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Implement SpookyNet potential #133

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@ArnNag ArnNag commented May 29, 2024

Todos

  • Module dropout
  • Batching (relevant in attention for the charge embedding and nonlocal interactions)
    • Figured it out for charge embedding, more involved for nonlocal interactions

Questions

  • Do I need to add trainable element and charge biases? Are we doing this in other models?
  • Where is charge balancing being taken care off?

Status

  • Ready to go

@wiederm wiederm added the enhancement New feature or request label Jun 2, 2024
ArnNag and others added 25 commits June 3, 2024 10:29
… Modifications: hardcoded SiLU activation function. Hardcoded approximate attention. Hardcoded initialization of weights of second linear layer in residual block to 0.
…ment of tuple return value matches, but second element (after final resblock) does not.
wiederm and others added 30 commits July 11, 2024 23:34
# Conflicts:
#	modelforge/dataset/dataset.py
#	modelforge/potential/ani.py
#	modelforge/potential/models.py
#	modelforge/potential/painn.py
#	modelforge/potential/physnet.py
#	modelforge/potential/processing.py
#	modelforge/potential/sake.py
#	modelforge/potential/schnet.py
#	modelforge/potential/utils.py
#	modelforge/tests/data/training_defaults/default.toml
#	modelforge/tests/test_models.py
#	modelforge/tests/test_painn.py
#	modelforge/tests/test_sake.py
#	modelforge/tests/test_schnet.py
#	modelforge/tests/test_training.py
#	modelforge/train/training.py
… implementation on a batched methane with the reference implementation on a single methane
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2 participants