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
• Acceleration of multi-scale modelling using machine learning
• Materials Datasets (works describing materials data sets with well-curated meta-data, e.g., microstructure images with processing, composition and properties data)

Submission Instructions

Submissions should not be more than a total of eight (8) pages including references, and must be in PDF format and formatted according to the new standard ACM Conference Proceedings Template.
For LaTeX users: unzip acmart-master.zip and use sample-sigconf.tex as a template.
The review process is single-round and double-blind (submission files have to be anonymized).
We are using Microsoft's Conference Management Toolkit(CMT) for submissions. Submit your paper here.

Accepted papers will be presented during the workshop and archived on the workshop website. The papers will not be a part of the KDD proceedings as per KDD regulations.

Please email any enquiries to mlms-workshop-kdd@googlegroups.com.