拓扑性：一种用于发现拓扑材料的机器学习化学规则
Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
摘要：
拓扑材料呈现出非常规的电子特性，使它们对基础科学和下一代技术应用都具有吸引力。大多数目前已知的拓扑材料都是使用涉及量子波函数的基于量子力学的分析的方法发现的。在这里，我们使用机器学习来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地诊断材料是否具有拓扑结构。这个启发式规则是基于一个概念，我们称之为拓扑性，一个机器学习的数值为每个元素，松散地捕捉其形成拓扑材料的趋势。接下来，我们实现了一个高吞吐量的程序，发现拓扑材料的启发式拓扑规则预测的基础上从头验证。通过这种方式，我们发现了新的拓扑材料，这些材料不能使用对称性指标进行诊断，其中包括几种可能有希望用于实验观察的材料。
英文摘要：
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 wavefunction. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
