[pull] master from deepmodeling:master - #301
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## Summary - add Hessian prefactors directly to dpmodel `EnergyLoss` instead of introducing a separate loss class - add Hessian label requirements, loss/RMSE reporting, and serialization fields - enable Hessian outputs in the JAX trainer when the Hessian loss is configured ## Tests - `source venv/bin/activate && pytest source/tests/common/dpmodel/test_loss_ener.py -q` - `source venv/bin/activate && ruff check .` - `source venv/bin/activate && ruff format .` <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Optional energy Hessian-loss supervision with configurable scheduled weighting and Hessian RMSE/MAE reporting. * Trainer and model now automatically activate Hessian outputs based on the loss/data contract, including Hessian tensor creation for mixed/padded batches. * **Bug Fixes** * Hessian activation is idempotent to prevent repeated reconfiguration. * Clearer runtime validation for unsupported Hessian + huber combinations. * **Tests** * Expanded Hessian loss, serialization (v4→v5 backward compatible), padding/ghost handling, and MAE/L2 dispatch coverage across NumPy and PyTorch. * **Documentation** * Updated Hessian training docs for JAX and clarified `ener`/legacy `ener_hess` alias behavior. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: njzjz-bot <njzjz.bot@gmail.com> Co-authored-by: njzjz-bot <njzjz-bot@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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