Machine learning sparse tight-binding parameters for defects

Christoph Schattauer, Milica Todorović, Kunal Ghosh, Patrick Rinke, Florian Libisch*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
124 Downloads (Pure)


We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure, local density of states, transport and level spacing simulations for two common defects in single layer graphene. Our machine learning approach achieves results comparable to maximally localized Wannier functions (i.e., DFT accuracy) without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time. It is general and can be applied to a wide range of other materials, enabling accurate large-scale simulations of material properties in the presence of different defects.

Original languageEnglish
Article number116
Pages (from-to)1-11
Number of pages11
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 20 May 2022
MoE publication typeA1 Journal article-refereed


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