Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …

Metal-halide hybrid perovskite materials are excellent candidates for solar cells and
photoelectric devices. In recent years, machine learning (ML) techniques have developed …

Perovskite halide compounds are expected to provide various applications such as solar
cells and light-emitting diodes. In the present work, structure models of ABX 3 (A= Rb, or Cs …

Feature selection has kept playing a significant role in the workflow of materials machine
learning, but currently most of works of materials machine learning tend to use single or …

Currently, a major obstacle restricting the commercial application of halide perovskites is
their low thermodynamic stability. Herein, inspired by the high-stability high-entropy alloys …

In this paper, we have built a stepwise model based on the XGBoost machine learning
algorithm to screen perovskite materials with high dielectric breakdown strength by …

Even though the prototypes built using Fused Deposition Modelling (FDM) process are
found to exhibit good mechanical properties, there are ample scopes to improve them by …

In the post-Moore's law era, the progress of electronics relies on discovering superior
semiconductor materials and optimizing device fabrication. Computational methods …

Perovskite solar cells (PSC) are a potential candidate for next‐generation photovoltaic
technology. Despite the significant advancements in the field of PSCs, the ongoing …

Open framework structures (eg, ScF 3, Sc 2 W 3 O 12, etc.) exhibit significant potential for
thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and …