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
Jonathan YatesDepartment of Materials, University of OxfordVerified email at materials.ox.ac.uk
andrás baranyaiInstitute of Chemistry, Eötvös UniversityVerified email at chem.elte.hu
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
David M WilkinsQueen's University BelfastVerified email at qub.ac.uk
Carl PoelkingUniversity of CambridgeVerified email at cam.ac.uk
Fred ManbyIambic TherapeuticsVerified email at iambic.ai
Sergey N. PozdnyakovÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Félix MusilCuspAIVerified email at cusp.ai
Andrea GrisafiCNRS researcher, Sorbonne Université, ParisVerified email at sorbonne-universite.fr

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	2921	2010
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013	2598	2013
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	865	2021
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	785	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	734	2017
G aussian approximation potentials: A brief tutorial introduction AP Bartók, G Csányi International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015	669	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	625	2018
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	477	2021
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	442	2016
Accuracy and transferability of Gaussian approximation potential models for tungsten WJ Szlachta, AP Bartók, G Csányi Physical Review B 90 (10), 104108, 2014	335	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	270	2018
Regularized SCAN functional AP Bartók, JR Yates The Journal of chemical physics 150 (16), 2019	239	2019
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	239	2013
Incompleteness of atomic structure representations SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti Physical Review Letters 125 (16), 166001, 2020	206	2020
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	149	2022
Efficient sampling of atomic configurational spaces LB Pártay, AP Bartók, G Csányi The Journal of Physical Chemistry B 114 (32), 10502-10512, 2010	126	2010
Determining pressure-temperature phase diagrams of materials RJN Baldock, LB Pártay, AP Bartók, MC Payne, G Csányi Physical Review B 93 (17), 174108, 2016	76	2016
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	55	2021
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	54	2013
Nested sampling for materials: The case of hard spheres LB Pártay, AP Bartók, G Csányi Physical Review E 89 (2), 022302, 2014	48	2014

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