We invite original research papers on leveraging machine learning for materials science and engineering problems. Works on combining physics knowledge with artificial intelligence/machine learning algorithms are highly encouraged. Topics of interest include, but are not limited to, the following:

- AI/ML frameworks for process-structure-property correlations modelling
- Incorporating physics domain knowledge into deep neural networks
- Discovery of physically interpretable laws from data
- AI/ML techniques for learning and interpretation from material microstructure images
- Materials Datasets (works describing materials data sets with well-curated meta-data, e.g., microstructure images with processing, composition and properties data)