References of "Poltavskyi, Igor 50009760"
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See detailMachine Learning Force Fields: Recent Advances and Remaining Challenges
Poltavskyi, Igor UL; Tkatchenko, Alexandre UL

in Journal of Physical Chemistry Letters (2021)

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See detailMachine Learning Force Fields
Oliver T. Unke; Stefan Chmiela; Huziel E. Sauceda et al

in Chemical Reviews (2021)

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See detailAccurate description of nuclear quantum effects with high-order perturbed path integrals (HOPPI)
Poltavskyi, Igor UL; Kapil, Venkat; Ceriotti, Michele et al

in Journal of Chemical Theory and Computation (2020)

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See detailStability of functionalized platform molecules on Au(111)
Jasper-Tönnies, Torben; Poltavskyi, Igor UL; Ulrich, Sandra et al

in Journal of Chemical Physics (2018), 149

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy ... [more ▼]

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy data reveal that >99% of ethyl-TOTA and methyl-TOTA remain intact, whereas 60% of H-TOTA and >99% of propynyl-TOTA and ethynyl-TOTA decompose. The observed tendency toward fragmentation on Au(111) is opposite to the sequence of gas-phase stabilities of the molecules. Although Au(111) is the noblest of all metal surfaces, the binding energies of the decomposition products to Au(111) destabilize the functionalized platforms by 2 to 3.9 eV (190–370 kJ/mol) and even render some of them unstable as revealed by density functional theory calculations. Van der Waals forces are important, as they drive the adsorption of the platform molecules. [less ▲]

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See detailQuantum tunneling of thermal protons through pristine graphene
Poltavskyi, Igor UL; Tkatchenko, Alexandre UL; Mortazavi, Majid et al

in Journal of Chemical Physics (2018), 148(20), 204707

Engineering of atomically thin membranes for hydrogen isotope separation is an actual challenge which has a broad range of applications. Recent experiments [M. Lozada-Hidalgo et al., Science 351, 68 (2016 ... [more ▼]

Engineering of atomically thin membranes for hydrogen isotope separation is an actual challenge which has a broad range of applications. Recent experiments [M. Lozada-Hidalgo et al., Science 351, 68 (2016)] unambiguously demonstrate an order-of-magnitude difference in permeabilities of graphene-based membranes to protons and deuterons at ambient conditions, making such materials promising for novel separation technologies. Here we demonstrate that the permeability mechanism in such systems changes from quantum tunneling for protons to quasi-classical transport for heavier isotopes. Quantum nuclear effects exhibit large temperature and mass dependence, modifying the Arrhenius activation energy and Arrhenius prefactor for protons by more than 0.5 eV and by seven orders of magnitude correspondingly. Our findings not only shed light on the separation process for hydrogen isotope ions passing through pristine graphene but also offer new insights for controlling ion transport mechanisms in nanostructured separation membranes by manipulating the shape of the barrier and transport process conditions. [less ▲]

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See detailPerturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials
Poltavskyi, Igor UL; DiStasio, Robert; Tkatchenko, Alexandre UL

in Journal of Chemical Physics (2018), 148(10), 102325

Nuclear quantum effects (NQE), which include both zero-point motion and tunneling, exhibit quite an impressive range of influence over the equilibrium and dynamical properties of molecules and materials ... [more ▼]

Nuclear quantum effects (NQE), which include both zero-point motion and tunneling, exhibit quite an impressive range of influence over the equilibrium and dynamical properties of molecules and materials. In this work, we extend our recently proposed perturbed path-integral (PPI) approach for modeling NQE in molecular systems [I. Poltavsky and A. Tkatchenko, Chem. Sci. 7, 1368 (2016)], which successfully combines the advantages of thermodynamic perturbation theory with path-integral molecular dynamics (PIMD), in a number of important directions. First, we demonstrate the accuracy, performance, and general applicability of the PPI approach to both molecules and extended (condensed-phase) materials. Second, we derive a series of estimators within the PPI approach to enable calculations of structural properties such as radial distribution functions (RDFs) that exhibit rapid convergence with respect to the number of beads in the PIMD simulation. Finally, we introduce an effective nuclear temperature formalism within the framework of the PPI approach and demonstrate that such effective temperatures can be an extremely useful tool in quantitatively estimating the “quantumness” associated with different degrees of freedom in the system as well as providing a reliable quantitative assessment of the convergence of PIMD simulations. Since the PPI approach only requires the use of standard second-order imaginary-time PIMD simulations, these developments enable one to include a treatment of NQE in equilibrium thermodynamic properties (such as energies, heat capacities, and RDFs) with the accuracy of higher-order methods but at a fraction of the computational cost, thereby enabling first-principles modeling that simultaneously accounts for the quantum mechanical nature of both electrons and nuclei in large-scale molecules and materials. [less ▲]

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See detailMachine learning of accurate energy-conserving molecular force fields
Chmiela, Stefan; Tkatchenko, Alexandre UL; Sauceda, Huziel et al

in Science Advances (2017), 3

Using conservation of energy — a fundamental property of closed classical and quantum mechanical systems — we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate ... [more ▼]

Using conservation of energy — a fundamental property of closed classical and quantum mechanical systems — we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. [less ▲]

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See detailThermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111)
Maurer, Reinhard J.; Liu, Wei; Poltavskyi, Igor UL et al

in Physical Review Letters (2016), 116

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