Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface

The crystal structure prediction (CSP) has emerged in recent years as a major theme in research across many scientific disciplines in physics, chemistry, materials science, and geoscience, among others. The central task here is to find the global energy minimum on the potential energy surface (PES) associated with the vast structural configuration space of pertinent crystals of interest, which presents a formidable challenge to efficient and reliable computational implementation. Considerable progress in recent CSP algorithm developments has led to many methodological advances along with successful applications, ushering in a new paradigm where computational research plays a leading predictive role in finding novel material forms and properties which, in turn, offer key insights to guide experimental synthesis and characterization. In this Account, we first present a concise summary of major advances in various CSP methods, with an emphasis on the overarching fundamentals for the exploration of the PES and its impact on CSP. We then take our developed CALYPSO method as an exemplary case study to give a focused overview of the current status of the most prominent issues in CSP methodology. We also provide an overview of the basic theory and main features of CALYPSO and emphasize several effective strategies in the CALYPSO methodology to achieve a good balance between exploration and exploitation. We showcase two exemplary cases of the theory-driven discovery of high-temperature superconducting superhydrides and a select group of atypical compounds, where CSP plays a significant role in guiding experimental synthesis toward the discovery of new materials. We finally conclude by offering perspectives on major outstanding issues and promising opportunities for further CSP research.