.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated_examples/1-ase-simulations/plot_md_nve.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_generated_examples_1-ase-simulations_plot_md_nve.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_examples_1-ase-simulations_plot_md_nve.py:


NVE molecular dynamics
======================

Microcanonical (constant energy) molecular dynamics of bulk silicon using
:py:class:`ase.md.verlet.VelocityVerlet`. Velocities are initialized from
a Maxwell–Boltzmann distribution at 300 K; total energy should stay flat
up to integration noise.

.. GENERATED FROM PYTHON SOURCE LINES 10-66



.. image-sg:: /generated_examples/1-ase-simulations/images/sphx_glr_plot_md_nve_001.png
   :alt: Total energy drift, Kinetic / potential energy
   :srcset: /generated_examples/1-ase-simulations/images/sphx_glr_plot_md_nve_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/upet/upet/.tox/docs/lib/python3.13/site-packages/metatomic_ase/_neighbors.py:78: UserWarning: `compute_requested_neighbors_from_options` is deprecated and will be removed in a future version. Please use `neighbor_lists_for_model` to get the calculators and call them directly.
      vesin.metatomic.compute_requested_neighbors_from_options(






|

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from ase import units
    from ase.build import bulk
    from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
    from ase.md.verlet import VelocityVerlet

    from upet.calculator import UPETCalculator


    atoms = bulk("Si", cubic=True, a=5.43, crystalstructure="diamond")
    atoms.calc = UPETCalculator(model="pet-mad-xs", version="1.5.0", device="cpu")

    MaxwellBoltzmannDistribution(atoms, temperature_K=300, rng=np.random.default_rng(0))

    dyn = VelocityVerlet(atoms, timestep=1.0 * units.fs)

    n_steps = 100
    times, e_tot, e_kin, e_pot = [], [], [], []  # type: ignore


    def log():
        times.append(len(times) * 1.0)  # fs
        ekin = atoms.get_kinetic_energy()
        epot = atoms.get_potential_energy()
        e_kin.append(ekin)
        e_pot.append(epot)
        e_tot.append(ekin + epot)


    log()
    for _ in range(n_steps):
        dyn.run(1)
        log()

    times = np.asarray(times)
    e_tot = np.asarray(e_tot)
    e_kin = np.asarray(e_kin)
    e_pot = np.asarray(e_pot)

    fig, (ax_tot, ax_split) = plt.subplots(1, 2, figsize=(9, 3.5))
    ax_tot.plot(times, e_tot - e_tot[0])
    ax_tot.set_xlabel("time [fs]")
    ax_tot.set_ylabel("E(t) − E(0) [eV]")
    ax_tot.set_title("Total energy drift")

    ax_split.plot(times, e_kin - e_kin[0], label="kinetic")
    ax_split.plot(times, e_pot - e_pot[0], label="potential")
    ax_split.set_xlabel("time [fs]")
    ax_split.set_ylabel("ΔE [eV]")
    ax_split.set_title("Kinetic / potential energy")
    ax_split.legend()

    fig.tight_layout()
    plt.show()


.. _sphx_glr_download_generated_examples_1-ase-simulations_plot_md_nve.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_md_nve.ipynb <plot_md_nve.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_md_nve.py <plot_md_nve.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_md_nve.zip <plot_md_nve.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_