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

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”.

This Special Issue aims to showcase recent progress using machine learning approaches to address challenges in organic, inorganic, and hybrid photoelectric materials. We invite Research Articles, Reviews, and Perspectives. The topics to be covered include, but are not limited to, the following:
● Development of photoelectric-related material or system database using high throughput experimental/computational methods;
● Machine learning guided research and development of photoelectric materials;
● Interpretability machine learning modeling of the structure-property relationships for photoelectric materials;
● Small dataset machine learning predictive modeling of photoelectric materials;
● AI-enhanced self-developed tool/software for photoelectric materials;
● GPT models and applications for photoelectric materials.

30 Nov 2024