Advances in high-pressure materials discovery enabled by machine learning

Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenges related to computational efficiency and scalability, particularly when applied to complex systems. Recent advances in machine learning (ML) have shown tremendous promise in addressing these limitations, enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions. This review provides a concise overview of recent progress in ML-assisted CSP methodologies, with a particular focus on machine learning potentials and generative models. By critically analyzing these advances, we highlight the transformative impact of ML in accelerating materials discovery, enhancing computational efficiency, and broadening the applicability of CSP. Additionally, we discuss emerging opportunities and challenges in this rapidly evolving field.