The Ab-Initio Prediction of Crystal Structure: Combining Data Mining Ideas with Quantum Mechanics

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TECHNICAL SUMMARY:This award supports computational and theoretical research that aims to develop a rigorous formalism to capture "knowledge" from past experimental and computed data, and use it to rapidly guide accurate quantum mechanical energy or free energy methods towards the most stable structure in binary and ternary metallic alloys and oxides. In a departure from traditional computational approaches, the PI will merge ideas from data-mining to extract knowledge from the large body of existing crystal structure information with the predictive power of quantum mechanical calculations. The PI will pursue a probabilistic approach. The probability of particular structure to appear in a new alloy is expanded in terms of correlations between structures at different compositions and between structures and elements. To capture structure correlations present in nature, the PI will data mine some of the largest databases available for metallic alloys and oxide compounds and construct a maximum entropy representation of it. This will enable predictions for many alloys for which currently little or no characterization is present. The resulting structure prediction tool and all in-house generated data will be made available to the research community as a web-based structure predictor so that these new methods can be most efficiently disseminated.The new developments gained from this research, and the ab-initio database that will be created, will be integrated with the freely available (on the web) electronic course on Computational Materials Science the PI teaches. This contributes to the cyberinfrastructure of the materials research community.NON-TECHNICAL SUMMARY:This award supports computational and theoretical research that aims to develop a rigorous formalism to capture "knowledge" from past experimental and computed data, and use it to rapidly guide accurate computations that aim to predict how atoms will organize themselves in materials. The PI will focus on classes of alloys and oxide materials. Crystal structure plays a fundamental and widely applicable role in materials science. Many relevant physical properties of inorganic materials are directly tied to, and sometimes prohibited by, the underlying symmetry of the way atoms arrange themselves in a crystal. In computational materials science where one tries to predict properties of materials before they are synthesized, the prediction of structure is a key but a missing cornerstone of materials design by computer. This work contributes to efforts to develop computational methods that can predict the way atoms will arrange themselves in a crystal.This work contributes to the cyberinfrastructure of the materials research community. It involves the novel application of data mining to materials computations and the use of the resulting integration to solve complex materials problems. The successful completion of this research project will lead to an approach that can determine the stable arrangement of atoms in a material with a high confidence level, and to the creation of a database available to the public that contains the results of computations for a large number of crystal structures and alloys that can be queried by theorists, computational materials researchers, experimentalists, students, and materials educators.