Accelerating Materials Discovery with Data-Driven Atomistic Computational Tools
Dept. of Materials Science and Eng., Northwestern University, Evanston, IL (USA)
Many of the key technological problems associated with alternative energies (e.g., thermoelectrics, advanced batteries, hydrogen storage, etc.) may be traced back to the lack of suitable materials. Both the materials discovery and materials development processes may be greatly aided by the use of computational methods, particular those atomistic methods based on density functional theory (DFT). Here, we present an overview of our recent work utilizing high-throughput computation and data mining approaches to accelerate materials discovery, specifically highlighting several new approaches. We describe our high-throughput DFT database, the Open Quantum Materials Database (OQMD), which contains over 450,000 DFT calculations and is freely available for public use at http://oqmd.org. We show how this type of large database can be used to effectively screen for new materials with desired properties and show examples of this screening approach for batteries, thermoelectrics, structural metals, etc. We also describe a machine learning approach to construct a materials screening model based on an extensive set of thousands of DFT calculations. The resulting model, which has “learned” rules of chemistry from these many examples, can predict the stability of arbitrary compositions without requiring any a priori knowledge of crystal structure, at about six orders of magnitude lower computational expense than the original QM tools. We use this model to scan—in a matter of minutes—roughly 1.6 million candidate compositions for novel ternary compounds (AxByCz), and predict roughly 4,500 new stable materials.