Positive and Unlabeled Materials Machine Learning
PUMML is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
machine-learning semi-supervised-learning materials informatics physics chemistry
Objectives & Highlights

• Construct a dataset of materials with quantum mechanical simulations using high-performance computing resources. • Develop a semi-supervised ML model to predict a "synthesizability" score for materials. • Identify most interesting materials with high synthesizability for laboratory experiments.

Takeaways & Next Steps

• Positive and Unlabeled learning is a powerful method in materials science where datasets are often small, diverse, and not labeled. • ML can accelerate the slow, traditional approach to developing new materials. • PUMML can be applied to new materials systems and entirely new problems.

Don't forget to add the tag @ncfrey in your comments.

University of Pennsylvania Materials Science PhD candidate. https://about.me/ncfrey
Share this project
Similar projects
The Illustrated Self-Supervised Learning
A visual introduction to self-supervised learning methods in Computer Vision