Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects
We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is si...
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| Language: | English |
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Académie des sciences
2023-10-01
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| Series: | Comptes Rendus. Mécanique |
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| Online Access: | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.220/ |
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| author | Gorynina, Olga Legoll, Frédéric Lelièvre, Tony Perez, Danny |
| author_facet | Gorynina, Olga Legoll, Frédéric Lelièvre, Tony Perez, Danny |
| author_sort | Gorynina, Olga |
| collection | DOAJ |
| description | We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy. |
| format | Article |
| id | doaj-art-6dac9dce93c44e6399fdbf139b305a2e |
| institution | Kabale University |
| issn | 1873-7234 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Académie des sciences |
| record_format | Article |
| series | Comptes Rendus. Mécanique |
| spelling | doaj-art-6dac9dce93c44e6399fdbf139b305a2e2025-02-07T13:46:20ZengAcadémie des sciencesComptes Rendus. Mécanique1873-72342023-10-01351S147950310.5802/crmeca.22010.5802/crmeca.220Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defectsGorynina, Olga0Legoll, Frédéric1Lelièvre, Tony2Perez, Danny3CERMICS, École des Ponts, Marne-La-Vallée, France; MATHERIALS project-team, Inria, Paris, FranceMATHERIALS project-team, Inria, Paris, France; Navier, École des Ponts, Univ Gustave Eiffel, CNRS, Marne-La-Vallée, FranceCERMICS, École des Ponts, Marne-La-Vallée, France; MATHERIALS project-team, Inria, Paris, FranceTheoretical Division T-1, Los Alamos National Laboratory, Los Alamos, NM 87545, USAWe numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.220/Parallel-in-time simulationMolecular dynamicsAdaptive algorithmStatistical accuracy |
| spellingShingle | Gorynina, Olga Legoll, Frédéric Lelièvre, Tony Perez, Danny Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects Comptes Rendus. Mécanique Parallel-in-time simulation Molecular dynamics Adaptive algorithm Statistical accuracy |
| title | Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects |
| title_full | Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects |
| title_fullStr | Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects |
| title_full_unstemmed | Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects |
| title_short | Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects |
| title_sort | combining machine learned and empirical force fields with the parareal algorithm application to the diffusion of atomistic defects |
| topic | Parallel-in-time simulation Molecular dynamics Adaptive algorithm Statistical accuracy |
| url | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.220/ |
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