Detection of topological materials with machine learning

Nikolas Claussen, B. Andrei Bernevig, Nicolas Regnault

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Databases compiled using ab initio and symmetry-based calculations now contain tens of thousands of topological insulators and topological semimetals. This makes the application of modern machine learning methods to topological materials possible. Using gradient boosted trees, we show how to construct a machine learning model which can predict the topology of a given existent material with an accuracy of 90%. Such predictions are orders of magnitude faster than actual ab initio calculations. We use machine learning models to probe how different material properties affect topological features. Notably, we observe that topology is mostly determined by the "coarse-grained" chemical composition and crystal symmetry and depends little on the particular positions of atoms in the crystal lattice. We identify the sources of our model's errors and we discuss approaches to overcome them.

Original languageEnglish (US)
Article number245117
JournalPhysical Review B
Issue number24
StatePublished - Jun 15 2020

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics


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