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 strategy 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.
翻译:地形学材料具有非常规的电子特性,因此对基础科学和下一代技术应用都具有吸引力。大多数目前已知的地形学材料都是采用对量子波函数进行对称分析的方法发现的。在这里,我们利用机器学习来开发一种简单到使用的超温化学规则,该规则以高精度诊断一种材料是否仅使用化学公式就具有地形学性质。这种休眠学规则基于一种概念,即我们给每个元素下调定调高,一种机器学得来的数字值,可以随意捕捉其形成地形学材料的倾向。我们接下来执行一种高通量战略,在对量子波子波子进行对量子波子进行对称分析的基础上发现地形学材料,然后进行初步鉴定。这样,我们发现新的地形学材料,不能使用对称指标进行分辨,包括一些可能对实验观测有希望的材料。