Finding new functional materials is always tricky. But searching for very specific properties among a relatively small family of known materials is even more difficult.
But Joshua Young and Prof. Rondinelli collaborating with Dr. Balachandran and Dr. Lookman of Los Alamos National Laboratory report a workaround in their article titled, “Learning from data to design functional materials without inversion symmetry,” appearing in the Feb. 17, 2017, issue of Nature Communications. The team developed a novel workflow combining machine learning and density functional theory calculations to create design guidelines for new materials that exhibit useful electronic properties, such as ferroelectricity and piezoelectricity.
The key outcome is the identification of 242 compositions after screening ∼3,200 that show potential for noncentrosymmetric structures, a 25-fold increase in the projected number of known noncentrosymmetric Ruddlesden-Popper oxides.