Best practices for second-generation Car-Parrinello ab initio molecular dynamics with CP2K/Quickstep
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911714980659200 |
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| author | Kühne, Thomas D. |
| author_facet | Kühne, Thomas D. |
| contents | Second-generation Car--Parrinello \textit{ab initio} molecular dynamics (CP2G AIMD) combines a Born--Oppenheimer-like nuclear equation of motion with a predictor-corrector propagation of the one-particle density matrix and a modified Langevin equation to ensure an accurate sampling of the Boltzmann distribution. In the CP2K implementation, this approach can substantially reduce the number of self-consistent-field iterations required per molecular-dynamics step, but only if the density-matrix propagation, the orbital-transformation corrector, and the modified Langevin friction parameters are tuned consistently. This tutorial gives a practical workflow for setting up CP2G AIMD calculations in CP2K, starting from Born--Oppenheimer pre-equilibration and proceeding through corrector-step-size selection, always-stable predictor-corrector order optimization, final relaxation, and the calibration of the dissipative and stochastic terms used for canonical sampling. The protocol is demonstrated for a 32-molecule liquid-water benchmark and is written for users who already know the basics of CP2K input files but want a reliable route from a conventional AIMD setup to a production-ready CP2G simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12191 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Best practices for second-generation Car-Parrinello ab initio molecular dynamics with CP2K/Quickstep Kühne, Thomas D. Computational Physics Disordered Systems and Neural Networks Materials Science Statistical Mechanics Chemical Physics Second-generation Car--Parrinello \textit{ab initio} molecular dynamics (CP2G AIMD) combines a Born--Oppenheimer-like nuclear equation of motion with a predictor-corrector propagation of the one-particle density matrix and a modified Langevin equation to ensure an accurate sampling of the Boltzmann distribution. In the CP2K implementation, this approach can substantially reduce the number of self-consistent-field iterations required per molecular-dynamics step, but only if the density-matrix propagation, the orbital-transformation corrector, and the modified Langevin friction parameters are tuned consistently. This tutorial gives a practical workflow for setting up CP2G AIMD calculations in CP2K, starting from Born--Oppenheimer pre-equilibration and proceeding through corrector-step-size selection, always-stable predictor-corrector order optimization, final relaxation, and the calibration of the dissipative and stochastic terms used for canonical sampling. The protocol is demonstrated for a 32-molecule liquid-water benchmark and is written for users who already know the basics of CP2K input files but want a reliable route from a conventional AIMD setup to a production-ready CP2G simulation. |
| title | Best practices for second-generation Car-Parrinello ab initio molecular dynamics with CP2K/Quickstep |
| topic | Computational Physics Disordered Systems and Neural Networks Materials Science Statistical Mechanics Chemical Physics |
| url | https://arxiv.org/abs/2601.12191 |