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Tutorials
=========

Here we present beginner and advanced examples of how to use ``metatrain`` for
various applications. The beginner examples cover basic usage and data
preparation, while the advanced examples demonstrate more complex scenarios.



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Beginner Tutorials
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This sections includes the beginner tutorials on the usage of the ``metatrain`` package.




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    <div class="sphx-glr-thumbcontainer" tooltip="metatrain is designed for a direct usage from the command line (cli). The program is registered via the abbreviation mtt to your command line. The general help of metatrain can be accessed using ">

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  .. image:: /generated_examples/0-beginner/images/thumb/sphx_glr_00-basic-usage_thumb.png
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  :ref:`sphx_glr_generated_examples_0-beginner_00-basic-usage.sh`

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      <div class="sphx-glr-thumbnail-title">Basic Usage</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="    This tutorial is only relevant for users who need to prepare their data from scratch     from several files or for big datasets. If you already have your data in a common     file format (like XYZ or `ASE database`_), you can skip this tutorial and directly     start training.">

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  .. image:: /generated_examples/0-beginner/images/thumb/sphx_glr_01-data_preparation_thumb.png
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  :ref:`sphx_glr_generated_examples_0-beginner_01-data_preparation.py`

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      <div class="sphx-glr-thumbnail-title">How to prepare data for training</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="  This section of the documentation is only relevant for PET model so far.">

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  .. image:: /generated_examples/0-beginner/images/thumb/sphx_glr_02-fine-tuning_thumb.png
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  :ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py`

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      <div class="sphx-glr-thumbnail-title">Fine-tune a pre-trained model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial explains how to train a model with metatrain from scratch and evaluate it. This dataset &lt;ethanol_reduced_100.xyz&gt; is used here as an example of the preferred dataset format. If you have your own dataset, you can simply replace the dataset file name with yours.">

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  .. image:: /generated_examples/0-beginner/images/thumb/sphx_glr_03-train_from_scratch_thumb.png
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  :ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py`

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      <div class="sphx-glr-thumbnail-title">Training a model from scratch</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to visualise your model output using parity plots. In the sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py we learned how to evaluate a trained model on a test set and save the results to an output file. Here we will show how to create parity plots from these results.">

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  .. image:: /generated_examples/0-beginner/images/thumb/sphx_glr_04-parity_plot_thumb.png
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  :ref:`sphx_glr_generated_examples_0-beginner_04-parity_plot.py`

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      <div class="sphx-glr-thumbnail-title">Model validation with parity plots</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to use an already trained and exported model to run an ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained using the architecture-soap-bpnn architecture on 100 ethanol systems containing energies and forces. You can obtain the dataset file &lt;ethanol_reduced_100.xyz&gt; used in this example from our website. The dataset is a subset of the rMD17 dataset.">

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  :ref:`sphx_glr_generated_examples_0-beginner_05-run_ase.py`

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      <div class="sphx-glr-thumbnail-title">Running molecular dynamics with ASE</div>
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Advanced Tutorials
------------------

This sections includes the advanced tutorials on the usage of the ``metatrain`` package.
If you are new to ``metatrain``, we recommend to start with the :ref:`label_quickstart`
and the :ref:`beginner_tutorials`.



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    <div class="sphx-glr-thumbcontainer" tooltip="  This section of the documentation is only relevant for PET model so far.">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_00-transfer-learning_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_00-transfer-learning.py`

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      <div class="sphx-glr-thumbnail-title">Transfer Learning (experimental)</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a model with uncertainties using metatrain. It involves the computation of the uncertainties on ethanol molecules, using the last-layer prediction rigidity (LLPR) approximation. Both total and local (LPR) uncertainties are computed">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_01-llpr_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_01-llpr.py`

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      <div class="sphx-glr-thumbnail-title">Computing LLPR uncertainties</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a model with ZBL corrections.">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_02-zbl_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_02-zbl.py`

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      <div class="sphx-glr-thumbnail-title">Training a model with ZBL corrections</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Besides energy-like targets, the library also supports reading (and training on) more generic targets.">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_03-fitting-generic-targets_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_03-fitting-generic-targets.py`

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      <div class="sphx-glr-thumbnail-title">Fitting generic targets</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a FlashMD model for the direct prediction of molecular dynamics. This type of model affords faster MD simulations compared to MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350).">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_04-flashmd_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_04-flashmd.py`

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      <div class="sphx-glr-thumbnail-title">Training a FlashMD model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="metatrain supports training a model with several GPUs, which can accelerate the training, especially when the training dataset is large / there are many training epochs. This feature is enabled by the :pytorch.distributed module, and thus can do multiprocess parallelism across several nodes.">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_05-multi-gpu_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_05-multi-gpu.py`

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      <div class="sphx-glr-thumbnail-title">Multi-GPU training</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a model on datasets containing structures with and without stress information in the same training set. This is useful when working with:">

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  .. image:: /generated_examples/1-advanced/images/thumb/sphx_glr_06-mixed-stress-training_thumb.png
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  :ref:`sphx_glr_generated_examples_1-advanced_06-mixed-stress-training.py`

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      <div class="sphx-glr-thumbnail-title">Training with Mixed Stress Structures</div>
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   /generated_examples/0-beginner/index.rst
   /generated_examples/1-advanced/index.rst


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      :download:`Download all examples as source code: generated_examples.zip </generated_examples/generated_examples.zip>`

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


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