Pressure induced modulations in the optoelectronic properties of Hg2Cl2 compound: Insights from the first-principle calculations

Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies

“…The E g values of CeMoN 3 and CeWN 3 compounds are predicted to be ∼1.55 and 1.76 eV from RFR ML model for the G 0 W 0 @HSE06 input of 1.60 and 1.81 eV respectively. While the DFT calculations at HSE06 and G 0 W 0 @HSE06 level of theories can closely predict the experimentally observed E g values of the systems within <1% error, 40,42,45,48,97,99,100 the predicted E g values of CeBN 3 (B = Mo, W) compounds as obtained from RFR ML model are estimated within an error of ∼3% with standard deviation of ∼0.035. The selective choice of G 0 W 0 @HSE06 level of theory in the DFT calculation as input for the prediction of band gaps is thus meaningful, given its efficacy in reproducing the experimental band gaps of many such compounds as reported elsewhere.…”

“…The negative values of ε 1 ( ω ) signify metallic Drude – like behaviour of the compounds within the said energy windows. 42,43,104…”

“…Moreover, nite values of 3 2 (u) between ẼM ∼2.30 (2.40) and 10.01 (12.50) eV for CeMoN 3 (CeWN 3 ) crystal corroborate strong attenuation of the incident EM wave inside the crystal systems within the said energy window. 42,43 The response of strong attenuation of the incident EM wave inside the CeBN 3 (B = Mo, W) compounds, so reected in the 3 2 (u)-ẼM plots (Fig. 6), can also be attributed from their absorption coefficients [a(u)] and optical conductivities [s(u)].…”

“…[37][38][39] In this regard DFT calculations, as accomplished from single-shot GW (G 0 W 0 ) approximation using hybrid exchange-correlation (XC) functionals such as Heyd-Scuseria-Ernzerhof (HSE), Becke-3-parameter-Lee-Yang-Parr (B3LYP) and B3PW91 are known to predict the E g values of the compounds close to the experimental results. 14,33,[40][41][42][43][44][45][46][47][48] The major pitfalls of such calculations are that they are computationally demanding and need high end servers to run them. In this context, the machine learning (ML) is now considered as an effective alternative route to avoid the inherent computational costs linked with DFT calculations and helps in establishing a simple model between the characteristics features of materials and the target variable (here E g ).…”

“…However, beyond ẼM ∼3.75 (6.86) eV, 3 1 (u) values of CeMoN 3 (CeWN 3 ) system gradually decrease and attain negative values within the energy window ẼM ∼4.72 (7.86)-10.42(13.39) eV. The negative values of 3 1 (u) signify metallic Drudelike behaviour of the compounds within the said energy windows 42,43,104.…”