Data Mining for Crystal Structure Prediction

1. Data Mining for Crystal Structure Prediction Materials Database Correlation Extractor Structure determination (ab-initio or experiment) Professor Gerbrand Ceder, MIT, Prof. Dane Morgan, UW-Madison, DMR-0312537 Crystal structure plays an essential role in determining many properties of a material ranging from equilibrium properties (elasticity/piezoelectricity) to transport (thermal/electrical conductivity). Hence, knowledge of a material's structure is often essential to the materials designer. However, predicting crystal structure from first principles remains a diffucult problem. Our approach uses correlation information embedded in a comprehensive database of binary intermetallic alloys to suggest the most likely set of structures for new materials. Combining this correlation information with additional experiments or ab initio methods is a powerful way of identifying the crystal structure of new materials.

2. Data Mining for Crystal Structure Prediction Broad Impact:Coupling data mining methods to a large database and modern ab initio techniques is a powerful tool to design new materials. Professor Gerbrand Ceder, MIT, Prof. Dane Morgan, UW-Madison, DMR-0312537 We are committed to making our research results web accessible. Our database of over 13,000 electronic structure calculations spanning more than 85 alloy systems is available (http://datamine.mit.edu) and a web-based structure predictor is under development. Easy Interface Structure details Local environments, unit cells Calculation Analysis Convex hull, stable states at 0 Kelvin Calculation Details Structure information and input files