Automatic Prediction of Surface Phase Diagrams Using Ab Initio Grand Canonical Monte Carlo
| Autor: | Andrew M. Rappe, Robert B. Wexler, Tian Qiu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Surface (mathematics)
Materials science Diagram Ab initio 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials General Energy Chemical bond Chemical physics Phase space Surface phase Density functional theory Physical and Theoretical Chemistry 0210 nano-technology Grand canonical monte carlo |
| Zdroj: | The Journal of Physical Chemistry C. 123:2321-2328 |
| ISSN: | 1932-7455 1932-7447 |
| DOI: | 10.1021/acs.jpcc.8b11093 |
| Popis: | The properties of a material are often strongly influenced by its surfaces. Depending on the nature of the chemical bonding in a material, its surface can undergo a variety of stabilizing reconstructions that dramatically alter the chemical reactivity, light absorption, and electronic band offsets. For decades, ab initio thermodynamics has been the method of choice for computationally determining the surface phase diagram of a material under different conditions. The surfaces considered for these studies, however, are often human-selected and too few in number, leading both to insufficient exploration of all possible surfaces and to biases toward portions of the composition–structure phase space that often do not encompass the most stable surfaces. To overcome these limitations and automate the discovery of realistic surfaces, we combine density functional theory and grand canonical Monte Carlo (GCMC) into “ab initio GCMC.” This paper presents the successful application of ab initio GCMC to the study of o... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|