Detection of Topological Materials with Machine Learning

Autor:	Claussen, Nikolas, Bernevig, B. Andrei, Regnault, Nicolas
Rok vydání:	2019
Předmět:	Condensed Matter - Materials Science Physics - Computational Physics
Zdroj:	Phys. Rev. B 101, 245117 (2020)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevB.101.245117
Popis:	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. Through extensive testing of different models we determine which properties help detect topological materials. We identify the sources of our model's errors and we discuss approaches to overcome them. Comment: 34 pages, 7 figures. The ML model is available online at https://www.topologicalquantumchemistry.com/mltqc. Version 2 includes corrections after peer review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.10161 Zobrazit plný text záznamu View this record from Arxiv