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Expanding PyProcar for new features, maintainability, and reliability

https://doi.org/10.1016/j.cpc.2023.109063

Lang, Logan; Tavadze, Pedram; Tellez, Andres; Bousquet, Eric; Xu, He; Muñoz, Francisco; Vasquez, Nicolas; Herath, Uthpala; Romero, Aldo H (April 2024, Computer Physics Communications)

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Using convolutional neural networks to support examiners in duct tape physical fit comparisons

https://doi.org/10.1016/j.forsciint.2023.111884

Lang, Logan; Tavadze, Pedram; Prusinowski, Meghan; Andrews, Zachary; Neumann, Cedric; Trejos, Tatiana; Romero, Aldo H (December 2023, Forensic Science International)

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MechElastic: A Python library for analysis of mechanical and elastic properties of bulk and 2D materials

https://doi.org/10.1016/j.cpc.2021.108068

Singh, Sobhit; Lang, Logan; Dovale-Farelo, Viviana; Herath, Uthpala; Tavadze, Pedram; Coudert, François-Xavier; Romero, Aldo H. (October 2021, Computer Physics Communications)
null (Ed.)
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Exploring the elastic and electronic properties of chromium molybdenum diboride alloys

https://doi.org/10.1016/j.jallcom.2021.158885

Dovale-Farelo, Viviana; Tavadze, Pedram; Verstraete, Matthieu J.; Bautista-Hernández, Alejandro; Romero, Aldo H. (June 2021, Journal of Alloys and Compounds)
null (Ed.)
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Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

https://doi.org/10.1038/s41524-021-00651-0

Tavadze, Pedram; Boucher, Reese; Avendaño-Franco, Guillermo; Kocan, Keenan X.; Singh, Sobhit; Dovale-Farelo, Viviana; Ibarra-Hernández, Wilfredo; Johnson, Matthew B.; Mebane, David S.; Romero, Aldo H. (November 2021, npj Computational Materials)

Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the HubbardUandJparameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore theUandJparameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largestUcorrection. PBE has the smallest standard deviation and itsUandJparameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.
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PyProcar: A Python library for electronic structure pre/post-processing

https://doi.org/10.1016/j.cpc.2019.107080

Herath, Uthpala; Tavadze, Pedram; He, Xu; Bousquet, Eric; Singh, Sobhit; Muñoz, Francisco; Romero, Aldo H. (June 2020, Computer Physics Communications)

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