Designing novel materials with the desired physical and chemical properties is recognized as an outstanding problem in materials research. This problem is extremely challenging because materials’ properties depend on a large number of variables such as constitutive elements, crystal forms, and geometrical or electronic characteristics. Traditionally, computational design of materials has been accomplished by means of quantum mechanical and simulations approaches. However, rapid growth of material research has led to the accumulation of a huge body of empirical data on crystal structures and properties of materials stored in specialized databases. These developments create new opportunities to explore the materials knowledge base using statistical and data mining approaches and enable further development and enhancement of the nascent field of materials informatics.
We develop and deploy, ultimately in the form of user-friendly software, novel materials informatics approaches and computational tools to empower material scientists with efficient technologies for analyzing, modeling, and imputing properties of materials. These tools will enable:
- mining of specialized databases to identify materials with specific constitutional, geometric, electronic, and other measured or computed characteristics;
- visualizing very large sets of diverse materials to detect clusters of materials with both similar characteristics and properties and determine patterns of materials characteristics directly responsible for their properties; and
- establishing reliable Materials Quantitative Structure-Property Relationship (MQSPR) models for predicting materials’ properties from their computed characteristics.