Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Théo Jaffrelot Inizan"'
1
Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including Long-range Effects
Autor: Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, Pengyu Ren, Hatice Gökcan, Olexandr Isayev, Louis Lagardère, Jean-Philip Piquemal
Publikováno v: Chemical Science
Chemical Science, 2023, ⟨10.1039/D2SC04815A⟩
International audience; Deep-HP is a scalable extension of the Tinker-HP multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders of magnitu
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3204c37f45afbf96b891d166d560cc0
https://hal.science/hal-03738403
Zobrazit plný text záznamu
2
An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems
Autor: Frédéric Célerse, Théo Jaffrelot Inizan, Louis Lagardère, Olivier Adjoua, Pierre Monmarché, Yinglong Miao, Etienne Derat, Jean-Philip Piquemal
Publikováno v: Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation, 2022, 18 (2), pp.968-977. ⟨10.1021/acs.jctc.1c01024⟩
We introduce a novel multilevel enhanced sampling strategy grounded on Gaussian-accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the ne
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15687d14160af574ed9c81bf22929bb9
https://hal.science/hal-03360596
Zobrazit plný text záznamu
3
Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory
Autor: Pier Paolo Poier, Théo Jaffrelot Inizan, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal
Publikováno v: Journal of Physical Chemistry Letters
Journal of Physical Chemistry Letters, 2022, 13 (19), pp.4381-4388. ⟨10.1021/acs.jpclett.2c00936⟩
International audience; Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirs
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::175f74cb9826f9aabd221ec4fb84524e
Zobrazit plný text záznamu
4
Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials
Autor: Thomas Plé, Nastasia Mauger, Olivier Adjoua, Théo Jaffrelot Inizan, Louis Lagardère, Simon Huppert, Jean-Philip Piquemal
Publikováno v: Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation, 2023, ⟨10.1021/acs.jctc.2c01233⟩
We report the implementation of a multi-CPU and multi-GPU massively parallel platform dedicated to the explicit inclusion of nuclear quantum effects (NQEs) in the Tinker-HP molecular dynamics (MD) package. The platform, denoted Quantum-HP, exploits t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ab761d06976e5b1f44ee4c592521672
Zobrazit plný text záznamu
5
High-Resolution Mining of SARS-CoV-2 Main Protease Conformational Space: Supercomputer-Driven Unsupervised Adaptive Sampling
Autor: Pengyu Ren, Chengwen Liu, Louis Lagardère, Théo Jaffrelot Inizan, Dina El Ahdab, Frédéric Célerse, Luc-Henri Jolly, Matthieu Montes, Olivier Adjoua, Pierre Monmarché, Nathalie Lagarde, Jean-Philip Piquemal
Publikováno v: Chemical Science
Chemical Science, The Royal Society of Chemistry, 2021, 12 (13), pp.4889-4907. ⟨10.1039/D1SC00145K⟩
Chemical Science, 2021, 12 (13), pp.4889-4907. ⟨10.1039/D1SC00145K⟩
We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b11f49814c050122eb957706f0e41803
https://doi.org/10.26434/chemrxiv.13003166.v6
Zobrazit plný text záznamu
7
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems
Autor: Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Zhi Wang, Thibault Very, Isabelle Dupays, Théo Jaffrelot Inizan, Frederic Célerse, Pengyu Ren, Jay Ponder, Jean-Philip Piquemal
Publikováno v: Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation, American Chemical Society, 2021, 17 (4), pp.2034-2053. ⟨10.1021/acs.jctc.0c01164⟩
HAL
ArXiv
Journal of Chemical Theory and Computation, 2021, 17 (4), pp.2034-2053. ⟨10.1021/acs.jctc.0c01164⟩
International audience; We present the extension of the Tinker-HP package (Lagardere, et al. Chem. Sci. 2018, 9, 956−972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body fo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07ce9e1b0c92c1cfc3b81eee2cbc5516
https://hal.archives-ouvertes.fr/hal-02943337
Zobrazit plný text záznamu
8
Binding and 2D organization of arginine on Cu(1 1 0)
Autor: Roberta Totani, Théo Jaffrelot Inizan, Vincent Humblot, Claire-Marie Pradier, Christophe Méthivier, D. Costa
Publikováno v: Applied Surface Science
Applied Surface Science, Elsevier, 2019, pp.144865. ⟨10.1016/j.apsusc.2019.144865⟩
Applied Surface Science, Elsevier, 2020, 509, pp.144865. ⟨10.1016/j.apsusc.2019.144865⟩
In this work we present a detailed surface science characterization of L-arginine adsorption on the Cu(1 1 0) surface in a submonolayer regime. Arginine (ARG) is one of the main components of the tripeptide RGD (arginine-glycine-aspartic acid) common
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bf760aef316dffd761efbf49f769d04
https://hal.archives-ouvertes.fr/hal-02411672
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje