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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
27732010
On representing chemical environments
AP Bartók, R Kondor, G Csányi
Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013
24552013
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
7612021
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
7452016
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
7092017
G aussian approximation potentials: A brief tutorial introduction
AP Bartók, G Csányi
International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015
6392015
Machine learning a general-purpose interatomic potential for silicon
AP Bartók, J Kermode, N Bernstein, G Csányi
Physical Review X 8 (4), 041048, 2018
5882018
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
4362021
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
4332016
Accuracy and transferability of Gaussian approximation potential models for tungsten
WJ Szlachta, AP Bartók, G Csányi
Physical Review B 90 (10), 104108, 2014
3242014
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
2592018
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—Condensed Matter and Materials Physics 88 (5), 054104, 2013
2342013
Regularized SCAN functional
AP Bartók, JR Yates
The Journal of chemical physics 150 (16), 2019
2172019
Incompleteness of atomic structure representations
SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti
Physical Review Letters 125 (16), 166001, 2020
1952020
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
1312022
Efficient sampling of atomic configurational spaces
LB Pártay, AP Bartók, G Csányi
The Journal of Physical Chemistry B 114 (32), 10502-10512, 2010
1242010
Determining pressure-temperature phase diagrams of materials
RJN Baldock, LB Pártay, AP Bartók, MC Payne, G Csányi
Physical Review B 93 (17), 174108, 2016
742016
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
532013
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
492021
Nested sampling for materials: The case of hard spheres
LB Pártay, AP Bartók, G Csányi
Physical Review E 89 (2), 022302, 2014
472014
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Articles 1–20