Artikel mit Open-Access-Mandaten - Kieron BurkeWeitere Informationen
Nicht verfügbar: 2
Halogen and chalcogen binding dominated by density-driven errors
Y Kim, S Song, E Sim, K Burke
The journal of physical chemistry letters 10 (2), 295-301, 2018
Mandate: US National Science Foundation
Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives
JC Snyder, M Rupp, KR Müller, K Burke
International Journal of Quantum Chemistry 115 (16), 1102-1114, 2015
Mandate: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung …
Verfügbar: 79
Bypassing the Kohn-Sham equations with machine learning
F Brockherde, L Vogt, L Li, ME Tuckerman, K Burke, KR Müller
Nature communications 8 (1), 872, 2017
Mandate: US National Science Foundation, US Department of Defense
Finding density functionals with machine learning
JC Snyder, M Rupp, K Hansen, KR Müller, K Burke
Physical review letters 108 (25), 253002, 2012
Mandate: Deutsche Forschungsgemeinschaft
Understanding band gaps of solids in generalized Kohn–Sham theory
JP Perdew, W Yang, K Burke, Z Yang, EKU Gross, M Scheffler, ...
Proceedings of the national academy of sciences 114 (11), 2801-2806, 2017
Mandate: US Department of Energy, Deutsche Forschungsgemeinschaft
Retrospective on a decade of machine learning for chemical discovery
OA von Lilienfeld, K Burke
Nature communications 11 (1), 4895, 2020
Mandate: US National Science Foundation, Schweizerischer Nationalfonds zur Förderung …
Quantum chemical accuracy from density functional approximations via machine learning
M Bogojeski, L Vogt-Maranto, ME Tuckerman, KR Müller, K Burke
Nature communications 11 (1), 5223, 2020
Mandate: US National Science Foundation, US Department of Defense, Deutsche …
DFT: A theory full of holes?
A Pribram-Jones, DA Gross, K Burke
Annual review of physical chemistry 66 (1), 283-304, 2015
Mandate: US Department of Energy
Zero-bias molecular electronics: Exchange-correlation corrections to Landauer's formula
M Koentopp, K Burke, F Evers
Physical Review B—Condensed Matter and Materials Physics 73 (12), 121403, 2006
Mandate: Deutsche Forschungsgemeinschaft
Understanding machine‐learned density functionals
L Li, JC Snyder, IM Pelaschier, J Huang, UN Niranjan, P Duncan, M Rupp, ...
International Journal of Quantum Chemistry 116 (11), 819-833, 2016
Mandate: US National Science Foundation, Schweizerischer Nationalfonds zur Förderung …
Density functional calculations of nanoscale conductance
M Koentopp, C Chang, K Burke, R Car
Journal of Physics: Condensed Matter 20 (8), 083203, 2008
Mandate: Deutsche Forschungsgemeinschaft
Kohn-Sham equations as regularizer: Building prior knowledge into machine-learned physics
L Li, S Hoyer, R Pederson, R Sun, ED Cubuk, P Riley, K Burke
Physical review letters 126 (3), 036401, 2021
Mandate: US National Science Foundation, US Department of Energy
Pure density functional for strong correlation and the thermodynamic limit from machine learning
L Li, TE Baker, SR White, K Burke
Physical Review B 94 (24), 245129, 2016
Mandate: US Department of Energy
The Hubbard dimer: a density functional case study of a many-body problem
DJ Carrascal, J Ferrer, JC Smith, K Burke
Journal of Physics: Condensed Matter 27 (39), 393001, 2015
Mandate: US Department of Energy, European Commission, Government of Spain
Orbital-free bond breaking via machine learning
JC Snyder, M Rupp, K Hansen, L Blooston, KR Müller, K Burke
The Journal of chemical physics 139 (22), 2013
Mandate: Deutsche Forschungsgemeinschaft, European Commission
The importance of being inconsistent
A Wasserman, J Nafziger, K Jiang, MC Kim, E Sim, K Burke
Annual review of physical chemistry 68 (1), 555-581, 2017
Mandate: US National Science Foundation, US Department of Energy
Roadmap on machine learning in electronic structure
HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ...
Electronic Structure 4 (2), 023004, 2022
Mandate: US Department of Energy, Natural Sciences and Engineering Research Council …
Understanding kernel ridge regression: Common behaviors from simple functions to density functionals
K Vu, JC Snyder, L Li, M Rupp, BF Chen, T Khelif, KR Müller, K Burke
International Journal of Quantum Chemistry 115 (16), 1115-1128, 2015
Mandate: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung …
Quantifying density errors in DFT
E Sim, S Song, K Burke
The journal of physical chemistry letters 9 (22), 6385-6392, 2018
Mandate: US National Science Foundation
Density functional analysis: The theory of density-corrected DFT
S Vuckovic, S Song, J Kozlowski, E Sim, K Burke
Journal of chemical theory and computation 15 (12), 6636-6646, 2019
Mandate: US National Science Foundation, Netherlands Organisation for Scientific Research
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