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Examples Gallery
================

This gallery contains examples demonstrating the capabilities of ALCHEMI Toolkit-Ops.
Each example is a fully executable Python script that showcases different features
of the library, from basic neighbor list construction to advanced dispersion corrections.

All examples are designed to run on both CPU and GPU, automatically detecting available
hardware. The code can be downloaded as Python scripts or Jupyter notebooks.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates B-spline charge spreading and gathering operations used in Particle Mesh Ewald (PME) and related methods. B-splines provide smooth interpolation between particle positions and mesh grid points.">

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  .. image:: /examples/images/thumb/sphx_glr_01_bspline_visualization_thumb.png
    :alt:

  :ref:`sphx_glr_examples_01_bspline_visualization.py`

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      <div class="sphx-glr-thumbnail-title">B-Spline Interpolation for Particle Mesh Methods</div>
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   /examples/01_bspline_visualization

Interactions
============

Examples demonstrating GPU-accelerated computation of atomic interactions,
including DFT-D3 dispersion corrections with environment-dependent C6 coefficients.

These examples show how to:

* Compute dispersion energies and forces for molecules
* Process batches of crystal structures
* Integrate with PyTorch for differentiable workflows



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to compute the DFT-D3 dispersion energy and forces for a single molecular system using GPU-accelerated kernels.">

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  .. image:: /examples/dispersion/images/thumb/sphx_glr_01_dftd3_molecule_thumb.png
    :alt:

  :ref:`sphx_glr_examples_dispersion_01_dftd3_molecule.py`

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      <div class="sphx-glr-thumbnail-title">DFT-D3 Dispersion Correction for a Molecule</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates GPU-accelerated DFT-D3 calculations for batches of periodic crystal structures. Batch processing is essential for high-throughput computational materials screening and allows efficient parallelization across multiple structures simultaneously.">

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  .. image:: /examples/dispersion/images/thumb/sphx_glr_02_dftd3_batch_crystals_thumb.png
    :alt:

  :ref:`sphx_glr_examples_dispersion_02_dftd3_batch_crystals.py`

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      <div class="sphx-glr-thumbnail-title">DFT-D3 Dispersion Correction for Batched Crystals</div>
    </div>


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    </div>

Electrostatics
==============

Examples demonstrating GPU-accelerated computation of long-range electrostatic
interactions in periodic systems using Coulomb, Ewald summation, and Particle
Mesh Ewald (PME).

These examples show how to:

* Compute direct Coulomb interactions (damped and undamped)
* Use Ewald summation for periodic systems with automatic parameter estimation
* Apply Particle Mesh Ewald (PME) for O(N log N) scaling
* Work with neighbor list and neighbor matrix formats
* Perform batch evaluation for multiple systems
* Leverage autograd for computing forces and gradients



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to compute Coulomb electrostatic interactions using GPU-accelerated kernels. The implementation supports both direct (undamped) Coulomb interactions and damped interactions used as the real-space component in Ewald/PME methods.">

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  .. image:: /examples/electrostatics/images/thumb/sphx_glr_01_coulomb_example_thumb.png
    :alt:

  :ref:`sphx_glr_examples_electrostatics_01_coulomb_example.py`

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      <div class="sphx-glr-thumbnail-title">Coulomb Electrostatic Interactions</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to compute long-range electrostatic interactions in periodic systems using the Ewald summation method. Ewald splits the slowly converging Coulomb sum into rapidly converging real-space and reciprocal-space components.">

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  .. image:: /examples/electrostatics/images/thumb/sphx_glr_02_ewald_summation_example_thumb.png
    :alt:

  :ref:`sphx_glr_examples_electrostatics_02_ewald_summation_example.py`

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      <div class="sphx-glr-thumbnail-title">Ewald Summation for Long-Range Electrostatics</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to compute long-range electrostatic interactions using the Particle Mesh Ewald (PME) method. PME achieves O(N log N) scaling by using FFT-based mesh interpolation for the reciprocal-space contribution.">

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  .. image:: /examples/electrostatics/images/thumb/sphx_glr_03_pme_example_thumb.png
    :alt:

  :ref:`sphx_glr_examples_electrostatics_03_pme_example.py`

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      <div class="sphx-glr-thumbnail-title">Particle Mesh Ewald (PME) for Long-Range Electrostatics</div>
    </div>


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Neighbor Lists
==============

Examples demonstrating efficient O(N) neighbor list construction using GPU-accelerated
cell list algorithms.

These examples show how to:

* Build neighbor lists for single and batched systems
* Use dense or sparse COO output formats
* Detect when neighbor lists need rebuilding
* Optimize performance with ``torch.compile``
* Integrate with molecular dynamics workflows



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the basic neighbor list functions in nvalchemiops on random systems. We&#x27;ll cover:">

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  .. image:: /examples/neighborlist/images/thumb/sphx_glr_01_simple_neighbor_list_thumb.png
    :alt:

  :ref:`sphx_glr_examples_neighborlist_01_simple_neighbor_list.py`

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      <div class="sphx-glr-thumbnail-title">Simple Neighbor List Example</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the batch neighbor list functions in nvalchemiops with multiple molecular and crystalline systems. We&#x27;ll cover:">

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  .. image:: /examples/neighborlist/images/thumb/sphx_glr_02_batch_neighbor_list_thumb.png
    :alt:

  :ref:`sphx_glr_examples_neighborlist_02_batch_neighbor_list.py`

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      <div class="sphx-glr-thumbnail-title">Batch Neighbor List Example</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use rebuild detection functions in nvalchemiops to efficiently determine when neighbor lists need to be reconstructed during molecular dynamics simulations. We&#x27;ll cover:">

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  .. image:: /examples/neighborlist/images/thumb/sphx_glr_03_rebuild_neighborlist_detection_thumb.png
    :alt:

  :ref:`sphx_glr_examples_neighborlist_03_rebuild_neighborlist_detection.py`

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      <div class="sphx-glr-thumbnail-title">Neighbor List Rebuild Detection Example</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the significant performance benefits of using torch.compile with nvalchemiops neighbor list routines in a realistic molecular dynamics simulation. We&#x27;ll cover:">

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  .. image:: /examples/neighborlist/images/thumb/sphx_glr_04_neighbors_list_torch_compile_performance_thumb.png
    :alt:

  :ref:`sphx_glr_examples_neighborlist_04_neighbors_list_torch_compile_performance.py`

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      <div class="sphx-glr-thumbnail-title">Torch.compile Performance Benefits Example</div>
    </div>


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.. toctree::
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   :includehidden:


   /examples/dispersion/index.rst
   /examples/electrostatics/index.rst
   /examples/neighborlist/index.rst


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      :download:`Download all examples in Python source code: examples_python.zip </examples/examples_python.zip>`

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

      :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip </examples/examples_jupyter.zip>`


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