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Major list update 2024-08. Postponed from 2024-01, 2024-03, 2024-06.
Previous major updates in issues #181, #94.
For the procedure, reusing the **Major List Update How-To** from issue #94 comme…
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The name `metatensor-models` is a bit long, and there is some confusion between the code here and the code in `metatensor.torch.atomistic`.
It might be worth to pick a new name for this repo instea…
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Hi,
I'm trying to train a model using forces and energies for silicon, based on the tutorial found here: https://schnetpack.readthedocs.io/en/latest/tutorials/tutorial_03_force_models.html.
The t…
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Comment below with questions or thoughts about the reading for [this week's workshop](https://github.com/uchicago-computation-workshop/Winter2020/tree/master/01-09_Boudourides).
Please make your co…
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Dear Dr. Niklas Gebauer,
I've recently been using your project to train a custom dataset and trying to apply it on new attributes. During this process, I encountered some doubts about property trai…
ldhkc updated
4 months ago
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# Introduction
There have been an emerging trend and great interest in foundation machine learning interatomic potentials (MLIPs/MLFFs) for simulating atomistic systems close to density functional …
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When I try to train the model on QM9 I got an error: ValueError: dictionary update sequence element #0 has length 1; 2 is required. When I try to run the line: trainer.fit(task, datamodule=qm9data)
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**Project details:**
- Project Name: GPUMD
- Github URL: https://gpumd.org/ and https://github.com/brucefan1983/GPUMD
- Category: Interatomic Potentials (ML-IAP)
- Labels: "ml-iap", "lang-cpp"…
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Dear All,
I am new to the schnetpack. I am training a model using config.yaml files, not doing any scripting in python.
My question:
Is there any directive to write progress of the training,…