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arXiv 2209.05850cond-mat.mtrl-sci
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一种抗噪声的粉末衍射强度晶体结构预测数据同化方法

A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diffraction Intensity

Seiji Yoshikawa, Ryuhei Sato, Ryosuke Akashi, Synge Todo, Shinji Tsuneyuki

摘要:

在凝聚态科学中,对给定化学组成的晶体结构进行预测一直是一个挑战。我们最近已经表明,实验粉末X射线衍射(XRD)数据有助于使用模拟退火进行晶体结构搜索,即使它们本身不足以确定结构(N。Tsujimoto等人,Phys. Rev. Materials 2,053801(2018))。在该方法中,XRD数据被同化到模拟中,通过添加一个惩罚函数的物理势能,其中我们使用的堆叠型惩罚函数定义的实验和模拟的衍射角之间的差异。为了提高成功率和噪声鲁棒性,我们引入了一个相关系数类型的惩罚函数,适用于XRD数据与显着的实验噪声。我们将新的罚函数应用于SiO $_2$柯石英和$ε$ -Zn(OH)$_2$,以确定它在数据同化方法中的有效性.

英文摘要:

Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search using simulated annealing, even when they are insufficient for structure determination by themselves (N. Tsujimoto et al., Phys. Rev. Materials 2, 053801 (2018)). In the method, the XRD data are assimilated into the simulation by adding a penalty function to the physical potential energy, where we used a crystallinity-type penalty function defined by the difference between experimental and simulated diffraction angles. To improve the success rate and noise robustness, we introduce a correlation-coefficient-type penalty function adaptable to XRD data with significant experimental noise. We apply the new penalty function to SiO$_2$ coesite and $ε$-Zn(OH)$_2$ to determine its effectiveness in the data assimilation method.

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