Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Lagardère , Louis"'
Autor:
Gouraud, Nicolaï, Lagardère, Louis, Adjoua, Olivier, Plé, Thomas, Monmarché, Pierre, Piquemal, Jean-Philip
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where t
Externí odkaz:
http://arxiv.org/abs/2412.15073
Publikováno v:
Journal of Chemical Physics, 2024, 161, 042502
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years, numerous advanc
Externí odkaz:
http://arxiv.org/abs/2405.01491
Autor:
Lagardère, Louis, Maurin, Lise, Adjoua, Olivier, Hage, Krystel El, Monmarché, Pierre, Piquemal, Jean-Philip, Hénin, Jérôme
Publikováno v:
Journal of Chemical Theory and Computation 20 (2024) 4481-4498
We introduce an efficient and robust method to compute alchemical free energy differences, resulting from the application of multiple walker Adaptive Biasing Force (ABF) in conjunction with strongly damped Langevin $\lambda$-dynamics. Unbiased alchem
Externí odkaz:
http://arxiv.org/abs/2307.08006
Publikováno v:
J. Chem. Phys. 159, 154109 (2023)
We derive and implement an alternative formulation of the Stochastic Lanczos algorithm to be employed in connection with the Many-Body Dispersion model (MBD). Indeed, this formulation, which is only possible due to the Stochastic Lanczos' reliance on
Externí odkaz:
http://arxiv.org/abs/2307.02278
Publikováno v:
Chemical Science, 2023, 14, 12554-12569
We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-mo
Externí odkaz:
http://arxiv.org/abs/2301.08734
Publikováno v:
J. Chem. Theory Comput. 2023, 19, 10, 2887-2905
To evaluate electrostatics interactions, Molecular dynamics (MD) simulations rely on Particle Mesh Ewald (PME), an O(Nlog(N)) algorithm that uses Fast Fourier Transforms (FFTs) or, alternatively, on O(N) Fast Multipole Methods (FMM) approaches. Howev
Externí odkaz:
http://arxiv.org/abs/2212.08284
Autor:
Plé, Thomas, Mauger, Nastasia, Adjoua, Olivier, Jaffrelot-Inizan, Théo, Lagardère, Louis, Huppert, Simon, Piquemal, Jean-Philip
Publikováno v:
J. Chem. Theory Comput., 2023
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:
http://arxiv.org/abs/2212.03137
Publikováno v:
J. Phys. Chem. Lett., 2023, 14, 6, 1609-1617
We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA) formalism, thus enabling the inclusion of van der Waals contributions
Externí odkaz:
http://arxiv.org/abs/2210.09784
Autor:
Inizan, Théo Jaffrelot, Plé, Thomas, Adjoua, Olivier, Ren, Pengyu, Gökcan, Hattice, Isayev, Olexandr, Lagardère, Louis, Piquemal, Jean-Philip
Publikováno v:
Chemical Science, 2023
Deep-HP is a scalable extension of the \TinkerHP\ 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 magnitude offering access to n
Externí odkaz:
http://arxiv.org/abs/2207.14276
Publikováno v:
J. Phys. Chem. B, 2022, 126, 43, 8813-8826
We introduce a new parametrization of the AMOEBA polarizable force field for water denoted Q-AMOEBA, for use in simulations that explicitly account for nuclear quantum effects (NQEs). This study is made possible thanks to the recently introduced adap
Externí odkaz:
http://arxiv.org/abs/2206.13430