ABX3 perovskite-based materials have attracted research attention in various electronic and optoelectronic applications. The ability to tune the energy band gap through various dopants makes perovskites a potential candidate in many implementations. Among various perovskite materials, BaTiO3 has shown great applicability as a robust UV absorber with an energy band gap of around 3.2 eV. Herein, we provide a new sonochemical-assisted solid-phase method for preparing BaTiO3 thin films that optoelectronic devices can typically be used. BaTiO3 nano-powder and the thin film deposited on a glass substrate were characterized using physicochemical and optical techniques. In addition, the work demonstrated a computational attempt to optically model the BaTiO3 from the atomistic level using density functional theory to the thin film level using finite difference time domain Maxwell's equation solver. Seeking repeatability, the dispersion and the extinction behavior of the BaTiO3 thin film have been modeled using Lorentz-Dude (LD) coefficients, where all fitting parameters are listed. A numerical model has been experimentally verified using the experimental UV–Vis spectrometer measurements, recording an average root-mean-square error of 1.44%.

Various solar cell architectures and materials are currently studied, seeking enhanced photon management mechanisms. Herein, we provide an attempt to prepare, characterize, model, and simulate a novel semiconductor, Lithium Titanate, which has a band gap of 3.55 eV. The semiconductor was prepared from H2TiO3 and LiCO3 by calcination at 500 °C for 5 h after grinding with deionized water. XRD, SEM, EDX, and AFM carried out a complete morphological characterization on powder and thin-film levels. Additionally, experimentally validated atomistic DFT modeling was performed where the density of states and the imaginary part of the permittivity were extracted. Finally, the optical transmission spectrum was simulated for a 4.28 μm thickness film, with the aid of a finite-difference time-domain solver, against an experimentally measured spectrum, showing a root-mean-square mismatching error of 3.78%.

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Tandem structures have been introduced to the photovoltaics (PV) market to boost power conversion efficiency (PCE). Single-junction cells’ PCE, either in a homojunction or heterojunction format, are clipped to a theoretical limit associated with the absorbing material bandgap. Scaling up the single-junction cells to a multi-junction tandem structure penetrates such limits. One of the promising tandem structures is the perovskite over silicon topology. Si junction is utilized as a counter bare cell with perovskites layer above, under applying the bandgap engineering aspects. Herein, we adopt BaTiO 3 /CsPbCl 3 /MAPbBr 3 /CH 3 NH 3 PbI 3 /c-Si tandem structure to be investigated. In tandem PVs, various input parameters can be tuned to maximize PCE, leading to a massive increase in the input combinations. Such a vast dataset directly reflects the computational requirements needed to simulate the wide range of combinations and the computational time. In this study, we seed our random-forest machine learning model with the 3×10 6 points’ dataset with our optoelectronic numerical model in SCAPS. The machine learning could estimate the maximum PCE limit of the proposed tandem structure at around 37.8%, which is more than double the bare Si-cell reported by 18%.