We have successfully developed a new computer system for prediction of the electrical conductivity of several realistic complex systems such as catalysts. This simulator is namely based on the combination of tight-binding quantum chemical molecular dynamics (TB-QCMD) with a kinetic Monte Carlo method (KMC). It has been applied to the prediction of the electrical conductivity of metal oxides with models for bulk and surface. Moreover, prediction of the electrical conductivity, using this simulator, was performed for materials such as Zn doped In2O3 which is a p-type transparent conducting material, MgO as catalyst support, a SnO2(1 1 0) surface, a material used in gas sensors, and carbon materials including graphite and alkene chains. Our simulator was also successfully applied to the prediction of the electric breakdown in SiO2 that happens under a high electric field. Finally, combining our electrical conductivity prediction simulator with the Wiedemann-Franz law enabled us to evaluate the thermal conductivity of Ti and Sn materials. The excellent results obtained in all these case studies show that our newly developed simulator is suitable to investigate the electrical conductivity of complex systems such as catalyst materials and surfaces.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Physical and Theoretical Chemistry