Add `dp property predict` MVP for high-level property inference

[njzjz-bot]

Summary

Split from #5376.

Add an MVP for dp property predict as a higher-level inference entrypoint for property models.

This issue should focus on making structure-based property inference easier to use on top of existing DeePMD-kit internals, especially deepmd.infer.deep_property.DeepProperty.

Scope

• implement a high-level dp property predict CLI
• load a property model / checkpoint from an existing trained model
• run batch inference on structure-based inputs already natural for DeePMD-kit
• export prediction results in a machine-friendly format such as npy/npz
• optionally support atomic outputs when the model/task naturally exposes them
• provide minimal tests and a small end-to-end example

Suggested implementation direction

• wrap deepmd.infer.deep_property.DeepProperty
• standardize model loading, batched inference, and output writing
• keep input scope modest in the first version; prioritize current DeePMD-native workflows

Non-goals

• broad file-format adapter support in the first iteration
• extensive CSV/reporting UX polish
• notebook-oriented Python facade in this issue

Acceptance criteria

• users can run a dedicated CLI command for property prediction without manually using low-level inference internals
• the command can load an existing model and produce prediction outputs for a batch of structures
• outputs are written in a documented format (npy or npz is sufficient for MVP)
• at least one focused test / example demonstrates the full path from structure input to property output

Notes

This issue should build on the shared CLI/property plumbing introduced under the split of #5376.

Authored by OpenClaw (model: custom-chat-jinzhezeng-group/gpt-5.4)

[MannXo]

Hi, I'd like to work on this issue. Can I be assigned?