Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Maillet, Jean-Bernard"'
Autor:
Lafourcade, Paul, Maillet, Jean-Bernard, Denoual, Christophe, Duval, Eléonore, Allera, Arnaud, Goryaeva, Alexandra M., Marinica, Mihai-Cosmin
The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computa
Externí odkaz:
http://arxiv.org/abs/2307.01560
Autor:
Allera, Arnaud, Goryaeva, Alexandra M., Lafourcade, Paul, Maillet, Jean-Bernard, Marinica, Mihai-Cosmin
Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analysis methods often reach their limits when applied to crystalline syste
Externí odkaz:
http://arxiv.org/abs/2307.00978
Publikováno v:
Propellants, Explosives, Pyrotechnics / Volume 47, Issue 8 / e202100384 / 2022
The equation of state of the triclinic compound 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as well as its second-order isothermal elastic tensor were computed through classical molecular dynamics simulations under various temperature and pressure co
Externí odkaz:
http://arxiv.org/abs/2201.03224
Autor:
Nikolov, Svetoslav, Wood, Mitchell A., Cangi, Attila, Maillet, Jean-Bernard, Marinica, Mihai-Cosmin, Thompson, Aidan P., Desjarlais, Michael P., Tranchida, Julien
Publikováno v:
npj Computational Materials 7, 153 (2021)
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin mo
Externí odkaz:
http://arxiv.org/abs/2101.07332
Autor:
Allera, Arnaud, Goryaeva, Alexandra M., Lafourcade, Paul, Maillet, Jean-Bernard, Marinica, Mihai-Cosmin
Publikováno v:
In Computational Materials Science 5 January 2024 231
Autor:
Gkeka, Paraskevi, Stoltz, Gabriel, Farimani, Amir Barati, Belkacemi, Zineb, Ceriotti, Michele, Chodera, John, Dinner, Aaron R., Ferguson, Andrew, Maillet, Jean-Bernard, Minoux, Hervé, Peter, Christine, Pietrucci, Fabio, Silveira, Ana, Tkatchenko, Alexandre, Trstanova, Zofia, Wiewiora, Rafal, Leliévre, Tony
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable in
Externí odkaz:
http://arxiv.org/abs/2004.06950
Autor:
Faure, Gérôme, Maillet, Jean-Bernard
Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic method which allows to select the level of resolution at which a fluid is simulated. The aim of this work is to extend SDPD to chemically reactive systems.To this end, an additional progre
Externí odkaz:
http://arxiv.org/abs/1709.03890
Akademický článek
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We present a permutation-invariant distance between atomic configurations, defined through a functional representation of atomic positions. This distance enables to directly compare different atomic environments with an arbitrary number of particles,
Externí odkaz:
http://arxiv.org/abs/1507.02911
Akademický článek
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