基于单位胞条件流的分子晶体结构预测
Molecular Crystal Structure Prediction from Conditional Flow on the Unit Cells
浏览论文内容
中文总结 AI 辅助
该研究提出一种基于单位胞条件流的三步生成方法，通过解耦对称性、单位胞和分子排列，在84个系统中成功预测83个晶体结构，支持给定Hall设置和$Z'$的晶体结构预测。
中文摘要 AI 辅助
分子晶体结构由其空间群对称性、单位胞以及不对称单元内的分子排列共同描述。同时预测这三个变量是一项艰巨的任务，因为它将离散的对称性选择与连续空间中的高维搜索混合在一起。为了应对这一挑战，我们使用三步生成过程来解耦这些变量。具体来说，我们训练一个流模型，从分子图、Hall设置和不对称单元中的分子数（$Z'$）学习不变晶格描述符（例如直接和倒易晶格的连续最小值和Selling标量）的条件分布。利用两个顺序的准随机采样过程，我们首先重建满足预测晶格不变量和密度要求的胞参数，然后在给定的对称性和单位胞约束内进行分子堆积搜索。在84个$Z' \le 1$的单组分系统上，我们的方法为83个系统重现了实验匹配；剩余的失败源于力场在保持实验结构方面的局限性。这些结果表明，学习到的胞提议可以有效地支持给定Hall设置和$Z'$的晶体结构预测（CSP），并可能在未来扩展到具有可变对称性和$Z'$设置的完全盲预测。
英文摘要
A molecular crystal structure is jointly described by its space group symmetry, unit cell, and the molecular alignment within the asymmetric unit. Concurrently predicting all three variables is a daunting task, as it mixes discrete symmetry choices with a high-dimensional search in the continuous space. To address this challenge, we decouple these variables using a three-step generation process. Specifically, we train a flow model to learn the conditional distribution of invariant lattice descriptors (e.g. direct- and reciprocal-lattice successive minima and Selling scalars) from a molecular graph, a Hall setting, and the number of molecules in the asymmetric unit ($Z'$). Using a two sequential quasi-random sampling processes, we first reconstruct the cell parameters that match the predicted lattice invariants and density requirements, and then conduct a molecular packing search within the give symmetry and unit cell constraint. On 84 single-component systems with $Z' \le 1$, our approach reproduces experimental matches for 83 systems; the remaining failure stems from force-field limitations in preserving the experimental structure. These results demonstrate that learned cell proposals can effectively support crystal structure prediction (CSP) for a given Hall setting and $Z'$, which may be extended to fully blind prediction with variable symmetry and $Z'$ settings in the future.
发表机构
- University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
- North Carolina Battery Complexity, Autonomous Vehicle and Electrification (BATT CAVE) Research Center（北卡罗来纳电池复杂性、自动驾驶与电气化（BATT CAVE）研究中心）
机构由 AI 辅助整理，请以论文原文为准。
