论文标题
使用粉末衍射强度用于晶体结构预测的噪声数据同化方法
A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diffraction Intensity
论文作者
论文摘要
长期以来，给定的化学成分的晶体结构预测一直是凝结的科学的挑战。我们最近表明，即使使用模拟退火，实验性粉末X射线衍射（XRD）数据对于晶体结构搜索也很有帮助，即使它们不足以确定结构（N。在该方法中，通过向物理势能添加惩罚函数，将XRD数据吸收到模拟中，在那里我们使用了由实验性和模拟衍射角之间的差异定义的结晶度型惩罚函数。为了提高成功率和噪声鲁棒性，我们引入了具有明显实验噪声的XRD数据的相关性型惩罚函数。我们将新的罚款功能应用于SIO $ _2 $ coesite和$ε$ -ZN（OH）$ _ 2 $，以确定其在数据同化方法中的有效性。
Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search using simulated annealing, even when they are insufficient for structure determination by themselves (N. Tsujimoto et al., Phys. Rev. Materials 2, 053801 (2018)). In the method, the XRD data are assimilated into the simulation by adding a penalty function to the physical potential energy, where we used a crystallinity-type penalty function defined by the difference between experimental and simulated diffraction angles. To improve the success rate and noise robustness, we introduce a correlation-coefficient-type penalty function adaptable to XRD data with significant experimental noise. We apply the new penalty function to SiO$_2$ coesite and $ε$-Zn(OH)$_2$ to determine its effectiveness in the data assimilation method.
