[SY-D2] Systematic evaluation of ionization potentials of divalent cation binary oxides
Finding materials with suitable surface properties is unarguably important for applications including (photo)catalysis and crystal growth. Modeling of surface properties requires identification of surfaces that a given material will preferentially adopt. In a data-driven materials discovery and design approach, reasonable models of surfaces with various orientations and terminations must be obtained with minimum human intervention. Moreover, the surface energy as well as any relevant surface property, such as the ionization potential (IP), needs to be calculated systematically and results have to be stored in a database. Ultimately, reducing the number of costly first-principles calculations is desirable by using regression or some other means to estimate a certain surface property and eliminate sampling of the search space that is highly likely to have an unfavorable surface probability.
In this presentation we use first-principles calculations to systematically investigate the IPs of divalent cation binary oxides. The algorithm to make nonpolar and stoichiometric slab-and-vacuum models by Hinuma et al. [1] is used to construct such models. Identification of the drivers that affect the IP, which is a fundamental surface property, will be discussed. The insight obtained from this study would assist the search of descriptors that determine surface properties.
[1] Y. Hinuma, Y. Kumagai, F. Oba, I. Tanaka. Comp. Mater. Sci. 113, 221 (2016).
In this presentation we use first-principles calculations to systematically investigate the IPs of divalent cation binary oxides. The algorithm to make nonpolar and stoichiometric slab-and-vacuum models by Hinuma et al. [1] is used to construct such models. Identification of the drivers that affect the IP, which is a fundamental surface property, will be discussed. The insight obtained from this study would assist the search of descriptors that determine surface properties.
[1] Y. Hinuma, Y. Kumagai, F. Oba, I. Tanaka. Comp. Mater. Sci. 113, 221 (2016).