Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
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DOI：
10.1021/acs.nanolett.2c03307
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发表时间：
2022-02
期刊：
影响因子：
10.8
通讯作者：
Andrew Ma;Yang Zhang;Thomas Christensen;H. Po;Li Jing;L. Fu;M. Soljavci'c
中科院分区：
文献类型：
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作者：
Andrew Ma;Yang Zhang;Thomas Christensen;H. Po;Li Jing;L. Fu;M. Soljavci'c
拓扑材料呈现出非常规的电子特性，这使其对基础科学和下一代技术应用都具有吸引力。目前已知的大多数拓扑材料都是使用涉及基于对称性的量子波函数分析的方法发现的。在这里，我们使用机器学习开发一种简单易用的启发式化学规则，仅使用其化学式就可以高精度诊断材料是否是拓扑的。这种启发式规则基于我们称之为拓扑性的概念，这是每个元素的机器学习数值，可以松散地捕获其形成拓扑材料的趋势。接下来，我们实施一个高通量程序，基于启发式拓扑规则预测和从头验证来发现拓扑材料。通过这种方式，我们发现了无法使用对称性指标来诊断的新拓扑材料，其中包括几种可能有希望用于实验观察的拓扑材料。
Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wave function. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.