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838 results on '"Kumar, Upendra"'

4. Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory

Author

Kumar, Upendra, Kim, Hyeon Woo, Maurya, Gyanendra Kumar, Raj, Bincy Babu, Singh, Sobhit, Kushwaha, Ajay Kumar, Cho, Sung Beom, and Ko, Hyunseok

Subjects
Condensed Matter - Materials Science
Abstract

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

Published
2024

15. Designing Pr-based Advanced Photoluminescent Materials using Machine Learning and Density Functional Theory

Author

Kumar, Upendra, Kim, Hyeon Woo, Singh, Sobhit, Ko, Hyunseok, and Cho, Sung Beom

Subjects
Condensed Matter - Materials Science
Abstract

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr$_3$AlO$_6$, Pr$_4$Al$_2$O$_9$, Pr$_3$ScO$_6$ and Pr$_3$Sc$_5$O$_{12}$) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.

Published
2023

36. Identification of Image Spoofing Using Deep Convolution Neural Network

Author

Tripathi, Esha, Kumar, Upendra, Tripathi, Surya Prakash, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Swaroop, Abhishek, editor, Kansal, Vineet, editor, Fortino, Giancarlo, editor, and Hassanien, Aboul Ella, editor

Published
2023
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42. Gallium-Boron-Phosphide (GaBP$_2$): A New III-V Semiconductor for photovoltaics

Author

Kumar, Upendra, Nayak, Sanjay, Chakrabarty, Soubhik, Bhattacharjee, Satadeep, and Lee, Seung-Cheol

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Using machine learning (ML) approach, we unearthed a new III-V semiconducting material having an optimal bandgap for high efficient photovoltaics with the chemical composition of Gallium-Boron-Phosphide(GaBP$_2$, space group: Pna2$_1$). ML predictions are further validated by state of the art ab-initio density functional theory (DFT) simulations. The stoichiometric Heyd-Scuseria-Ernzerhof (HSE) bandgap of GaBP$_2$ is noted to 1.65 eV, a close ideal value (1.4-1.5 eV) to reach the theoretical Queisser-Shockley limit. The calculated electron mobility is similar to that of silicon. Unlike perovskites, the newly discovered material is thermally, dynamically and mechanically stable. Above all the chemical composition of GaBP$_2$ are non-toxic and relatively earth-abundant, making it a new generation of PV material. Using ML, we show that with a minimal set of features the bandgap of III-III-V and II-IV-V semiconductor can be predicted up to an RMSE of less than 0.4 eV. We presented a set of scaling laws, which can be used to estimate the bandgap of new III-III-V and II-IV-V semiconductor, with three different crystal phases, within an RMSE of approx. 0.5 eV.

Published
2019
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