State Key Laboratory of Superhard Materials & International Center of Computational Method and Software, College of Physics, Jilin University, Changchun 130012, China.
International Center of Future Science, Jilin University, Changchun 130012, China.
Acc Chem Res. 2022 Aug 2;55(15):2068-2076. doi: 10.1021/acs.accounts.2c00243. Epub 2022 Jul 19.
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.
晶体结构预测（CSP）近年来已成为物理学、化学、材料科学和地球科学等多个科学领域研究的一个主要主题。这里的核心任务是在与相关感兴趣晶体的巨大结构配置空间相关的势能表面（PES）上找到全局能量最小值，这对高效可靠的计算实现提出了巨大的挑战。近年来 CSP 算法的发展取得了相当大的进展，带来了许多方法上的进步和成功的应用，开创了一个新的范式，即计算研究在寻找新型材料形式和特性方面发挥着主导的预测作用，而这些反过来又为指导实验合成和表征提供了关键的见解。在本述评中，我们首先简要总结了各种 CSP 方法的主要进展，重点介绍了探索 PES 的总体基础及其对 CSP 的影响。然后，我们以我们开发的 CALYPSO 方法为例，重点介绍 CSP 方法学中最突出问题的当前状况。我们还概述了 CALYPSO 的基本理论和主要特点，并强调了 CALYPSO 方法学中的几个有效策略，以在探索和利用之间取得良好的平衡。我们展示了两个理论驱动的高温超导超氢化物和一组典型化合物的发现的示例案例，其中 CSP 在指导实验合成以发现新材料方面发挥了重要作用。最后，我们通过提供对进一步 CSP 研究的主要未解决问题和有前途的机会的看法来结束。
