Machine Learning Bandgaps of Inorganic Mixed Halide Perovskites

Stanley, Jared; Gagliardi, Alessio

doi:10.1109/nano.2018.8626420

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Supervised machine learning (ML) is a powerful and efficient tool for predicting the band gap, − adsorption volume, , formation energy, , and stability. , In the context of band gap predictions, Omprakash et al applied graph neural networks (GNNs) to predict varying perovskite band gaps in a few milliseconds. The GNN model was trained using a database of 24,501 perovskites created based on density functional theory (DFT) calculations.…”

Section: Introductionmentioning

confidence: 99%

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials

Wang

Zhong²,

Dong

et al. 2023

Inorg. Chem.

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Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design due to the issues of computational accuracy and cost of first-principles methods. Although machine learning (ML) techniques have shown great successes in predicting the versatile properties of materials, their practicality is often limited by the data set quality. Here, by using a combination of natural language processing searches and domain knowledge, we built an experimental database of inorganic borates, including their chemical compositions, band gaps, and crystal structures. We performed graph network deep learning to predict the band gaps of borates with accuracy, and the results agreed favorably with experimental measurements from the visible-light to the deep-ultraviolet (DUV) region. For a realistic screening problem, our ML model could correctly identify most of the investigated DUV borates. Furthermore, the extrapolative ability of the model was validated against our newly synthesized borate crystal Ag 3 B 6 O 10 NO 3 , supplemented by the discussion of an ML-based material design for structural analogues. The applications and interpretability of the ML model were also evaluated extensively. Finally, we implemented a web-based application, which could be utilized conveniently in material engineering for the desired band gap. The philosophy behind this study is to use cost-effective data mining techniques to build high-quality ML models, which can provide useful clues for further material design.

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Section: Introductionmentioning

confidence: 99%

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials

Wang

Zhong²,

Dong

et al. 2023

Inorg. Chem.

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“…The generation of theoretical data does not consider experimental factors inherent to the synthesis processes of thin films; therefore, experimental information can be considered the most convenient source of data for the generation of models. Previous studies using machine learning to predict the performance of solar cells have used data from theoretical calculations (Balachandran, Kowalski, et al, 2018;Gladkikh et al, 2020;Takahashi et al, 2018) and from experimental studies (Lu et al, 2018(Lu et al, , 2019Odabaşı et al, 2019;Stanley & Gagliardi, 2019;Wu & Wang, 2019;Yu et al, 2019). However, collecting experimental data is costly unless we take such data from information available in academic literature.…”

Section: Introductionmentioning

confidence: 99%

Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells

Velez Sanchez,

Botero Londoño,

Sepulveda Sepulveda

et al. 2023

JART

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In the last decade, the development of perovskite-based solar cells has emerged as a technological alternative for the photovoltaic generation with a higher efficiency/cost ratio. Many contributions have been made in recent years, as evidenced by many academic publications with worldwide experimental results in this area. Machine learning as a tool can support the development of this technology by predicting new materials, novel solar cell configurations, and evaluating the most relevant experimental parameters, among others. For this, the automatic learning models used in predicting or classifying the information available in the literature or generated experimentally must be improved. One way to improve these models is by including new descriptors that allow improving the prediction. In this work, we evaluated the use of the absorber layer thickness as a descriptor in a linear regression model using a database of 221 literature records containing information on the bandgap, the ?HOMO (perovskite-HTL), and ?LUMO (perovskite-ETL) of different perovskite cells, together with the thickness of the absorber layer. By building two multiple linear regression models, including or not the thickness of the absorber layer, a reduction in the root mean square error RMSE of 4.4% and 2.8% was found in the prediction of the Jsc and PCE, respectively. By applying a linear regression model, an improvement in the prediction of Jsc can be seen due to the inclusion of thickness as a descriptor, which is in line with the relatively high value of the mutual information measure between thickness and Jsc.

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Machine Learning Bandgaps of Inorganic Mixed Halide Perovskites

Cited by 2 publications

References 8 publications

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials

Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells

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