Better Documentation for M3GNet potential training with stresses

[kenko911]

Summary

Better Documentation for M3GNet potential training with stresses.

Checklist

• Google format doc strings added. Check with ruff.
• Type annotations included. Check with mypy.
• Tests added for new features/fixes.
• If applicable, new classes/functions/modules have duecredit @due.dcite decorators to reference relevant papers by DOI (example)

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

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[coderabbitai]

Walkthrough

The notebook Training a M3GNet Potential with PyTorch Lightning.ipynb has been updated to improve how stress data is handled during training. Specifically, the collate function now includes stress data in addition to line graphs, and the Lightning module configuration now incorporates a stress weight parameter to balance its influence during training.

Changes

File Path	Change Summary
examples/.../Training a M3GNet Potential with PyTorch Lightning.ipynb	Modified my_collate_fn to include stress data and set stress_weight in PotentialLightningModule.
Notebook Metadata	Version changed from "3.10.14" to "3.10.9".

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Notebook
    participant PotentialLightningModule
    participant collate_fn_pes

    User->>Notebook: Start notebook execution
    Notebook->>collate_fn_pes: Initialize with include_line_graph=True, include_stress=True
    Notebook->>PotentialLightningModule: Initialize with model, include_line_graph=True, stress_weight=0.01

    Note over Notebook,PotentialLightningModule: Training process
    PotentialLightningModule->>Notebook: Return training results including stress data

Loading

Possibly related issues

• Issue with Stress training #280
  • Adding stress handling and setting the stress_weight in the PotentialLightningModule as done in this PR is likely intended to address issues with stress inclusion during training.

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Recent review details

Configuration used: .coderabbit.yaml
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 5abfde6 and 147d4f5.

Files selected for processing (1)

• examples/Training a M3GNet Potential with PyTorch Lightning.ipynb (3 hunks)

Additional comments not posted (2)

examples/Training a M3GNet Potential with PyTorch Lightning.ipynb (2)

139-139: Approved: Updated collate_fn_pes function call to include stress

The addition of include_stress=True aligns with the PR objectives to incorporate stress considerations into the training process. Ensure that this change is consistently applied across all relevant parts of the notebook.

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153-153: Approved: Updated PotentialLightningModule initialization with stress_weight

The introduction of the stress_weight=0.01 parameter is a significant enhancement, allowing the model to account for stress during training. Verify that the usage of this parameter is consistent and correctly implemented across the notebook.

Verification successful

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Approved: Consistent use of stress_weight parameter verified

The stress_weight=0.01 parameter is correctly implemented in the PotentialLightningModule initialization within the notebook. This enhancement ensures the model accounts for stress during training.

• examples/Training a M3GNet Potential with PyTorch Lightning.ipynb: lit_module = PotentialLightningModule(model=model, include_line_graph=True, stress_weight=0.01)

Scripts executed

The following scripts were executed for the analysis:

---

Script:

#!/bin/bash
# Description: Verify consistent use of `stress_weight` parameter across the notebook.

# Test: Search for the `PotentialLightningModule` initialization across the notebook. Expect: Only occurrences with `stress_weight`.
rg --type python --json $'PotentialLightningModule(.*)stress_weight'

Length of output: 100

---

---

Script:

#!/bin/bash
# Description: Verify consistent use of `stress_weight` parameter across the notebook.

# Test: Search for the `PotentialLightningModule` initialization across the notebook. Expect: Only occurrences with `stress_weight`.
rg 'PotentialLightningModule(.*)stress_weight' --glob "*.ipynb"

Length of output: 233

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