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diff --git a/‎.pre-commit-config.yaml‎
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diff --git a/‎CITATIONS.bib‎
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diff --git a/‎deepmd/dpmodel/atomic_model/__init__.py‎
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diff --git a/‎deepmd/dpmodel/atomic_model/property_atomic_model.py‎
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diff --git a/‎deepmd/dpmodel/descriptor/dpa2.py‎
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diff --git a/‎deepmd/dpmodel/fitting/property_fitting.py‎
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diff --git a/‎deepmd/dpmodel/model/property_model.py‎
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diff --git a/‎deepmd/entrypoints/test.py‎
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diff --git a/‎deepmd/infer/deep_eval.py‎
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diff --git a/‎deepmd/infer/deep_property.py‎
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@@ -29,7 +29,7 @@ repos:
         exclude: ^source/3rdparty
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.8.3
+    rev: v0.8.4
     hooks:
       - id: ruff
         args: ["--fix"]
 
@@ -128,26 +128,26 @@ @article{Zhang_NpjComputMater_2024_v10_p94
   doi          = {10.1038/s41524-024-01278-7},
 }
 
-@misc{Zhang_2023_DPA2,
+@article{Zhang_npjComputMater_2024_v10_p293,
   annote       = {DPA-2},
   author       = {
     Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and Chun
-    Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Jiameng Huang and
-    Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang and Siyuan Liu and
-    Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou and Jianchuan Liu
-    and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang and Jing Wu and Yudi Yang
-    and Jiyuan Yang and Manyi Yang and Fu-Qiang Gong and Linshuang Zhang and
-    Mengchao Shi and Fu-Zhi Dai and Darrin M. York and Shi Liu and Tong Zhu and
-    Zhicheng Zhong and Jian Lv and Jun Cheng and Weile Jia and Mohan Chen and
-    Guolin Ke and Weinan E and Linfeng Zhang and Han Wang
+    Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Anyang Peng and
+    Jiameng Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang
+    and Siyuan Liu and Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou
+    and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang and
+    Jing Wu and Yudi Yang and Jiyuan Yang and Manyi Yang and Fu-Qiang Gong and
+    Linshuang Zhang and Mengchao Shi and Fu-Zhi Dai and Darrin M. York and Shi
+    Liu and Tong Zhu and Zhicheng Zhong and Jian Lv and Jun Cheng and Weile Jia
+    and Mohan Chen and Guolin Ke and Weinan E and Linfeng Zhang and Han Wang
   },
-  title        = {
-    {DPA-2: Towards a universal large atomic model for molecular and material
-    simulation}
-  },
-  publisher    = {arXiv},
-  year         = 2023,
-  doi          = {10.48550/arXiv.2312.15492},
+  title        = {{DPA-2: a large atomic model as a multi-task learner}},
+  journal      = {npj Comput. Mater},
+  year         = 2024,
+  volume       = 10,
+  number       = 1,
+  pages        = 293,
+  doi          = {10.1038/s41524-024-01493-2},
 }
 
 @article{Zhang_PhysPlasmas_2020_v27_p122704,
 
@@ -42,6 +42,9 @@
 from .polar_atomic_model import (
     DPPolarAtomicModel,
 )
+from .property_atomic_model import (
+    DPPropertyAtomicModel,
+)
 
 __all__ = [
     "BaseAtomicModel",
@@ -50,6 +53,7 @@
     "DPDipoleAtomicModel",
     "DPEnergyAtomicModel",
     "DPPolarAtomicModel",
+    "DPPropertyAtomicModel",
     "DPZBLLinearEnergyAtomicModel",
     "LinearEnergyAtomicModel",
     "PairTabAtomicModel",
 
@@ -1,4 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import numpy as np
+
 from deepmd.dpmodel.fitting.property_fitting import (
     PropertyFittingNet,
 )
@@ -15,3 +17,25 @@ def __init__(self, descriptor, fitting, type_map, **kwargs):
                 "fitting must be an instance of PropertyFittingNet for DPPropertyAtomicModel"
             )
         super().__init__(descriptor, fitting, type_map, **kwargs)
+
+    def apply_out_stat(
+        self,
+        ret: dict[str, np.ndarray],
+        atype: np.ndarray,
+    ):
+        """Apply the stat to each atomic output.
+
+        In property fitting, each output will be multiplied by label std and then plus the label average value.
+
+        Parameters
+        ----------
+        ret
+            The returned dict by the forward_atomic method
+        atype
+            The atom types. nf x nloc. It is useless in property fitting.
+
+        """
+        out_bias, out_std = self._fetch_out_stat(self.bias_keys)
+        for kk in self.bias_keys:
+            ret[kk] = ret[kk] * out_std[kk][0] + out_bias[kk][0]
+        return ret
@@ -387,7 +387,7 @@ def __init__(
         use_tebd_bias: bool = False,
         type_map: Optional[list[str]] = None,
     ) -> None:
-        r"""The DPA-2 descriptor. see https://arxiv.org/abs/2312.15492.
+        r"""The DPA-2 descriptor[1]_.
 
         Parameters
         ----------
@@ -434,6 +434,11 @@ def __init__(
         sw:                 torch.Tensor
             The switch function for decaying inverse distance.
 
+        References
+        ----------
+        .. [1] Zhang, D., Liu, X., Zhang, X. et al. DPA-2: a
+           large atomic model as a multi-task learner. npj
+           Comput Mater 10, 293 (2024). https://doi.org/10.1038/s41524-024-01493-2
         """
 
         def init_subclass_params(sub_data, sub_class):
 
@@ -41,10 +41,9 @@ class PropertyFittingNet(InvarFitting):
             this list is of length :math:`N_l + 1`, specifying if the hidden layers and the output layer are trainable.
     intensive
             Whether the fitting property is intensive.
-    bias_method
-            The method of applying the bias to each atomic output, user can select 'normal' or 'no_bias'.
-            If 'normal' is used, the computed bias will be added to the atomic output.
-            If 'no_bias' is used, no bias will be added to the atomic output.
+    property_name:
+            The name of fitting property, which should be consistent with the property name in the dataset.
+            If the data file is named `humo.npy`, this parameter should be "humo".
     resnet_dt
             Time-step `dt` in the resnet construction:
             :math:`y = x + dt * \phi (Wx + b)`
@@ -74,7 +73,7 @@ def __init__(
         rcond: Optional[float] = None,
         trainable: Union[bool, list[bool]] = True,
         intensive: bool = False,
-        bias_method: str = "normal",
+        property_name: str = "property",
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
@@ -89,9 +88,8 @@ def __init__(
     ) -> None:
         self.task_dim = task_dim
         self.intensive = intensive
-        self.bias_method = bias_method
         super().__init__(
-            var_name="property",
+            var_name=property_name,
             ntypes=ntypes,
             dim_descrpt=dim_descrpt,
             dim_out=task_dim,
@@ -113,9 +111,9 @@ def __init__(
     @classmethod
     def deserialize(cls, data: dict) -> "PropertyFittingNet":
         data = data.copy()
-        check_version_compatibility(data.pop("@version"), 3, 1)
+        check_version_compatibility(data.pop("@version"), 4, 1)
         data.pop("dim_out")
-        data.pop("var_name")
+        data["property_name"] = data.pop("var_name")
         data.pop("tot_ener_zero")
         data.pop("layer_name")
         data.pop("use_aparam_as_mask", None)
@@ -131,6 +129,8 @@ def serialize(self) -> dict:
             **InvarFitting.serialize(self),
             "type": "property",
             "task_dim": self.task_dim,
+            "intensive": self.intensive,
         }
+        dd["@version"] = 4
 
         return dd
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
-from deepmd.dpmodel.atomic_model.dp_atomic_model import (
-    DPAtomicModel,
+from deepmd.dpmodel.atomic_model import (
+    DPPropertyAtomicModel,
 )
 from deepmd.dpmodel.model.base_model import (
     BaseModel,
@@ -13,7 +13,7 @@
     make_model,
 )
 
-DPPropertyModel_ = make_model(DPAtomicModel)
+DPPropertyModel_ = make_model(DPPropertyAtomicModel)
 
 
 @BaseModel.register("property")
 
@@ -779,9 +779,17 @@ def test_property(
     tuple[list[np.ndarray], list[int]]
         arrays with results and their shapes
     """
-    data.add("property", dp.task_dim, atomic=False, must=True, high_prec=True)
+    var_name = dp.get_var_name()
+    assert isinstance(var_name, str)
+    data.add(var_name, dp.task_dim, atomic=False, must=True, high_prec=True)
     if has_atom_property:
-        data.add("atom_property", dp.task_dim, atomic=True, must=False, high_prec=True)
+        data.add(
+            f"atom_{var_name}",
+            dp.task_dim,
+            atomic=True,
+            must=False,
+            high_prec=True,
+        )
 
     if dp.get_dim_fparam() > 0:
         data.add(
@@ -832,12 +840,12 @@ def test_property(
         aproperty = ret[1]
         aproperty = aproperty.reshape([numb_test, natoms * dp.task_dim])
 
-    diff_property = property - test_data["property"][:numb_test]
+    diff_property = property - test_data[var_name][:numb_test]
     mae_property = mae(diff_property)
     rmse_property = rmse(diff_property)
 
     if has_atom_property:
-        diff_aproperty = aproperty - test_data["atom_property"][:numb_test]
+        diff_aproperty = aproperty - test_data[f"atom_{var_name}"][:numb_test]
         mae_aproperty = mae(diff_aproperty)
         rmse_aproperty = rmse(diff_aproperty)
 
@@ -854,7 +862,7 @@ def test_property(
         detail_path = Path(detail_file)
 
         for ii in range(numb_test):
-            test_out = test_data["property"][ii].reshape(-1, 1)
+            test_out = test_data[var_name][ii].reshape(-1, 1)
             pred_out = property[ii].reshape(-1, 1)
 
             frame_output = np.hstack((test_out, pred_out))
@@ -868,7 +876,7 @@ def test_property(
 
         if has_atom_property:
             for ii in range(numb_test):
-                test_out = test_data["atom_property"][ii].reshape(-1, 1)
+                test_out = test_data[f"atom_{var_name}"][ii].reshape(-1, 1)
                 pred_out = aproperty[ii].reshape(-1, 1)
 
                 frame_output = np.hstack((test_out, pred_out))
 
@@ -70,8 +70,6 @@ class DeepEvalBackend(ABC):
         "dipole_derv_c_redu": "virial",
         "dos": "atom_dos",
         "dos_redu": "dos",
-        "property": "atom_property",
-        "property_redu": "property",
         "mask_mag": "mask_mag",
         "mask": "mask",
         # old models in v1
@@ -276,6 +274,10 @@ def get_has_spin(self) -> bool:
         """Check if the model has spin atom types."""
         return False
 
+    def get_var_name(self) -> str:
+        """Get the name of the fitting property."""
+        raise NotImplementedError
+
     @abstractmethod
     def get_ntypes_spin(self) -> int:
         """Get the number of spin atom types of this model. Only used in old implement."""
 
@@ -37,25 +37,41 @@ class DeepProperty(DeepEval):
         Keyword arguments.
     """
 
-    @property
     def output_def(self) -> ModelOutputDef:
-        """Get the output definition of this model."""
-        return ModelOutputDef(
+        """
+        Get the output definition of this model.
+        But in property_fitting, the output definition is not known until the model is loaded.
+        So we need to rewrite the output definition after the model is loaded.
+        See detail in change_output_def.
+        """
+        pass
+
+    def change_output_def(self) -> None:
+        """
+        Change the output definition of this model.
+        In property_fitting, the output definition is known after the model is loaded.
+        We need to rewrite the output definition and related information.
+        """
+        self.output_def = ModelOutputDef(
             FittingOutputDef(
                 [
                     OutputVariableDef(
-                        "property",
-                        shape=[-1],
+                        self.get_var_name(),
+                        shape=[self.get_task_dim()],
                         reducible=True,
                         atomic=True,
+                        intensive=self.get_intensive(),
                     ),
                 ]
             )
         )
-
-    def change_output_def(self) -> None:
-        self.output_def["property"].shape = self.task_dim
-        self.output_def["property"].intensive = self.get_intensive()
+        self.deep_eval.output_def = self.output_def
+        self.deep_eval._OUTDEF_DP2BACKEND[self.get_var_name()] = (
+            f"atom_{self.get_var_name()}"
+        )
+        self.deep_eval._OUTDEF_DP2BACKEND[f"{self.get_var_name()}_redu"] = (
+            self.get_var_name()
+        )
 
     @property
     def task_dim(self) -> int:
@@ -120,10 +136,12 @@ def eval(
             aparam=aparam,
             **kwargs,
         )
-        atomic_property = results["property"].reshape(
+        atomic_property = results[self.get_var_name()].reshape(
             nframes, natoms, self.get_task_dim()
         )
-        property = results["property_redu"].reshape(nframes, self.get_task_dim())
+        property = results[f"{self.get_var_name()}_redu"].reshape(
+            nframes, self.get_task_dim()
+        )
 
         if atomic:
             return (
@@ -141,5 +159,9 @@ def get_intensive(self) -> bool:
         """Get whether the property is intensive."""
         return self.deep_eval.get_intensive()
 
+    def get_var_name(self) -> str:
+        """Get the name of the fitting property."""
+        return self.deep_eval.get_var_name()
+
 
 __all__ = ["DeepProperty"]
Original file line number	Diff line number	Diff line change
`@@ -1,6 +1,6 @@`
`1`	`1`	`# SPDX-License-Identifier: LGPL-3.0-or-later`
`2`		`-from deepmd.dpmodel.atomic_model.dp_atomic_model import (`
`3`		`- DPAtomicModel,`
	`2`	`+from deepmd.dpmodel.atomic_model import (`
	`3`	`+ DPPropertyAtomicModel,`
`4`	`4`	`)`
`5`	`5`	`from deepmd.dpmodel.model.base_model import (`
`6`	`6`	`BaseModel,`
`@@ -13,7 +13,7 @@`
`13`	`13`	`make_model,`
`14`	`14`	`)`
`15`	`15`
`16`		`-DPPropertyModel_ = make_model(DPAtomicModel)`
	`16`	`+DPPropertyModel_ = make_model(DPPropertyAtomicModel)`
`17`	`17`
`18`	`18`
`19`	`19`	`@BaseModel.register("property")`