Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
This work addresses the problem of efficiently identifying topological materials for researchers in condensed matter physics and materials science, offering a novel approach that complements existing methods.
The researchers tackled the challenge of discovering topological materials by developing a machine-learned heuristic rule called topogivity, which uses only chemical formulas to predict topological properties with high accuracy, leading to the discovery of new materials not identifiable by traditional symmetry-based methods.
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.