A Bayesian Framework for Materials Knowledge Systems

Artificial Intelligence for Material and Process Design

Automated Data Curation for Electron Microscopy Using the Materials Data Facility

Combining Machine Learning and ICME for Alloy Development

Computational Classification, Generation and Time-evolution Prediction of Alloy Microstructures with Deep Learning

Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science

Discovering and Navigating Gaps and Connections in Data for Materials Design

Gaps and Barriers to the Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows

Gaps, Limitations, and Pitfalls of Materials Informatics

Improved Performance of Automatic Characterization of Steel Microstructure by Machine Learning Architecture

L-18 (Invited): Multi-fidelity Surrogate Assisted Framework for Prediction and Control of Meltpool Geometry in Additive Manufacturing Processes

L-19: Data-driven Hard-magnetic Materials Selection for AC Applications by Multiple Attribute Decision Making

L-20: Data Driven Prediction of Crystallographic Attributes of Small Molecules Using Various Molecular Fingerprints

L-21 (Digital): Deep Learning Image Analysis for Lattice Material Qualification

L-22: Effect of Microtextured Regions on the Deformation Behavior of Titanium Alloys Submitted to Monotonic and Cyclic Loadings Investigated using FFT-EVP Simulations

L-25: Multi-class Inclusion Identification via Machine Learning of Multilevel Image Features

L-26: Prediction of Temperature after Cooling in Coils Using Machine Learning and Finite Element Method

L-27: Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-informed Data-driven Modeling with Experimental Validation

Machine Learning-directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-confined Regime

Machine Learning for Materials Science: Open, Online Tools in NanoHUB

Machine Learning to Predict Oxidation Behavior of High-temperature Alloys

Magicmat (MAterials Genome and Integrated Computational MAterials Toolkit) and Its Application for Thermoelectric Materials Design

Polymer Informatics: Current Status & Critical Next Steps

Predicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network

Prediction of Steel Micro-structure by Deep Learning Using Database of Thermo-dynamics and Phase Field Model

Reduction of Uncertainty in a First-principles-based CALPHAD-type Phase Diagram via Sequential Learning of Phase Equilibrium Data

Relating Microstructure Features to Response Using Convolutional Neural Networks

Steel Development and Optimization Using Response Surface Models

The MGI and ICME

Training Data-driven Machine Learning Models Using Physics Simulations: Predicting Local Thermal Histories in Additive Manufactured Components

Uncertainty Quantification and Propagation in ICME Enabled by ESPEI

View on Data Ecosystem of Materials

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