.. _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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  :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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  :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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  :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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  :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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  :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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  :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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  :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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.. 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