论文标题
DELACALIZED化学键在基于方形网络的拓扑半学中的作用
The role of delocalized chemical bonding in square-net-based topological semimetals
论文作者
论文摘要
预测材料的反应或特性的原理定义了化学学科。在这项工作中，我们基于原子距离和化学键特征得出化学规则，这些规则可以预测具有方形结构基序的化合物中的拓扑材料。使用这些规则，我们确定了300多种潜在的新拓扑材料。我们表明，简单的化学启发式方法可以成为表征拓扑问题的强大工具。与以前的数据库驱动材料分类相反，我们的方法使我们能够识别具有统计空位的合金，固态或化合物的候选者。尽管以前的材料搜索依赖于密度功能理论，但我们的方法不受此方法的限制，也可以用于发现磁性和统计序列的拓扑半学。
Principles that predict reactions or properties of materials define the discipline of chemistry. In this work we derive chemical rules, based on atomic distances and chemical bond character, which predict topological materials in compounds that feature the structural motif of a square-net. Using these rules we identify over 300 potential new topological materials. We show that simple chemical heuristics can be a powerful tool to characterize topological matter. In contrast to previous database-driven materials categorization our approach allows us to identify candidates that are alloys, solid-solutions, or compounds with statistical vacancies. While previous material searches relied on density functional theory, our approach is not limited by this method and could also be used to discover magnetic and statistically-disordered topological semimetals.
