Big data opportunities in computational materials science
Prof. Sinisa Coh, Department of Mechanical Engineering, UCRThe modern electronic structure methods allow for a reasonably accurate computation of quantum mechanical behavior of atoms and electrons in a material with almost any chemical composition, using virtually no input parameters. These methods allow design of new materials even before they are made in the laboratory. The only input parameters for these methods are an integer Z specifying the chemical element (Z = number of protons in the nucleus) and an approximate position R of each atom in the material (if exact R is not known, it can be computed). However, there are about 100,000 known inorganic compounds! Therefore, figuring out which one of them might have interesting and useful properties quickly runs into a big-data bottleneck. In this presentation I will first give a quick introduction to the electronic structure methods and then I will mention few interesting problems which I think could be addressed from the big data perspective.