College of Science

Center for Simulation and Modeling
(formerly known as Computational Materials Science Center)

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SAMP: Structure-Adaptive Materials Prediction

E. Blaisten-Barojas, D. Carr, J. Schreifels, I. Vaisman

George Mason University


Intelligent data mining tools and materials design strategies using crystallographic and materials property data sources can be enriched by developing a rational cyber-design for determining the nature and types of data equivalencies in the structural chemical information of materials. A visual portal enhancing the use of intelligently organized correlations of structural chemical data for predicting better or new materials will be applied within a Web-based learning system for educational and development purposes. This approach will be tested in several graduate and undergraduate chemical courses.

We developed a novel knowledge-based approach to the zeolite structure classification and prediction. The methodology is based on computational geometry analysis of topology in the training set of known zeolite structures and it incorporates machine learning tools to build a structure classification model. Currently, the preliminary model for 22 mineral classes can be used for zeolite crystal assignment with high accuracy, ranging from 60 to 80% depending on the model type and number of parameters.

Delauney tessellated spherical fragment of the zeolite crystal shown above used for the model construction as a function of edge length: atomic scale (left), nanometer scale(right).