Polymers are one of the most widely used materials in modern society. The interest of applying polymers under extreme conditions (high pressure and high temperature) is ever increasing. However, our knowledge of the equation of state (EOS) and phase transition of polymers at high pressures and high temperatures is extremely limited, which prevents their applications in broad fields. Because of their mixed phases and their hierarchical structures, investigation on the structures and properties of polymers at extreme conditions is a big challenge to date. In this short review, we summarize the recently published studies on the EOS and phase transition of polymers at extreme conditions. We point out the challenges faced and the limitations of the experimental techniques used, which is expected to be useful for the investigations of the EOS and phase transition of polymers in the future.

With the rapid advancement of artificial intelligence (AI) technologies and hardware capabilities, AI has gradually become a revolutionary tool driving transformative changes across multiple scientific research domains. In the field of materials science, machine learning methods are significant in high-throughput materials design and property prediction. Over the past decade, machine learning-based approaches for constructing interatomic potentials have been widely applied in the study of material properties, and are providing crucial support for the theoretical design of novel materials and in-depth understanding of their underlying microscopic mechanisms. This article reviews the development of machine learning potentials, and introduces their fundamental workflows. The principles of mainstream methods and their applications in materials property research are outlined. Moreover, recent progress in emerging universal potential models is briefly discussed, then concludes with an analysis of current challenges and future research directions.