Applying machine learning to materials science

A new study at Penn focuses on the creation of two-dimensional materials.

Machine learning and artificial intelligence are applied to an increasing number of tasks, from recognizing faces in photos, to recommending movies, even to driving cars. The key ingredient that enables machine learning to be so effective is the availability of staggering amounts of labeled data. People have long been labeling data for Google, Facebook, and Netflix by tagging friends in pictures, identifying stop signs in grainy images before logging in, and rating films and TV shows.

Rendering of 2D graphene molecules

But using machine learning in materials science, which attempts to design and make materials for use in future technologies, has proven to be more difficult due to the lack of labeled data in the field. In materials science, data about materials that have successfully been created is considered labeled data, but information about the vast pool of materials postulated but yet to be synthesized is unlabeled. As such, creating new materials can feel a bit like guesswork for scientists, but a recent study at Penn strives to bring more clarity to the synthesis of new materials through an innovative machine learning technique.

Vivek Shenoy, the Eduardo D. Glandt President’s Distinguished Professor with appointments in Materials Science and EngineeringMechanical Engineering and Applied Mechanics, and Bioengineering, oversaw the study, which was led by Nathan Frey, a graduate student in Shenoy’s group and a National Defense Science and Engineering graduate fellow. Shenoy and Frey set out to apply machine learning to materials science, specifically focusing on the creation of two-dimensional materials, or materials with a thickness of just one or a few layers of atoms. 

Read more at the Penn Engineering blog.