.. _sphx_glr_generated_examples_1-advanced:

.. _advanced_tutorials:

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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  :doc:`/generated_examples/1-advanced/00-transfer-learning`

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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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  :doc:`/generated_examples/1-advanced/01-llpr`

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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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  :doc:`/generated_examples/1-advanced/02-zbl`

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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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  :doc:`/generated_examples/1-advanced/03-fitting-generic-targets`

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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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  :doc:`/generated_examples/1-advanced/04-flashmd`

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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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  :doc:`/generated_examples/1-advanced/05-multi-gpu`

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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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  :doc:`/generated_examples/1-advanced/06-mixed-stress-training`

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      <div class="sphx-glr-thumbnail-title">Training with Mixed Stress Structures</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a model for the prediction of the electronic density of states (DOS), while accounting for the unique properties of the DOS using the masked dos loss function &lt;dos-loss&gt;. This procedure can be used to train PET-MAD-DOS, a universal model for the electronic density of states. (https://arxiv.org/abs/2508.17418)">

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  :doc:`/generated_examples/1-advanced/07-dos-training`

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      <div class="sphx-glr-thumbnail-title">Training a DOS model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to generate and, optionally, further train an LLPR-derived shallow ensemble model using metatrain. Building on the LLPR approach, this more advanced technique allows for 1) generation of a last-layer ensemble model from an LLPR model and 2) gradient-based tuning of ensemble weights using a negative log-likelihood (NLL) loss, often leading to improved uncertainty estimates at the cost of further training.">

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  :doc:`/generated_examples/1-advanced/08-llpr-ensemble-training`

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      <div class="sphx-glr-thumbnail-title">Generating and training an LLPR-derived shallow ensemble model</div>
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.. toctree::
   :hidden:

   /generated_examples/1-advanced/00-transfer-learning
   /generated_examples/1-advanced/01-llpr
   /generated_examples/1-advanced/02-zbl
   /generated_examples/1-advanced/03-fitting-generic-targets
   /generated_examples/1-advanced/04-flashmd
   /generated_examples/1-advanced/05-multi-gpu
   /generated_examples/1-advanced/06-mixed-stress-training
   /generated_examples/1-advanced/07-dos-training
   /generated_examples/1-advanced/08-llpr-ensemble-training