Comments (2)
The model was trained on MP relaxations, which are mostly the primitive cells. However, like all IAPs, what matters is whether it captures the relationship between energies/forces and the atomic environments. I wouldn't think it matters whether you use primitive or conventional cells. For the benchmarks I showed, I used the conventional cubic cells because that is the easiest for most people to interpret. It appears just as accurate even though the original model was trained on primitive cells (mostly).
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Fantastic. Thank you!
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Related Issues (20)
- Transfer learning with m3gnet
- m3gnet for 1 or 2 atoms ValueError with tensorflow==2.10.0 HOT 4
- Only isolated atom calculation is difficult for m3gnet? HOT 1
- Get energy above hull through the m3gnet model HOT 1
- segmentation issue with energy calculation for some structures HOT 3
- Feature request: relaxation under pressure
- Python 3.11 HOT 6
- Cannot read 'structure' of the figshare training data HOT 2
- Error in loss function during training HOT 1
- Inconsistent results when predicting on batches with GPU HOT 1
- a suggestion to change predict_structure function name of m3gNet to predict_energy HOT 1
- fix atoms during MD/relax HOT 2
- Derivatives of forces are NaN when there's a right angle in a structure
- is this model can use multi cpu for relaxing?
- Reproduce IAP training results
- M3GNet will crash if it meets some materials in Material project, here are the mp-ids, any hint to fix them? HOT 3
- Potential fails to predict structure without threebody interaction in threebody cutoff of 4 Å HOT 5
- Model restored from checkpoint predicts wrong energies
- Recovering MatBench results from weights for submission
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