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Albert P. Bartok
Albert P. Bartok
Verified email at warwick.ac.uk
Title
Cited by
Cited by
Year
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
AP Bartók, MC Payne, R Kondor, G Csányi
Physical review letters 104 (13), 136403, 2010
25512010
On representing chemical environments
AP Bartók, R Kondor, G Csányi
Physical Review B 87 (18), 184115, 2013
22422013
Comparing molecules and solids across structural and alchemical space
S De, AP Bartók, G Csányi, M Ceriotti
Physical Chemistry Chemical Physics 18 (20), 13754-13769, 2016
6922016
Machine learning unifies the modeling of materials and molecules
AP Bartók, S De, C Poelking, N Bernstein, JR Kermode, G Csányi, ...
Science advances 3 (12), e1701816, 2017
6562017
Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein, DM Wilkins, M Ceriotti, G Csányi
Chemical Reviews 121 (16), 10073-10141, 2021
6122021
G aussian approximation potentials: A brief tutorial introduction
AP Bartók, G Csányi
International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015
5952015
Machine learning a general-purpose interatomic potential for silicon
AP Bartók, J Kermode, N Bernstein, G Csányi
Physical Review X 8 (4), 041048, 2018
5452018
Modeling molecular interactions in water: From pairwise to many-body potential energy functions
GA Cisneros, KT Wikfeldt, L Ojamäe, J Lu, Y Xu, H Torabifard, AP Bartók, ...
Chemical reviews 116 (13), 7501-7528, 2016
4152016
Physics-inspired structural representations for molecules and materials
F Musil, A Grisafi, AP Bartók, C Ortner, G Csányi, M Ceriotti
Chemical Reviews 121 (16), 9759-9815, 2021
3762021
Accuracy and transferability of Gaussian approximation potential models for tungsten
WJ Szlachta, AP Bartók, G Csányi
Physical Review B 90 (10), 104108, 2014
3072014
Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics
VL Deringer, N Bernstein, AP Bartók, MJ Cliffe, RN Kerber, LE Marbella, ...
The journal of physical chemistry letters 9 (11), 2879-2885, 2018
2412018
Machine-learning approach for one-and two-body corrections to density functional theory: Applications to molecular and condensed water
AP Bartók, MJ Gillan, FR Manby, G Csányi
Physical Review B 88 (5), 054104, 2013
2272013
Regularized SCAN functional
AP Bartók, JR Yates
The Journal of chemical physics 150 (16), 161101, 2019
1842019
Incompleteness of atomic structure representations
SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti
Physical Review Letters 125 (16), 166001, 2020
1762020
Efficient sampling of atomic configurational spaces
LB Pártay, AP Bartók, G Csányi
The Journal of Physical Chemistry B 114 (32), 10502-10512, 2010
1202010
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
1092022
Determining pressure-temperature phase diagrams of materials
RJN Baldock, LB Pártay, AP Bartók, MC Payne, G Csányi
Physical Review B 93 (17), 174108, 2016
662016
First-principles energetics of water clusters and ice: A many-body analysis
MJ Gillan, D Alfč, AP Bartók, G Csányi
The Journal of chemical physics 139 (24), 2013
512013
Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of
H Muhli, X Chen, AP Bartók, P Hernández-León, G Csányi, T Ala-Nissila, ...
Physical Review B 104 (5), 054106, 2021
452021
Computer simulation of the 13 crystalline phases of ice
A Baranyai, A Bartók, AA Chialvo
The Journal of chemical physics 123 (5), 2005
452005
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Articles 1–20