Representing individual electronic states for machine learning GW band structures of 2D materials

Nikolaj Rørbæk Knøsgaard*, Kristian Sommer Thygesen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

46 Downloads (Pure)


Choosing optimal representation methods of atomic and electronic structures is essential when machine learning properties of materials. We address the problem of representing quantum states of electrons in a solid for the purpose of machine leaning state-specific electronic properties. Specifically, we construct a fingerprint based on energy decomposed operator matrix elements (ENDOME) and radially decomposed projected density of states (RAD-PDOS), which are both obtainable from a standard density functional theory (DFT) calculation. Using such fingerprints we train a gradient boosting model on a set of 46k G0W0 quasiparticle energies. The resulting model predicts the self-energy correction of states in materials not seen by the model with a mean absolute error of 0.14 eV. By including the material’s calculated dielectric constant in the fingerprint the error can be further reduced by 30%, which we find is due to an enhanced ability to learn the correlation/screening part of the self-energy. Our work paves the way for accurate estimates of quasiparticle band structures at the cost of a standard DFT calculation.

Original languageEnglish
Article number468
JournalNature Communications
Issue number1
Number of pages10
Publication statusPublished - 2022


Dive into the research topics of 'Representing individual electronic states for machine learning GW band structures of 2D materials'. Together they form a unique fingerprint.

Cite this