Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials

Abstract

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.

arXiv commentMain text: 6 pages, 3 figures; supplementary materials: 43 pages, 62 figures, 5 tables

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• Topological photonics: fundamental concepts, recent developments, and future directions
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• Multimodal Foundation Models for Material Property Prediction and Discovery
• Discovering two-dimensional magnetic topological insulators by machine learning
• Predicting Many Crystal Properties via an Adaptive Transformer-based Framework
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• Accelerated Discovery of Topological Conductors for Nanoscale Interconnects