28. Python for Materials Discovery: Python use in the Materials Project

Audience level:
March 11th 8:15 a.m. – 8:20 a.m.


Better materials -- faster computers, better solar cells, energy storage -- are central to technological progress. The Materials Project aims to dramatically reduce the time for materials to go from lab to market. We show how Python powers all aspects of the Materials Project, from the interface to the database to simulations running on supercomputers.


Materials discovery and development is a key innovation driver for new technologies and markets, and an essential part of the drive to a renewable energy future.

Yet, it takes an average of 18 years to move new materials discoveries from lab to market. This is largely because materials designers operate with very little information and must painstakingly tweak new materials in the lab.

Computational materials science is now powerful enough that it can predict many properties of materials before those materials are ever synthesized in the lab.

By computing properties of all known materials, the Materials Project aims to remove guesswork from materials design in a variety of applications. By scaling materials computations over supercomputing clusters, we have computed some properties of over 80,000 materials and screened 25,000 of these for Li-ion batteries.

We show how Python powers the framework and tools of the Materials Project, from the Django web interface, through the distributed processing engine running on supercomputers, to materials science apps, to the MongoDB database, and back again.

To do your own exploration on the Materials Project, login with OpenID on the project website.