Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface

Abstract

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.

Two-Dimensional Tri-Hex Carbon: A Promising, Efficient, and Acid-Resistant Photocatalyst for Water Splitting

“…The structure search was conducted using the PSO algorithm with the evolutionary algorithm implemented in the CALYPSO method, − which is an effective tool for structural design. ,− Instead of total energy, we applied the bandgap-oriented structure search to rapidly progress toward the direction of finding the optimal electronic bandgap. The population size and number of generations were set to 30.…”

Structures of LaH10, EuH9, and UH8 superhydrides rationalized by electron counting and Jahn-Teller distortions in a covalent cluster model

“…22,23 The "workhorse" of this field is crystal structure prediction (CSP), an umbrella term for computational methods for determining the crystal structure of a material without any prior information. [24][25][26][27][28][29] Studies utilizing this method generally identify the most thermodynamically stable phases for a chosen AH x system as a function of pressure by running CSP calculations at various pressures and A:H ratios. 8 Superconducting critical temperatures can then be estimated for all candidate structures.…”

Electron Counting and High-Pressure Phase Transformations in Metal Hexaborides

“…Rationalization of solid-state structures through simple concepts, such as electron counting, has the potential to enhance the power of chemical intuition in materials discovery. Understanding chemical properties by counting electrons has been a mainstay of molecular chemistry for over a century, , with successes including the octet and 18-electron rules, Hückel aromaticity, the Wade–Mingos rules, , and superatoms. , Solid-state chemistry also boasts a wealth of electron counting techniques, including the ubiquitous formal oxidation states, the Hume–Rothery rules, the Zintl–Klemm concept, and the 18-n rule for transition-metal intermetallics. , Computational crystal structure prediction (CSP) is a rapidly expanding facet of materials discovery, − and we must augment our intuitive understanding of solid-state chemistry with newly discovered phases by expanding electron counting techniques. One promising opportunity for this is metal-rich bodies MB n , where n > 4, as this family contains boron clusters analogous to gas-phase boranes.…”