• We are broadly interested in the computational studies of composition-structure-property relationships of advanced nanomaterials for nanoelectronics, optoelectronic, and spintronic applications as well as related energy storage and conversion applications.

  • We use the high-throughput computational and data-mining techniques based on first-principles quantum mechanics calculations coupled with the principles of materials physics, materials chemistry, and materials science to search for the target functional materials with desired materials properties and to reveal underlying materials design principles.

  • Our ultimate objective is to develop advanced materials that can address the pressing materials challenges.
  • Yang
    Elucidate Composition-Structure-Property Relationships of Functional Materials We are interested in studying structural, electronic, optical and magnetic properties of materials from first-principles electronic structure calculations. Starting by understanding/manipulating materials properties through computer simulations, our primary aim is to help design novel functional materials with various applications from production, storage and conversion of energy to electronic information technology. We would like to collaborate with experimentalists and work together to explore unknown phenomena in every aspect of materials research, particularly in the fields of energy materials and spintronics. Yang Yang
    High-throughput (HT) Computational Materials Design HT computational materials design provides us a way to realize a rapid discovery of novel functional materials. With the development of computer hardware and availability of high-performance software for first-principles electronic structure calculations, it is becoming practical to produce a huge quantum materials repository. The key idea behind the HT computational materials design is to find a reliable and accessible descriptor, which can help us identify the candidates of desired materials. For example, in a recent study, Dr. Yang carried out a HT search of topological insulators (TI) based on the materials database, Dr. Yang and colleagues defined the 'SOC-noSOC energy discrepancy', the energy gap difference between spin-orbit coupling (SOC) and noSOC calculations as a function of cell dimensions, which is calculated at time-reversal-invariant momentum (TRIM) points. They found that 'SOC-noSOC energy discrepancy' varies much less than energy gaps calculated by SOC and noSOC calculations. On the basis of this discovery, Dr. Yang and colleagues introduced a variational 'high-throughput TI robustness descriptor'. Having this HT descriptor, the critical lattice inducing topological phase transition can be automatically estimated. They examined the whole database, and rapidly identified 28 novel potential TIs in five different symmetry families. [Nat. Mater. 11(7), 614-619, (2012)]. Yang
    Developing HT Computational and Data-analysis Infrastructures To search for materials with desired properties using HT method, besides automatic electronic structure calculations for hundreds of thousands of compounds, a systemic characterization of basic material properties is necessary. For instance, as one co-developer of the AFLOW consortium, Dr. Yang developed high-throughput programs for electronic structures analysis. These programs can generate the electronic band structures, density of states (DOS), and the projected-DOS plots automatically. They have been successfully applied to visualize the electronic band structures of over 30,000 compounds in Yang Yang