Data mining for structure type prediction

Abstract

Determining the stable structure types of an alloy is critical to determining many properties of that material. This can be done through experiment or computation. Both methods can be expensive and time consuming. Computational methods require energy calculations of hundreds of structure types. Computation time would be greatly improved if this large number of possible structure types was reduced. A method is discussed here to predict the stable structure types for an alloy based on compiled data. This would include experimentally observed stable structure types and calculated energies of structure types. In this paper I will describe the state of this technology. This will include an overview of past and current work. Curtarolo et al. showed a factor of three improvement in the number of calculations required to determine a given percentage of the ground state structure types for an alloy system by using correlations among a database of over 6000 calculated energies.I will show correlations among experimentally determined stable structure types appearing in the same alloy system through statistics computed from the Pauling File Inorganic Materials Database Binaries edition. I will compare a method to predict stable structure types based on correlations among pairs of structure types that appear in the same alloy system with a method based simply on the frequency of occurrence of each structure type. I will show a factor of two improvement in the number of calculations required to determine the ground state structure types between these two methods. This paper will examine the potential market value for a software tool used to predict likely stable structure types. A timeline for introduction of this product and an analysis of the market for such a tool will be included. There is no established market for structure type prediction software, but the market will be similar to that of materials database software and energy calculation software.The potential market is small, but the production and maintenance costs are also small. These small costs, combined with the potential of this tool to improve greatly over time, make this a potentially promising investment. These methods are still in development. The key to the value of this tool lies in the accuracy of the prediction methods developed over the next few years.