Igor Poltavsky
Igor Poltavsky
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Cited by
Machine learning of accurate energy-conserving molecular force fields
S Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Schütt, KR Müller
Science advances 3 (5), e1603015, 2017
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
i-PI 2.0: A universal force engine for advanced molecular simulations
V Kapil, M Rossi, O Marsalek, R Petraglia, Y Litman, T Spura, B Cheng, ...
Computer Physics Communications 236, 214-223, 2019
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
S Chmiela, HE Sauceda, I Poltavsky, KR Müller, A Tkatchenko
Computer Physics Communications 240, 38-45, 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
The Journal of chemical physics 150 (11), 2019
Machine learning force fields: Recent advances and remaining challenges
I Poltavsky, A Tkatchenko
The journal of physical chemistry letters 12 (28), 6551-6564, 2021
Quantum tunneling of thermal protons through pristine graphene
I Poltavsky, L Zheng, M Mortazavi, A Tkatchenko
The Journal of Chemical Physics 148 (20), 2018
Modeling quantum nuclei with perturbed path integral molecular dynamics
I Poltavsky, A Tkatchenko
Chemical science 7 (2), 1368-1372, 2016
Thermodynamics of low-dimensional spin-1 2 Heisenberg ferromagnets in an external magnetic field within a Green function formalism
TN Antsygina, MI Poltavskaya, II Poltavsky, KA Chishko
Physical Review B 77 (2), 024407, 2008
Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
V Vassilev-Galindo, G Fonseca, I Poltavsky, A Tkatchenko
The Journal of Chemical Physics 154 (9), 2021
Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag (111)
RJ Maurer, W Liu, I Poltavsky, T Stecher, H Oberhofer, K Reuter, ...
Physical review letters 116 (14), 146101, 2016
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
G Fonseca, I Poltavsky, V Vassilev-Galindo, A Tkatchenko
The Journal of Chemical Physics 154 (12), 2021
Thermodynamics of quasi-one-dimensional deposits on carbon nanobundles
TN Antsygina, II Poltavsky, KA Chishko, TA Wilson, OE Vilches
Low temperature physics 31 (12), 1007-1016, 2005
Thermodynamics of low-dimensional adsorption in grooves, on the outer surface, and in interstitials of a closed-ended carbon nanotube bundle
TN Antsygina, II Poltavsky, KA Chishko
Physical Review B 74 (20), 205429, 2006
Construction of machine learned force fields with quantum chemical accuracy: Applications and chemical insights
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
Machine Learning Meets Quantum Physics, 277-307, 2020
Stability of functionalized platform molecules on Au (111)
T Jasper-Tönnies, I Poltavsky, S Ulrich, T Moje, A Tkatchenko, R Herges, ...
The Journal of Chemical Physics 149 (24), 2018
Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials
I Poltavsky, RA DiStasio, A Tkatchenko
The Journal of Chemical Physics 148 (10), 2018
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
A Kabylda, V Vassilev-Galindo, S Chmiela, I Poltavsky, A Tkatchenko
nature communications 14 (1), 3562, 2023
Lattice dynamics and heat capacity of a two-dimensional monoatomic crystal on a substrate
TN Antsygina, II Poltavsky, MI Poltavskaya, KA Chishko
Low Temperature Physics 28 (6), 442-451, 2002
Accurate description of nuclear quantum effects with high-order perturbed path integrals (HOPPI)
I Poltavsky, V Kapil, M Ceriotti, KS Kim, A Tkatchenko
Journal of chemical theory and computation 16 (2), 1128-1135, 2020
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