Application Progress of Machine Learning in Optoelectronic Material Design and Device Performance Prediction

Key Points

• The aim is to explore the role of machine learning in optimizing optoelectronic materials and predicting device performance.
• Systematic review of machine learning workflows in material science.
• Analysis of machine learning applications in perovskite stability screening and bandgap regression.
• Exploration of defect dynamics simulation and process optimization strategies.
• Machine learning accelerates the discovery of new low-dimensional materials.
• Introduced methods successfully predict the physical mechanisms of quantum dots.
• Identified challenges in applying machine learning to material design and performance prediction.