Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …

In recent years, a rapid evolution of organic solar cells (OSCs) has been achieved by virtue
of structural design of active layer materials and optimization of film morphology. Along with …

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 …

Chemical structure of small molecule acceptors determines their performance in organic
solar cells. Multiscale simulations are necessary to avoid trial‐and‐error based design …

Polymer donor materials have been considered as a game changer, especially in the early
history of polymer solar cells. However, much progress is the result of hard work resulting …

Herein, a simple “Same-A-Strategy”(SAS), constructing p-type and n-type photovoltaic
materials with the same electron-accepting (A) unit of benzotriazole, is adopted to initially …

Non-fused ring-based OSCs are an excellent choice, which is attributed to their low cost and
flexibility in applications. However, developing efficient and stable non-fused ring-based …

In recent years, development in organic solar cells speeds up and performance continuously
increases. From the last few years, machine learning gains fame among scientists who are …

The problem of low efficiency of organic solar cells can be solved by improving the charge
mobility and open circuit voltage of these cells. The current research aims to present the role …

Density functional theory, along with its time dependent computational approach were
employed in order to fine tune the photovoltaic attributes along with the efficiency of the MO …