Effectively improving the accuracy of PBE functional in calculating the solid band gap via machine learning

Wan, Zhongyu; Wang, Quan‐De; Liu, Dongchang; Liang, Jinhu

doi:10.1016/j.commatsci.2021.110699

Cited by 30 publications

(12 citation statements)

References 31 publications

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“…The correlation strength of the predictions based on the PBE band gaps ( R 2 = 0.762 for the test set and R 2 = 0.964 for the training set) is similar to the correlation strength obtained from the HSE shift ( R 2 = 0.797 for the test set and R 2 = 0.971 for the training set). This relationship transforms the range of band gaps from 0.2 to 2.0 eV with PBE to 0.5–2.5 eV with the corrected HSE band gaps, which is similar to the effect seen when transforming from PBE to the corrected band gap when studying a variety of materials . The MAE for the HSE vs PBE band gaps using this method has an MAE of 0.157 eV in comparison to simple linear regression with an MAE of 0.200 eV, which is comparable to the improvement seen with neural networks …”

Section: Methodssupporting

confidence: 60%

“…This relationship transforms the range of band gaps from 0.2 to 2.0 eV with PBE to 0.5−2.5 eV with the corrected HSE band gaps, which is similar to the effect seen when transforming from PBE to the corrected band gap when studying a variety of materials. 63 The MAE for the HSE vs PBE band gaps using this method has an MAE of 0.157 eV in comparison to simple linear regression with an MAE of 0.200 eV, which is comparable to the improvement seen with neural networks. 64 Materials Descriptors.…”

Section: ■ Summarymentioning

confidence: 67%

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Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Sharma

Ward

Bhimani

et al. 2023

ACS Appl. Mater. Interfaces

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The non-toxic and stable chalcogenide perovskite BaZrS3 fulfills many key optoelectronic properties for a high-efficiency photovoltaic material. It has been shown to possess a direct band gap with a large absorption coefficient and good carrier mobility values. With a reported band gap of 1.7–1.8 eV, BaZrS3 is a good candidate for tandem solar cell materials; however, its band gap is significantly larger than the optimal value for a high-efficiency single-junction solar cell (∼1.3 eV, Shockley–Queisser limit)thus doping is required to lower the band gap. By combining first-principles calculations and machine learning algorithms, we are able to identify and predict the best dopants for the BaZrS3 perovskites for potential future photovoltaic devices with a band gap within the Shockley–Queisser limit. It is found that the Ca dopant at the Ba site or Ti dopant at the Zr site is the best candidate dopant. Based on this information, we report for the first time partial doping at the Ba site in BaZrS3 with Ca (i.e., Ba1–x Ca x ZrS3) and compare its photoluminescence with Ti-doped perovskites [i.e., Ba(Zr1–x Ti x )S3]. Synthesized (Ba,Ca)ZrS3 perovskites show a reduction in the band gap from ∼1.75 to ∼1.26 eV with <2 atom % Ca doping. Our results indicate that for the purpose of band gap tuning for photovoltaic applications, Ca-doping at the Ba-site is superior to Ti-doping at the Zr-site reported previously.

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Section: Methodssupporting

confidence: 60%

Section: ■ Summarymentioning

confidence: 67%

Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Sharma

Ward

Bhimani

et al. 2023

ACS Appl. Mater. Interfaces

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“…The decrease matches qualitatively with the change of band gap detected using UV–vis DRS. It is well-known that PBE cannot quantitatively reproduce band gaps due to its inherent limitations …”

Section: Resultsmentioning

confidence: 99%

Photodegradation of Xanthate Using a Bi₂Fe₄O₉/ZnIn₂S₄ Heterojunction: Experimental and DFT Studies

Nie,

Liu,

Liu

et al. 2023

Ind. Eng. Chem. Res.

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Bi 2 Fe 4 O 9 /ZnIn 2 S 4 (BFO/ZIS) composites are synthesized by using a facile impregnation method and tested for the photocatalytic degradation of xanthate for the first time. The effects of multiple parameters, including catalyst dosage and the pH value of solution, on xanthate photodegradation are investigated systematically. Furthermore, the photocatalytic mechanisms of BFO/ZIS are proposed based on various characterizations and density functional theory calculations. Results show that the degradation efficiency of 30BFO/ZIS is 98.25% during 90 min of visible light irradiation, which is 2.63 and 2.89 times higher than that of pure ZIS and BFO, respectively. This is mainly because the type II heterojunction formed between BFO and ZIS improves the separation of photogenerated carriers. Furthermore, the xanthate photodegradation efficiency of 30BFO/ZIS decreases slightly from 98.25 to 92.86% after four cycles, indicating its excellent durability and stability. Finally, the chemical oxygen demand (COD) removal efficiency of 30BFO/ZIS is up to 81.06%, exhibiting a high degree of mineralization.

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“…14 Computational design of materials has become an essential part of PV design. [15][16][17] By using computational methods, researchers can simulate and predict the performance of different materials and devices without costly and time-consuming experimental trials. Such methodology can greatly accelerate the materials design and device optimization process.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in energy chemistry: introduction, challenges and perspectives

2023

Energy Adv.

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Effectively improving the accuracy of PBE functional in calculating the solid band gap via machine learning

Cited by 30 publications

References 31 publications

Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Photodegradation of Xanthate Using a Bi₂Fe₄O₉/ZnIn₂S₄ Heterojunction: Experimental and DFT Studies

Machine learning in energy chemistry: introduction, challenges and perspectives

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Effectively improving the accuracy of PBE functional in calculating the solid band gap via machine learning

Cited by 30 publications

References 31 publications

Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite

Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite

Photodegradation of Xanthate Using a Bi2Fe4O9/ZnIn2S4 Heterojunction: Experimental and DFT Studies

Machine learning in energy chemistry: introduction, challenges and perspectives

Contact Info

Product

Resources

About

Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Machine Learning-Aided Band Gap Engineering of BaZrS₃ Chalcogenide Perovskite

Photodegradation of Xanthate Using a Bi₂Fe₄O₉/ZnIn₂S₄ Heterojunction: Experimental and DFT Studies