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Topic: Advances in Machine Learning for Photoelectric
Materials Research and Applications

Guest Editors

Prof. Fuquan Bai
International Joint Research Laboratory of Nano-Micro Architecture Chemistry,
Institute of Theoretical Chemistry, College of Chemistry, Jilin University.
Changchun, Jilin, China.

Prof. Baisheng Sa
School of Materials Science and Engineering, Fuzhou University, Fuzhou, Fujian,
China.

Special Topic Introduction

By converting photo-energy into electrical energy or vice versa, photoelectric
materials play a critical role in various applications, for instance, solar cells, light-
emitting devices, photocatalytic material and photodetectors, etc. Focusing on
improving the efficiency of converting light into electrical energy, accurately

controlling the electronic structures in photoelectric materials is essential.
Moreover, continuous research and development on new materials are required to
reduce costs and enhance stability for the practical photoelectric applications. With
the boosting of computing power and numerical algorithms, machine learning-
based artificial intelligence (AI) for science methods is increasingly important
in the rational design of novel materials for photoelectric applications. The high-
throughput technology realizes the automatic processing of high-standard databases
for photoelectric materials. Furthermore, based on data-driven machine learning
materials screening according to both experimental and theoretical databases, we

can now realize the discovery of new optoelectronic materials at large scales and
with high precision and efficiency, and predict structure-performance relationships.
On the other hand, the fast evolution of generative pre-trained transformer (GPT)
models greatly enhances the possibilities of the reversal design of photoelectric
materials. Therefore, to expand the capabilities and applications of photoelectric
materials in developing machine learning approaches, we are pleased to announce
this Special Issue titled “Advances in Machine Learning for Photoelectric Materials

Research and Applications”.

Journal of Materials Informatics I