Rational design of formamidine tin-based perovskite solar cell with 30% potential efficiency via 1-D device simulation.

As a promising photovoltaic technology, halide perovskite solar cells (PSCs) have recently attracted wide attention. This work presents a systematic simulation of low bandgap formamidinium tin iodide (FASnI3)-based p-n heterojunction PSCs to investigate the effects of multiple optoelectronic variations on the photovoltaic performance. The structures of the simulated devices are n-i-p, electron transport layer-free (ETL-free), hole transport layer-free (HTL-free), and inverted HTL-free. The simulation is conducted with the Solar Cell Capacitance Simulator (SCAPS-1D). The power conversion efficiencies (PCEs) dramatically decrease when the acceptor doping density (NA) of the absorber layer exceeds 1016 cm-3. For all devices, the photovoltaic parameters dramatically decrease when the absorber defect density (Nt) is over 1015 cm-3, and the best absorber layer thickness is 1000 nm. It should be pointed out that the Nt and the interface defect layer (IDL) are the primary culprits that seriously affect the device performance. When the interfacial defect density (Nit) exceeds 1012 cm-3, PCEs begin to decline significantly. Therefore, paying attention to these defect layers is necessary to improve the PCE. Furthermore, the proper conduction band offset (CBO) between the electron transport layer (ETL) and absorber layer positively affects PSCs' performance. These simulation results help fabricate highly efficient and environment-friendly narrow bandgap PSCs.

Research on novel quantum phenomena of transition metal-doped ZrO2 nanosheets.

The spintronic properties of cubic ZrO2 (c-ZrO2) nanosheets with intrinsic defects and transition metal (TM) elements doping have been systematically studied by first-principle calculation. The results show that impurity Fe has the lowest formation energy in each monolayer compared to other defects. The most stable (111) nanosheet, coupled with the higher defect formation energy, tends to disintegrate. Only Zr vacancy (VZr) on the (110) surface or O vacancy (VO) on the (111) surface can generate a ferromagnetic ground state, while other intrinsic defects cannot introduce spin polarization. Ni-doped (110) monolayer cannot introduce a local magnetic moment, while Fe and Co can. The magnetic moments produced by Fe, Co, and Ni in the (111) sheet are 2, 4, and 1 µB, respectively. Further investigation revealed that the magnetism was mainly contributed by the d orbitals of the TM atom and the p orbitals of the surrounding O atoms. Magnetic couplings show that only Co-Co doped monolayers can produce macroscopic magnetism. These are predicted to produce TCs Curie temperature above room temperature when Co-Co distances are 5.070 and 6.209 Å on the (110) surface and 7.170 and 9.485 Å on the (111) surface. The research is beneficial to the refinement of the development of spintronics.

Predicting photovoltaic parameters of perovskite solar cells using machine learning.

Perovskite solar cells (PSCs) have garnered significant attention owing to their highly power conversion efficiency (PCE) and cost-effectiveness. Traditionally, screening for PSCs with superior photovoltaic parameters relies on resource-intensive trial-and-error experiments. Nowadays, time-saving machine learning (ML) techniques serve as an artificial intelligence approach to expedite the prediction of photovoltaic parameters using accumulated research datasets. In this study, we employ seven supervised ML methods to forecast key photovoltaic parameters for PSCs such as PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Particularly, we design an artificial neural network (ANN) architecture that incorporates residual connectivity and layer normalization after the linear layers to enhance the scope and adaptability of the network. For PCE andJsc, ANN demonstrates superior prediction accuracy, yielding root mean square errors of 2.632% and 2.244 mA cm-2, respectively. The Random Forest (RF) model exhibits exceptional prediction performance forVocand FF. Additionally, an interpretability analysis of the model is conducted to elucidate the impact of features on PCE prediction, offering a novel approach for accurate and interpretable ML methods in the context of PSCs.

Study of crystal properties based on attention mechanism and crystal graph convolutional neural network.

The prediction of crystal properties has always been limited by huge computational costs. In recent years, the rise of machine learning methods has gradually made it possible to study crystal properties on a large scale. We propose an attention mechanism-based crystal graph convolutional neural network, which builds a machine learning model by inputting crystallographic information files and target properties. In our research, the attention mechanism is introduced in the crystal graph convolutional neural network (CGCNN) to learn the local chemical environment, and node normalization is added to reduce the risk of overfitting. We collect structural information and calculation data of about 36 000 crystals and examine the prediction performance of the models for the formation energy, total energy, bandgap, and Fermi energy of crystals in our research. Compared with the CGCNN, it is found that the accuracy (ACCU) of the predicted properties can be further improved to varying degrees by the introduction of the attention mechanism. Moreover, the total magnetization and bandgap can be classified under the same neural network framework. The classification ACCU of wide bandgap semiconductor crystals with a bandgap threshold of 2.3 eV reaches 93.2%, and the classification ACCU of crystals with a total magnetization threshold of 0.5 µBreaches 88.8%. The work is helpful to realize large-scale prediction and classification of crystal properties, accelerating the discovery of new functional crystal materials.