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 …

Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …

Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …

As representatives of third-generation solar cells, perovskite solar cells (PSCs) have
experienced rapid development. Suffering from inefficient traditional trial-and-error methods …

Perovskite solar cells (PSCs) have become a hot spot in the field of photovoltaic research in
recent years due to their low fabrication costs and rising efficiencies. However, the …

Material science has historically evolved in tandem with advancements in technologies for
characterization, synthesis, and computation. Another type of technology to add to this mix is …

Over the last few years there has been an increasing number of papers using machine
learning (ML) as a tool to aid research directed towards perovskite solar cells. This review …

Perovskite materials have been one of the most important research objects in materials
science due to their excellent photoelectric properties as well as correspondingly complex …

As machine learning (ML) continues to advance in the field of materials science, the
variation in strategies for the same steps of the ML workflow becomes increasingly …

The development and design of energy materials are essential for improving the efficiency,
sustainability, and durability of energy systems to address climate change issues. However …