Accelerating Materials Discovery with Atomistic Computational Tools
Many of the key technological problems associated with alternative energies 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 recent work utilizing high-throughput computation and data mining approaches to accelerate materials discovery. We show how computational crystal structure solution may be addressed via a new hybrid approach, the First-Principles Assisted Structure Solution (FPASS) approach, which combines experimental diffraction data, statistical symmetry information, and first-principles-based evolutionary algorithmic optimization to automatically solve crystal structures.
We also describe a newly-developed data mining approach, clustering-ranking-regression (CRR), which allows us to automatically identify chemical descriptors in large materials datasets.