עקוב אחר
Albert P. Bartok
Albert P. Bartok
University of Warwick
כתובת אימייל מאומתת בדומיין warwick.ac.uk
כותרת
צוטט על ידי
צוטט על ידי
שנה
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
29472010
On representing chemical environments‏
AP Bartók, R Kondor, G Csányi‏
Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013‏
26202013
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
8972021
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
8002016
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
7422017
G aussian approximation potentials: A brief tutorial introduction‏
AP Bartók, G Csányi‏
International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015‏
6742015
Machine learning a general-purpose interatomic potential for silicon‏
AP Bartók, J Kermode, N Bernstein, G Csányi‏
Physical Review X 8 (4), 041048, 2018‏
6282018
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
4852021
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
4482016
Accuracy and transferability of Gaussian approximation potential models for tungsten‏
WJ Szlachta, AP Bartók, G Csányi‏
Physical Review B 90 (10), 104108, 2014‏
3442014
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
2702018
Regularized SCAN functional‏
AP Bartók, JR Yates‏
The Journal of chemical physics 150 (16), 2019‏
2452019
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
2372013
Incompleteness of atomic structure representations‏
SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti‏
Physical Review Letters 125 (16), 166001, 2020‏
2092020
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
1532022
Efficient sampling of atomic configurational spaces‏
LB Pártay, AP Bartók, G Csányi‏
The Journal of Physical Chemistry B 114 (32), 10502-10512, 2010‏
1252010
Determining pressure-temperature phase diagrams of materials‏
RJN Baldock, LB Pártay, AP Bartók, MC Payne, G Csányi‏
Physical Review B 93 (17), 174108, 2016‏
762016
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
562021
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
532013
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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מאמרים 1–20