Representing individual electronic states for machine learning GW band structures of 2D materials
Published 2022 View Full Article
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Title
Representing individual electronic states for machine learning GW band structures of 2D materials
Authors
Keywords
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Journal
Nature Communications
Volume 13, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-02-03
DOI
10.1038/s41467-022-28122-0
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Related references
Note: Only part of the references are listed.- Towards fully automated GW band structure calculations: What we can learn from 60.000 self-energy evaluations
- (2021) Asbjørn Rasmussen et al. npj Computational Materials
- Recent progress of the computational 2D materials database (C2DB)
- (2021) Morten Niklas Gjerding et al. 2D Materials
- The GW Compendium: A Practical Guide to Theoretical Photoemission Spectroscopy
- (2019) Dorothea Golze et al. Frontiers in Chemistry
- Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction
- (2019) Jiechun Liang et al. Journal of Physical Chemistry Letters
- Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
- (2018) Arunkumar Chitteth Rajan et al. CHEMISTRY OF MATERIALS
- Machine learning-based screening of complex molecules for polymer solar cells
- (2018) Peter Bjørn Jørgensen et al. JOURNAL OF CHEMICAL PHYSICS
- Predicting the Band Gaps of Inorganic Solids by Machine Learning
- (2018) Ya Zhuo et al. Journal of Physical Chemistry Letters
- The computational 2D materials database: High-throughput modeling and discovery of atomically thin crystals
- (2018) Sten Haastrup et al. 2D Materials
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Big Data of Materials Science: Critical Role of the Descriptor
- (2015) Luca M. Ghiringhelli et al. PHYSICAL REVIEW LETTERS
- Quasiparticle GW calculations for solids, molecules, and two-dimensional materials
- (2013) Falco Hüser et al. PHYSICAL REVIEW B
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Polarization-induced renormalization of molecular levels at metallic and semiconducting surfaces
- (2009) J. M. Garcia-Lastra et al. PHYSICAL REVIEW B
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