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Parameters to reproduce published results of Spherical 3D MPNN and DimeNet ? #20

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octavian-ganea opened this issue May 10, 2021 · 2 comments
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3dgraph Deep Learning on 3D Graphs

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@octavian-ganea
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Is it please possible to publish the full set of parameters that are required in order to reproduce the published results of Spherical 3D MPNN on QM9 and MD17 ? I tried using the default parameters in https://github.com/divelab/DIG/blob/dig/benchmarks/threedgraph/threedgraph.ipynb and played with some variations (lr, bs, etc) but the results I got were quite far from the published results, for both Spherical Net and DimeNet. Worse, the models reach a plateau quite early in the optimization (e.g. after 20 epochs or so). Would be really great if you could publish the full set of hyperparameters used to obtain the results in your paper. Thank you!

@divelab divelab added the 3dgraph Deep Learning on 3D Graphs label May 10, 2021
@limei0307
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Hi, there is a typo in https://github.com/divelab/DIG/blob/dig/dig/threedgraph/method/run.py line 49. So the model didn't converge correctly. I have fixed it and you can try again. The hyperparameters are list in the supplementary of our paper. For SphereNet, it needs about 500 epochs to get the final results on QM9 and about 2000 epochs for MD17. For DimeNet++, you can follow their original hyperparameters here https://github.com/klicperajo/dimenet. Thanks.

@octavian-ganea
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Thank you!

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