Search Results - "Poltavsky, Igor"

sGDML: Constructing accurate and data efficient molecular force fields using machine learning by Chmiela, Stefan, Sauceda, Huziel E., Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre

“…We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully…”
Get full text

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules by Kabylda, Adil, Vassilev-Galindo, Valentin, Chmiela, Stefan, Poltavsky, Igor, Tkatchenko, Alexandre

“…Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy…”
Get full text

Machine learning of accurate energy-conserving molecular force fields by Chmiela, Stefan, Tkatchenko, Alexandre, Sauceda, Huziel E, Poltavsky, Igor, Schütt, Kristof T, Müller, Klaus-Robert

“…Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning…”
Get full text

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors by Gallegos, Miguel, Vassilev-Galindo, Valentin, Poltavsky, Igor, Martín Pendás, Ángel, Tkatchenko, Alexandre

“…Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to…”
Get full text

Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules by Kabylda, Adil, Vassilev-Galindo, Valentin, Chmiela, Stefan, Poltavsky, Igor, Tkatchenko, Alexandre

Get full text

Machine Learning Force Fields: Recent Advances and Remaining Challenges by Poltavsky, Igor, Tkatchenko, Alexandre

“…In chemistry and physics, machine learning (ML) methods promise transformative impacts by advancing modeling and improving our understanding of complex…”
Get full text

Plasma excitation dispersion in non-degenerate quantum wire over liquid helium by Antsygina, Tatiana N., Chishko, Konstantin A., Degtyaryov, Igor A., Poltavsky, Igor I., Sokolov, Sviatoslav S., Studart, Nelson

“…We calculate the dispersion laws of plasma oscillations for the quasi-one-dimensional multisubband non-degenerate charge system realized in a conducting…”
Get full text

Modeling quantum nuclei with perturbed path integral molecular dynamics by Poltavsky, Igor, Tkatchenko, Alexandre

“…The quantum nature of nuclear motions plays a vital role in the structure, stability, and thermodynamics of molecules and materials. The standard approach to…”
Get full text

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope by Fonseca, Gregory, Poltavsky, Igor, Tkatchenko, Alexandre

“…As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools…”
Get full text

Machine Learning Force Fields by Unke, Oliver T, Chmiela, Stefan, Sauceda, Huziel E, Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T, Tkatchenko, Alexandre, Müller, Klaus-Robert

“…In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational…”
Get full text

Modeling quantum nuclei with perturbed path integral molecular dynamicsElectronic supplementary information (ESI) available: Heat capacity estimator and first and second-order cumulant expansions of the TI approach. See DOI: 10.1039/c5sc03443d by Poltavsky, Igor, Tkatchenko, Alexandre

“…The quantum nature of nuclear motions plays a vital role in the structure, stability, and thermodynamics of molecules and materials. The standard approach to…”
Get full text

i-PI 2.0: A universal force engine for advanced molecular simulations by Kapil, Venkat, Rossi, Mariana, Marsalek, Ondrej, Petraglia, Riccardo, Litman, Yair, Spura, Thomas, Cheng, Bingqing, Cuzzocrea, Alice, Meißner, Robert H., Wilkins, David M., Helfrecht, Benjamin A., Juda, Przemysław, Bienvenue, Sébastien P., Fang, Wei, Kessler, Jan, Poltavsky, Igor, Vandenbrande, Steven, Wieme, Jelle, Corminboeuf, Clemence, Kühne, Thomas D., Manolopoulos, David E., Markland, Thomas E., Richardson, Jeremy O., Tkatchenko, Alexandre, Tribello, Gareth A., Van Speybroeck, Veronique, Ceriotti, Michele

“…Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for…”
Get full text

Modeling quantum nuclei with perturbed path integral molecular dynamics† †Electronic supplementary information (ESI) available: Heat capacity estimator and first and second-order cumulant expansions of the TI approach. See DOI: 10.1039/c5sc03443d by Poltavsky, Igor, Tkatchenko, Alexandre

“…Here we combine perturbation theory with the Feynman–Kac imaginary-time path integral approach to quantum mechanics for modeling quantum nuclear effects. The…”
Get full text

Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI) by Poltavsky, Igor, Kapil, Venkat, Ceriotti, Michele, Kim, Kwang S, Tkatchenko, Alexandre

“…Imaginary time path-integral (PI) simulations that account for nuclear quantum effects (NQE) beyond the harmonic approximation are increasingly employed…”
Get full text

Tuning Intermolecular Interactions with Nanostructured Environments by Chattopadhyaya, Mausumi, Hermann, Jan, Poltavsky, Igor, Tkatchenko, Alexandre

“…It is known that interactions between molecules may change in the presence of nanostructures. However, the exact mechanisms that dictate the tuning of…”
Get full text

Thermal and Electronic Fluctuations of Flexible Adsorbed Molecules: Azobenzene on Ag(111) by Maurer, Reinhard J, Liu, Wei, Poltavsky, Igor, Stecher, Thomas, Oberhofer, Harald, Reuter, Karsten, Tkatchenko, Alexandre

“…We investigate the thermal and electronic collective fluctuations that contribute to the finite-temperature adsorption properties of flexible adsorbates on…”
Get full text

Modeling molecular ensembles with gradient-domain machine learning force fields by Maldonado, Alex M, Poltavsky, Igor, Vassilev-Galindo, Valentin, Tkatchenko, Alexandre, Keith, John A

“…Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms,…”
Get full text

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope by Fonseca, Gregory, Poltavsky, Igor, Tkatchenko, Alexandre

“…As the sophistication of Machine Learning Force Fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools…”
Get full text

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark by Slootman, Emiel, Poltavsky, Igor, Shinde, Ravindra, Cocomello, Jacopo, Moroni, Saverio, Tkatchenko, Alexandre, Filippi, Claudia

“…J. Chem. Theory Comput. 2024, 20, 6020-6027 Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In…”
Get full text

Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning by Fonseca, Gregory, Poltavsky, Igor, Vassilev-Galindo, Valentin, Tkatchenko, Alexandre

“…The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection…”
Get full text