Journal
SCIENCE CHINA-MATERIALS
Volume 63, Issue 6, Pages 1024-1035Publisher
SCIENCE PRESS
DOI: 10.1007/s40843-019-1255-4
Keywords
descriptor; machine learning; Bayesian optimization; DFT calculations
Categories
Funding
- National Key Research and Development Program of China [2016YFB0700700]
- National Natural Science Foundation of China [11974257, 11674237, 51602211]
- Natural Science Foundation of Jiangsu Province of China [BK20160299]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality. In the field of photovoltaics, the bandgap (E-g) is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser limit. In this paper, the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability, optical absorption, and carrier lifetime. Multiple criteria make the quantitative description of material candidates difficult and computationally time-consuming. Taking halide perovskites as an example, we combine thermodynamic stability (Delta H-d) and E-g into a unified figure of merit and use Bayesian optimization (BO) to accelerate materials screening. We have found that, in comparison to an exhaustive search via multiple parameters, BO based on the unified figure of merit can screen optimal candidates (E-g,E-PBE between 0.6-1.2 eV, Delta H-d>-29 meV per atom) more efficiently. Therefore, the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost.
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