论文标题
基于Voronoi的相似性距离
Voronoi-based similarity distances between arbitrary crystal lattices
论文作者
论文摘要
本文开发了一种新的连续方法，可以在理想晶体的周期性晶格之间建立相似性。需要量化晶体结构之间的相似性，以实质上加快晶体结构预测，因为对晶体结构的许多目标性能的预测在计算上是缓慢的，并且对于许多几乎相同的模拟结构而言，基本上重复了晶体结构的预测。在所有刚体运动下，晶体结构任意周期晶格之间的距离是不变的，在原子扰动下满足了公理和连续性。上述属性使这些距离成为聚类和可视化晶体结构数据集的理想工具。所有结论均通过对2017年自然论文中报道的真实和模拟晶体结构的实验进行了严格证明和合理，并“使用能量结构 - 功能函数图发现功能材料”。
This paper develops a new continuous approach to a similarity between periodic lattices of ideal crystals. Quantifying a similarity between crystal structures is needed to substantially speed up the Crystal Structure Prediction, because the prediction of many target properties of crystal structures is computationally slow and is essentially repeated for many nearly identical simulated structures. The proposed distances between arbitrary periodic lattices of crystal structures are invariant under all rigid motions, satisfy the metric axioms and continuity under atomic perturbations. The above properties make these distances ideal tools for clustering and visualizing large datasets of crystal structures. All the conclusions are rigorously proved and justified by experiments on real and simulated crystal structures reported in the Nature 2017 paper "Functional materials discovery using energy-structure-function maps".
