Modified band alignment method to obtain hybrid functional accuracy from standard DFT: Application to defects in highly mismatched III-V:Bi alloys

Polak, Maciej P.; Kudrawiec, R.; Jacobs, Ryan; Szlufarska, Izabela; Morgan, Dane

doi:10.1103/physrevmaterials.5.124601

Cited by 2 publications

(5 citation statements)

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“…Note that we find errors are similar for lower and higher band gap materials, making the errors particularly problematic for systems with low band gaps. In addition, it is worth noting that for some defects and charge states, an LDA/GGA approach may incorrectly capture localization effects 27,38 , which is not corrected for in any band alignment-like method, including MBA 26 . These cases are difficult to unambiguously identify in a high-throughput approach, and are therefore included in this model as well, influencing the statistics.…”

Section: Resultsmentioning

confidence: 99%

“…This choice can lead to the relaxation algorithm failing to find some local minima 33 but was made to allow efficiency in the high-throughput calculations that enable this study. More details of the DFT calculations can be found in Mannodi-Kanakkithodi et al 25 and Polak et al 26 The LDA/GGA results from these studies were corrected with the MBA scheme, which is described in details in Ref. 26 .…”

Section: Datasets and Methodsmentioning

confidence: 99%

“…More details of the DFT calculations can be found in Mannodi-Kanakkithodi et al 25 and Polak et al 26 The LDA/GGA results from these studies were corrected with the MBA scheme, which is described in details in Ref. 26 . That approach eliminated the possible inconsistency coming from mixing LDA and PBE calculations in the same dataset.…”

Section: Datasets and Methodsmentioning

confidence: 99%

“…A root mean squared error (RMSE) of 0.34 eV was obtained for ML-predicted PBE transition levels vs. DFT-PBE calculated transition levels, while an RMSE of 0.61 eV was obtained for ML-predicted HSE06 transi-tion levels vs. DFT-HSE06 calculated transition levels. Sec-ond, from the perspective of enhancing the accuracy of defect transition levels obtained from DFT, recent work from Polak et al 26 formulated the modified band alignment (MBA) approach as an enhancement to the previously-used band alignment approach 27,28 . The MBA for defect levels is expressed as:…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 illustrates the necessary quantities, and the whole approach is described in detail in Ref. 26 . This MBA approach was found to improve the accuracy of defect transition levels obtained with local or semilocal functionals (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Polak

Jacobs

Mannodi-Kanakkithodi

et al. 2022

The Journal of Chemical Physics

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Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity density functional theory (DFT) calculations, corrected using a recently-proposed modified band alignment scheme which well-approximates transition levels from high-fidelity DFT. The model fit to the large multi-fidelity database shows improved accuracy compared to models trained on the more limited high-fidelity values. Crucially, in our approach when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV versus high-fidelity hybrid functional values, when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of band gap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zincblende III-V and II-VI systems.

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Section: Resultsmentioning

confidence: 99%

Section: Datasets and Methodsmentioning

confidence: 99%

Section: Datasets and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Polak

Jacobs

Mannodi-Kanakkithodi

et al. 2022

The Journal of Chemical Physics

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Experimental and theoretical studies of native deep-level defects in transition metal dichalcogenides

et al. 2022

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Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX2 (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.

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Modified band alignment method to obtain hybrid functional accuracy from standard DFT: Application to defects in highly mismatched III-V:Bi alloys

Cited by 2 publications

References 81 publications

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Experimental and theoretical studies of native deep-level defects in transition metal dichalcogenides

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