilicates

Machine Learning Regression Models of Crystalline Materials Properties: Comparing Approaches and Results in Prediction Intervals Determination

MAKSAT : AI for Mining and Manufacturing Sector

Methods of Surface Inspection for Plane Metal with the Use of CV

Microscopy Segmentation Models with Transfer Learning from a Large Microscopy Dataset

Microstructural Classification of Bainite Subclasses in Low-Carbon Multi-Phase Steels Using Supervised Machine Learning

Modeling a Monte Carlo Potts Solidification Model Using a Generative Adversarial Network

Modeling and Simulation of Additively Manufactured Lattice Structures to Support Component Qualification

Multi-scale Structure-Property Relationships in Low Carbon Steels

Nanoindentation Load-Displacement Analysis Using a Genetic Algorithm

Nanoindentation Mapping Defects Filtration for Heterogeneous Materials Using Generative Adversarial Networks (GAN’s)

Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Materials Processing

Neuromorphic Utilization of NVM Devices Using GeTe

Now On-Demand Only - Atomistic Line Graph Neural Network for Improved Materials Property Predictions

Now On-Demand Only - Defect Minimization in Additive Manufacturing Through a Customized High-Throughput Experimental Methodology and Machine Learning Approach

Optimization of the Additive Manufacturing Process for Refractory Metals Using Numerical Simulations and Machine Learning

Optimizing Fractional Composition to Achieve Extraordinary Properties

Orientation Imaging Microscopy Grain Reconstruction Using Deep Learning

ORIGINALLY MON PM: Accelerating the Growth of Metal-Organic Framework Thin Films Guided by Pool-based Active Learning

Physics-Constrained, Inverse Design of High-Temperature, High-Strength, Creep-Resistant Printable Al Alloys Using Machine Learning Methods

Plastic Deformation Reconstruction Based on Acoustic Emission Measurements

Predicting Multicomponent Alloy Properties with Neural Network Surrogate Models

Prediction of Anisotropic Plastic Flow from Indentation Responses Via Neural Networks Combined with Finite Element Analysis

Prediction of Stress-Strain Curve of High-Entropy Alloys and 3D Printed Steels Using Machine Learning

Predictive Modeling of Creep Elongation and Reduction in Area in High Temperature Alloys Using Machine Learning

Predictive Synthesis of Quantum Materials with Offline Reinforcement Learning

Productivity Enhancement of Photolithography by Big Data Learning

Profit-Driven Methodology for Servo Press Motion Selection Under Material Variability

Property Optimization of Multifunctional Materials with Complex Parameter Spaces

Qualitative Assessment of Degradation and Ageing Behaviour of Epoxy-Al Nanocomposites Through Machine Learning Assisted With LIBS.

Research Acceleration via Machine Learning for Characterization of Growing Dendritic Crystals from In Situ X-Ray Videos of Alloy Solidification

Semi-supervised Dynamic Sampling for 3D Electron Backscatter Diffraction

Supervised Machine Learning for Collision Weld Process Optimization

Teaching Printers How to Print: from Closed-Loop Control to Integrating AI and Cloud Computing into Additive Manufacture

Teaching the Machine: Characterizing Speckle Patterns for Virtual Demonstrations

Teaching Tools to be Teammates: Digital Manufacturing Research at AFRL

The nSoft Autonomous Formulation Laboratory: X-Ray and Neutron Scattering for Industrial Formulation Discovery

Uncovering Atomic Structure-Property Relationships Driving Segregation Energy Behavior

Unsupervised Topological Learning Approach for Crystal Nucleation in Pure Metals and Alloys

Use of Computer Vision to Characterize Non-Metallic Inclusions in Steel

Using Uncertainty in Machine Learning to Inform Decision Making on Structural Characterization of Materials

xT SAAM – An Industrial Small Data AI Platform(Design-Expert is Trying to Simplify Classical DoE. Making it a Bit Easier to Use)

Questions about ProgramMaster? Contact programming@programmaster.org