Comments (1)
I don't believe this will be something that we will be actively focusing on at this point in time. We do welcome people to contribute QM9 models, but our focus right now is on other models. It should be relatively straightforward to retrain on QM9 if you want to pursue this.
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Related Issues (20)
- Issues with training M3GNet potential on GPUs. HOT 2
- [Feature Request]: Relaxation under pressure HOT 1
- [Bug]: Data type for ASE potential energy result is not a float
- matgl - now available on conda-forge HOT 1
- [Bug]: Retrained M3Gnet potential cannot be used HOT 1
- [Feature Request]: Larger than memory datasets. HOT 3
- [Bug]: Not all data is on the same device HOT 1
- Missing 'bond type' attribute HOT 1
- How to use the potential of a finetuned model. HOT 1
- [Feature Request]: Implementation of other thermostats for molecular dynamics using matgl HOT 1
- what are the outputted units for stress? HOT 2
- Does the example training script work for multi-gpu training? HOT 1
- [Bug]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! HOT 5
- TensorNet HOT 1
- [Bug]: `RuntimeError: expected scalar type Float but found Double` HOT 1
- [Bug]: Cannot load from checkpoint HOT 2
- [Bug]: pymatgen CI broken due to transitive `pydantic` dependency via `matgl->dgl->pydantic`.
- [Bug]: Incompataibility with `dgl==2.1.0` and `torch==2.2.2` HOT 11
- [Bug]: Getting ValueError due to run "Training a M3GNet Formation Energy Model with PyTorch Lightning.ipynb" without any change in code. HOT 2
- [Bug]: AttributeError: 'M3GNet' object has no attribute 'calc_stresses' HOT 1
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