Improving Crystal Structure Prediction in Multicomponent Solids

改进多组分固体中的晶体结构预测

• 批准号: 2618475
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2618475
• 关键词: Improving; Crystal; Structure; Prediction; Multicomponent

To identify potential polymorphs, CSP methods search the crystal energy landscape to identify minima in the lattice energy (often used as a proxy for free energy) by optimising the variables which describe molecular conformation and unit cell geometry. For this search to be reliable, it requires that accurate energy models be used to describe the crystal, and that the crystal structure landscape be extensively searched, since it is often the case that polymorph structures are unknown a priori. The desire for reliability is thus at odds with the need to keep the problem tractable within reasonable computational times. To further advance CSP methods, there are two broad areas which should be explored: (1) Refining the energy models used to describe crystals; (2) Improving the efficiency of the search algorithms. Certainly, these two areas are not independent and must be studied in tandem.

为了识别潜在的多晶型，CSP方法搜索晶体能量景观，通过优化描述分子构象和单元胞几何形状的变量来识别晶格能量（通常用作自由能的代理）的最小值。为了使这种搜索可靠，需要使用准确的能量模型来描述晶体，并且需要广泛搜索晶体结构景观，因为通常情况下，多晶型结构是先验未知的。因此，对可靠性的渴望与在合理的计算时间内保持问题可处理性的需要是不一致的。为了进一步推进CSP方法，有两个广泛的领域需要探索：(1)改进用于描述晶体的能量模型；(2)提高搜索算法的效率。当然，这两个领域不是独立的，必须同时进行研究。