Feasibility study of Mg storage in a bilayer silicene anode via application of an external electric field.

With the goal of developing a Si-based anode for Mg-ion batteries (MIBs) that is both efficient and compatible with the current semiconductor industry, the current research utilized classical Molecular Dynamics (MD) simulation in investigating the intercalation of a Mg2+ ion under an external electric field (E-field) in a 2D bilayer silicene anode (BSA). First principles density functional theory calculations were used to validate the implemented EDIP potentials. Our simulation shows that there exists an optimum E-field value in the range of 0.2-0.4 V Å-1 for Mg2+ intercalation in BSA. To study the effect of the E-field on Mg2+ ions, an exhaustive spread of investigations was carried out under different boundary conditions, including calculations of mean square displacement (MSD), interaction energy, radial distribution function (RDF), and trajectory of ions. Our results show that the Mg2+ ions form a stable bond with Si in BSA. The effects of E-field direction and operating temperature were also investigated. In the X-Y plane in the 0°-45° range, 15° from the X-direction was found to be the optimum direction for intercalation. The results of this work also suggest that BSA does not undergo drastic structural changes during the charging cycles with the highest operating temperature being ∼300 K.

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

In this research, solar cell capacitance simulator-one-dimensional (SCAPS-1D) software was used to build and probe nontoxic Cs-based perovskite solar devices and investigate modulations of key material parameters on ultimate power conversion efficiency (PCE). The input material parameters of the absorber Cs-perovskite layer were incrementally changed, and with the various resulting combinations, 63,500 unique devices were formed and probed to produce device PCE. Versatile and well-established machine learning algorithms were thereafter utilized to train, test, and evaluate the output dataset with a focused goal to delineate and rank the input material parameters for their impact on ultimate device performance and PCE. The most impactful parameters were then tuned to showcase unique ranges that would ultimately lead to higher device PCE values. As a validation step, the predicted results were confirmed against SCAPS simulated results as well, highlighting high accuracy and low error metrics. Further optimization of intrinsic material parameters was conducted through modulation of absorber layer thickness, back contact metal, and bulk defect concentration, resulting in an improvement in the PCE of the device from 13.29 to 16.68%. Overall, the results from this investigation provide much-needed insight and guidance for researchers at large, and experimentalists in particular, toward fabricating commercially viable nontoxic inorganic perovskite alternatives for the burgeoning solar industry.

Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3-xBrx).

In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI3-xBrx devices. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentration─parameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device. Five different algorithms were utilized to explore feature engineering. The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI3: a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. ML analyses for optimal bromine doping resulted in the discovery of two devices with bromine concentrations of 22.43% (Br22) and 25.63% (Br25), with the latter being a more fine-tuned value obtained through extra rigorous analysis. To understand the total and relative impact of each feature on power conversion efficiency (PCE), Br22 and Br25 were analyzed with a state-of-the-art algorithm, namely, the SHapley Additive exPlanations (SHAP) algorithm. Focusing on the two discovered devices, further device optimizations were carried out utilizing SCAPS. Modulations of absorber thickness, bulk and interfacial defect density, and choice of electron transport layer (ETL) and hole transport layer (HTL) materials were tried. Device stability was analyzed through carrier lifetime studies. Following these optimization steps, Br22 and Br25 demonstrated final high PCE values of 20.72 and 17.37%, respectively. The ML-assisted quantitative analysis of the current work provides significant confidence for optimal bromine-doped tin-based devices to be considered as viable and competitive nontoxic alternatives to traditional technologies.