| # Repair extxyz tensor-label preservation for atomistic ML data |
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| You are working on a pinned snapshot of `deepmodeling/dpdata`, a data conversion |
| library used in atomistic simulation and machine-learning potential workflows. |
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| A collaborator is converting extended XYZ datasets produced by ASE/QUIP-style |
| tools into dpdata/DeepMD-style labeled systems. Energies, cells, coordinates, |
| and forces survive the conversion, but an important cell-level tensor label used |
| for stress/virial training is missing or not preserved for common extxyz files. |
| This is dangerous because downstream training can silently proceed with an |
| incomplete label set. |
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|
| Inspect the source tree and repair the implementation so that |
| extxyz datasets following common ASE-style conventions preserve the physically |
| equivalent tensor information needed by dpdata. Your fix should be general; do |
| not hard-code the public fixture, molecule, cell size, or numeric output. |
| Consider the public extxyz read and write paths that users naturally exercise |
| through dpdata's format APIs, not only the single public reproduction script. |
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| You can run the public reproduction workflow: |
|
|
| ```bash |
| python reproduce.py |
| ``` |
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| Keep generated files out of your patch. Do not modify task metadata. Existing |
| QUIP/GAP extxyz files that already provide virial-like labels should continue to |
| work. |
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|
