Albert P. Bartok

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Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
Risi KondorAssociate Professor, The University of ChicagoVerified email at cs.uchicago.edu
James KermodeProfessor of Materials Modelling, School of Engineering, University of WarwickVerified email at warwick.ac.uk
Livia B. PartayAssociate Professor, University of WarwickVerified email at warwick.ac.uk
Volker L. DeringerUniversity of OxfordVerified email at chem.ox.ac.uk
Christoph OrtnerUniversity of British ColumbiaVerified email at math.ubc.ca
Sandip DeBASFVerified email at basf.com
andrás baranyaiInstitute of Chemistry, Eötvös UniversityVerified email at chem.elte.hu
Jonathan YatesDepartment of Materials, University of OxfordVerified email at materials.ox.ac.uk
Matthew CliffeUniversity of NottinghamVerified email at nottingham.ac.uk
Rachel Nathaniel KerberSenior Scientist, Johnson Matthey Technology CentreVerified email at matthey.com
Clare P. GreyYusuf Hamied Department of Chemistry, University of CambridgeVerified email at cam.ac.uk
Ariel Chialvo - ORCID # 0000-0002-6...Oak Ridge National Laboratory (Retired)Verified email at ufl.edu
Carl PoelkingUniversity of CambridgeVerified email at cam.ac.uk
David M WilkinsQueen's University BelfastVerified email at qub.ac.uk
Fred ManbyUniversity of BristolVerified email at bristol.ac.uk
Dario AlfeProfessor of Physics at University College London and Universita' di Napoli Federico IIVerified email at ucl.ac.uk
Sergey N. PozdnyakovÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Kjartan Thor WikfeldtResearcher at RISE Research Institutes of SwedenVerified email at ri.se

Albert P. Bartok

University of Warwick

Verified email at warwick.ac.uk

Theoretical Chemistry Condensed Matter


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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	2617	2010
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013	2300	2013
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	700	2016
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	673	2017
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	654	2021
G aussian approximation potentials: A brief tutorial introduction AP Bartók, G Csányi International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015	610	2015
Machine learning a general-purpose interatomic potential for silicon AP Bartók, J Kermode, N Bernstein, G Csányi Physical Review X 8 (4), 041048, 2018	553	2018
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	421	2016
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	391	2021
Accuracy and transferability of Gaussian approximation potential models for tungsten WJ Szlachta, AP Bartók, G Csányi Physical Review B 90 (10), 104108, 2014	309	2014
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	248	2018
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	226	2013
Regularized SCAN functional AP Bartók, JR Yates The Journal of chemical physics 150 (16), 2019	195	2019
Incompleteness of atomic structure representations SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti Physical Review Letters 125 (16), 166001, 2020	180	2020
Efficient sampling of atomic configurational spaces LB Pártay, AP Bartók, G Csányi The Journal of Physical Chemistry B 114 (32), 10502-10512, 2010	122	2010
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	121	2022
Determining pressure-temperature phase diagrams of materials RJN Baldock, LB Pártay, AP Bartók, MC Payne, G Csányi Physical Review B 93 (17), 174108, 2016	70	2016
First-principles energetics of water clusters and ice: A many-body analysis MJ Gillan, D Alfè, AP Bartók, G Csányi The Journal of chemical physics 139 (24), 2013	52	2013
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	45	2021
Nested sampling for materials: The case of hard spheres LB Pártay, AP Bartók, G Csányi Physical Review E 89 (2), 022302, 2014	45	2014

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