A machine learning model for screening thermodynamic stable lead-free halide double perovskites

Liang, Gui-Qin; Zhang, Jian

doi:10.1016/j.commatsci.2021.111172

Cited by 22 publications

(8 citation statements)

References 68 publications

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“…Predicting the basic physical information in perovskite can be of great help in the exploration of new materials, the mapping of experimental parameters and the understanding of structurefunction relationships. Many models have been developed to predict the physical properties of perovskite such as bandgap 110,119 , oxide ionic conductivity 120 , thermodynamic stability 121,122 , dielectric breakdown strength 123,124 , lattice parameters 125 , crystal structure 126 . For example, Zhang et al establish the lattice constants model based on cubic perovskites 127,128 .…”

Section: Types Of Perovskite Prediction Tasksmentioning

confidence: 99%

Machine learning in energy chemistry: introduction, challenges and perspectives

2023

Energy Adv.

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Section: Types Of Perovskite Prediction Tasksmentioning

confidence: 99%

Machine learning in energy chemistry: introduction, challenges and perspectives

2023

Energy Adv.

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“…The HOIPs have been widely applied in the fields of solar cells, lightemitting diodes, X-ray or γ-ray detectors, lasers, and photodetectors due to their longer charge diffusion lengths, high absorption coefficients, high defect tolerance, high carrier mobility, and tunable E g [36][37][38]. Because of the adjustable photoelectric performance and good stability, the DPs have demonstrated promising applications in photocatalysis as well as in functional devices including solar cells, light-emitting diodes, scintillators, and photodetectors [39,40].…”

Section: The Structure and Features Of Perovskitementioning

confidence: 99%

“…By using arithmetic or weighted averages of ion radii or bond lengths, the concept of tolerance factors can be extended to DPs with more complex compositions [12]. The generalized octahedral factor has also been introduced as a judgment of perovskite formability [39]. For DPs, the common features are similar to those of the ABX 3 type, which are generally based on atomic parameters.…”

Section: Double Perovskitesmentioning

confidence: 99%

“…The important analysis of the features leads to the inference that the design of the B-and B -site cation combinations has a significant impact on the value of E g for DPs. Liang et al [39] developed ML models based on the energy above the convex hull (E hull ) to screen thermodynamically stable lead-free halide DPs. The dataset was assembled from 469 A 2 B BX 6 -type halide DPs with known labels and E hull values, containing 112 stable compounds with E hull ≤ 0 and 357 unstable compounds with E hull > 0.24 elemental features combined with six algorithms were used to build classification models for stable/unstable perovskites as well as the regression model of E hull .…”

Section: Feature Selection For Double Perovskitesmentioning

confidence: 99%

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Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

Wang

et al. 2023

Materials

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Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.

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“…During their analysis, the authors used data from halide perovskite cells from 181 published papers. Recently, Liang et al [17] conducted a study to predict the thermodynamic phase stability of lead-free halide perovskites. Authors reported that the XGBoost ML model provides excellent prediction results compared to other models for classification and regression.…”

Section: Introductionmentioning

confidence: 99%

Hybrid perovskites thin films morphology identification by adapting multiscale-SinGAN architecture, heat transfer search optimized feature selection and machine learning algorithms

et al. 2023

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The automation in image analysis while dealing with enormous images generated is imperative to deliver defect-free surfaces in the optoelectronic area. Five distinct morphological images of hybrid perovskites are investigated in this study to analyse and predict the surface properties using machine learning algorithms. Here, we propose a new framework called Multi-Scale-SinGAN to generate multiple morphological images from a single-image. Ten different quality parameters are identified and extracted from each image to select the best features. The heat transfer search is adopted to select the optimized features and compare them with the results obtained using the cuckoo search algorithm. A comparison study with four machine learning algorithms has been evaluated and the results confirms that the features selected through heat transfer search algorithm are effective in identifying thin film morphological images with machine learning models. In particular, ANN-HTS outperforms other combinations: Tree-HTS, KNN-HTS and SVM-HTS, in terms of accuracy, precision, recall and F1-score.

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A machine learning model for screening thermodynamic stable lead-free halide double perovskites

Cited by 22 publications

References 68 publications

Machine learning in energy chemistry: introduction, challenges and perspectives

Machine learning in energy chemistry: introduction, challenges and perspectives

Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

Hybrid perovskites thin films morphology identification by adapting multiscale-SinGAN architecture, heat transfer search optimized feature selection and machine learning algorithms

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