Abstract Machine learning is a powerful tool that can provide a way to revolutionize the
material science. Its use for the designing and screening of materials for polymer solar cells …

In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …

Accurate and efficient prediction of polymer properties is of great significance in polymer
design. Conventionally, expensive and time-consuming experiments or simulations are …

Under the guidance of the material genome initiative (MGI), the use of data‐driven methods
to discover new materials has become an innovation of materials science. The polymer …

Nonfullerene, a small molecular electron acceptor, has substantially improved the power
conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom …

In the recent decades, with rapid development in computing power and algorithms, machine
learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the …

The great tunability of the properties of halide perovskites presents new opportunities for
optoelectronic applications as well as significant challenges associated with exploring …

This article reviews recent developments in the applications of machine learning, data-
driven modeling, transfer learning, and autonomous experimentation for the discovery …

Despite the progress in machine learning (ML) in terms of prediction of power conversion
efficiency (PCE) in organic photovoltaics (OPV), the effectiveness of ML in practical …

Motivated by the challenging of deep learning on the low data regime and the urgent
demand for intelligent design on highly energetic materials, we explore a correlated deep …