A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction

Crystal Property Prediction (CPP) and Crystal Structure Prediction (CSP) play an important role in accelerating the design and discovery of advanced materials across various scientific disciplines. Traditional computational approaches to CSP/CPP often face challenges such as high computational costs, limited scalability, and difficulties in exploring complex energy surfaces. In recent years, the combination of machine learning (ML) has emerged as a powerful approach to overcome these limitations, offering data-driven methods that enhance prediction accuracy while significantly reducing computational expenses. This review provides a comprehensive overview of the evolution of CSP and CPP methodologies, with particular emphasis on the transition from classical optimization algorithms to modern ML-based methods. Various supervised and unsupervised ML algorithms applied in this field are discussed in detail. Beyond structure and property prediction, recent advancements in ML-based modeling of crystal defects are also reviewed. Moreover, several recent case studies on CSP/CPP are presented to demonstrate the practical effectiveness of ML approaches. Finally, the review discusses current challenges and provides recommendations for future research in ML-based investigations of CSP/CPP.