Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Poltavsky AN"'
Autor:
Slootman, Emiel, Poltavsky, Igor, Shinde, Ravindra, Cocomello, Jacopo, Moroni, Saverio, Tkatchenko, Alexandre, Filippi, Claudia
Publikováno v:
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 this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either
Externí odkaz:
http://arxiv.org/abs/2404.09755
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 to properly analyze and assess the practical performance of MLFFs. To go beyond average er
Externí odkaz:
http://arxiv.org/abs/2308.06871
Autor:
Miguel Gallegos, Valentin Vassilev-Galindo, Igor Poltavsky, Ángel Martín Pendás, Alexandre Tkatchenko
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but
Externí odkaz:
https://doaj.org/article/32319b0753f74842bfed0295b2e3da9a
Autor:
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 but at a small fraction of the computational cost. However, several challenges remain to b
Externí odkaz:
http://arxiv.org/abs/2209.03985
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 determines the applicability of the MLFF model for predictive molecular simulations. However, most
Externí odkaz:
http://arxiv.org/abs/2103.01674
Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields com
Externí odkaz:
http://arxiv.org/abs/2103.01103
Autor:
Unke, Oliver T., Chmiela, Stefan, Sauceda, Huziel E., Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T., Tkatchenko, Alexandre, Müller, Klaus-Robert
Publikováno v:
Chem. Rev. 2021, 121, 16, 10142-10186
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications
Externí odkaz:
http://arxiv.org/abs/2010.07067
Autor:
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges re
Externí odkaz:
https://doaj.org/article/4aaf3e78e81044debdb27aae42abcef1
Autor:
Sauceda, Huziel E., Chmiela, Stefan, Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of
Externí odkaz:
http://arxiv.org/abs/1909.08565
Publikováno v:
Измерение, мониторинг, управление, контроль, Iss 3 (2023)
Background. The purpose of the work is to investigate models and one of the approaches for monitoring parameters at the early stages of testing with the construction of algorithms for the control system of photo and video cameras of optoelectronic de
Externí odkaz:
https://doaj.org/article/b142e18fa15e4dfc86d501196dd2b789