There were several issues prohibiting the installation of gemnet as package:

• The setup.py file referred to a gemnet_pytorch package even though the folder name is gemnet
• python, cudatoolkit, pytorch and torch_geometric are invalid pip packages.

Most of these should be fixed by 1922774fbf8e21179de5c64d7490fe8dc28fa720

Hi, thanks for sharing the code of GemNet, wonderful work for the prediction of energy.

However, I intend to adopt your model to macromolecules such as proteins instead of small molecules. However, as you know, proteins have far more atoms, which unavoidably leads to much more GPU memories. In order to prevent the explosion of GPU memories, I had to control the size of model inputs. Thus, I am here to ask for some advice on how to reduce the size of your model input.

Specifically, a straightforward way is to decrease the cutoff distance. So that the edges become less. But I believe that is not a good practice. Can you give me some other solutions (for other hyperparameters)?

Hi there,

Thank you for providing the codebase. I am using GemNet-T pertained weights, and I get better performance in the downstream task. But I can't find how to get the GemNet-T pretrained weights.

Can I know what database and target is used in pretraining?

GemNet is a great work for molecules representation learning.I have a question about dihedral angle "cabd" definition,the definition in your paper seems not consider the direction of dihedral angle , what i means is that the dihedral angles in the flowing picture is the same in your definition, but we konw the dihedral angles is different .The difference may have a influence on message passing and your model.

Hi there,

Thank you for providing the codebase. I am trying to reproduce GemNet on COLL and MD17, and have two questions below:

1. For COLL, I'm wondering what is the total training time? On my end, it seems to be quite large (300-400 hours), so just want to double-check with you.
2. For MD17, can you help provide the config.yml and scaling_factors.json files on each task?

Any help is appreciated.

Hi, great work, and thanks for sharing the code. I have a small question regarding the gradient of inputs["R"]. As far as I know, inputs["R"] represents the positions of atoms. Why do you make its requires_grad True?

        if not self.direct_forces:
            inputs["R"].requires_grad = True

Thank you for your awesome work! When using the proposed model in another downstream task, I found that there is a potential bug in https://github.com/TUM-DAML/gemnet_pytorch/blob/master/gemnet/training/data_provider.py#L31-L49, it could causes evaluating the model on the training data rather than the testing subset.

For example, our indices are [n_train: n_train+n_val] when using split="val". As the shuffle=False, the idx sampler is SequentialSampler(Subset(data_container, indices)). Notice that this sampler produces indices in range [0, n_val].

However,

        super().__init__(
            data_container, ## here is the full set.
            sampler=batch_sampler,
            collate_fn=lambda x: collate(x, data_container),
            pin_memory=True,  # load on CPU push to GPU
            **kwargs
        )

as shown in this code snippet, the "dataset" passed to the DataLoader is the full dataset. Then, the iterator of data loader would take sample according to the indices provided by the sampler. As illustrated above, the sampler produces indices in range [0, n_val]. Therefore, it actually takes data from a subset of training part.

I noticed that such a problem is avoid in train.ipynb by using two data containers. However, this part would be ambiguous for users who want to generalize this model to other datasets.

I tried to fix it as:

class CustomDataLoader(DataLoader):
    def __init__(
        self, data_container, batch_size, indices, shuffle, seed=None, **kwargs
    ):

        if shuffle:
            generator = torch.Generator()
            if seed is not None:
                generator.manual_seed(seed)
            idx_sampler = SubsetRandomSampler(indices, generator)
        else:
            idx_sampler = SequentialSampler(Subset(data_container, indices))

        batch_sampler = BatchSampler(
            idx_sampler, batch_size=batch_size, drop_last=False
        )
        # Note: a bug here if we do not use subset.
        # Sequential sampler on subset returns index like (0, 1, 2, 3...)
        # However, the returned index is on the full data. 
        # If we do not take Subset here, it uses data from training subset. 
        dataset = data_container if shuffle else Subset(data_container, indices)

        super().__init__(
            dataset ,
            sampler=batch_sampler,
            collate_fn=data_container.collate_fn,
            pin_memory=True,  # load on CPU push to GPU
            **kwargs
        )

Hi, I notice you implemented experiments in extensive datasets. However, most of your baseline models show their performance in the popular QM9 dataset. Can you please provide this sort of information so that we can have a more clear understanding of how well your model is? Thanks.

Traceback (most recent call last): File "", line 1, in File "/Users/ngoccuongnguyen/GitHub/gemnet_pytorch/gemnet/model/gemnet.py", line 14, in from .layers.basis_layers import BesselBasisLayer, SphericalBasisLayer, TensorBasisLayer File "/Users/ngoccuongnguyen/GitHub/gemnet_pytorch/gemnet/model/layers/basis_layers.py", line 287 Kmax = if sph.shape[0]==0 else torch.max(torch.max(Kidx + 1), torch.tensor(0))
^ SyntaxError: invalid syntax