Dataset Information

Machine learning bandgaps of double perovskites.

ABSTRACT: The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

SUBMITTER: Pilania G

PROVIDER: S-EPMC4726030 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Publications

Machine learning bandgaps of double perovskites.

Pilania G G Mannodi-Kanakkithodi A A Uberuaga B P BP Ramprasad R R Gubernatis J E JE Lookman T T

Scientific reports 20160119

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accura ...[more]

PMID: 26783247

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Project description:Hybrid organic-inorganic perovskites (HOIPs) have shown the encouraging development in solar cells that have achieved excellent device performance. One of the most important issues has been focused on finding Pb-free candidates with suitable bandgaps, which could accelerate the commercialization of environmentally friendly HOIP-based cells. Herein, we propose a new inverse design method, proactive searching progress (PSP), to efficiently discover potential HOIPs from universal chemical space by combining machine learning (ML) techniques. Compared to the pioneering work on this topic, we carried out our ML study based on 1201 collected HOIP samples with experimental bandgaps rather than theoretical properties. On the basis of 25 selected features, a weighted voting regressor ML model was constructed to predict bandgaps of HOIPs. The model comprehensively embedded four submodels and performed the coefficient determinations of 0.95 for leaving-one-out cross-validation and 0.91 for testing set. The feature analysis revealed that the tolerance factor (t f) below 0.971 and the new tolerance factor (τf) in 3.75-4.09 contributed to lower bandgaps and vice versa. By applying the PSP method, the Pb-free HOIPs with optimal bandgaps were successfully designed from a generated chemical space comprising over 8.20 × 1018 combinations, which included 733848 candidates (e.g., Cs0.334FA0.266MA0.400Sn0.769Ge0.003Pd0.228Br0.164I2.836) with an optimal bandgap of 1.34 eV for single junction solar cells, 1511073 large-bandgap candidates (e.g., Cs0.392FA0.016MA0.592Cr0.383Sr0.347Sn0.270Br1.171I1.829) for top parts in tandem solar cells (TSCs), and 20242 low-bandgap ones (e.g., MA0.815FA0.185Sn0.927Ge0.073I3) for bottom cells in TSCs. Finally, three new HOIPs were synthesized with an average bandgap error 0.07 eV between predictions and experiments. We are convinced that the proposed PSP method and ML progress could facilitate the discovery of new promising HOIPs for photovoltaic devices with the desired properties.

Dataset Information

Machine learning bandgaps of double perovskites.

Publications

Machine learning bandgaps of double perovskites.

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