diff --git a/deepmd/pt/utils/dataset.py b/deepmd/pt/utils/dataset.py
index 3043839308..df4f1fc6cd 100644
--- a/deepmd/pt/utils/dataset.py
+++ b/deepmd/pt/utils/dataset.py
@@ -40,6 +40,18 @@ def __getitem__(self, index):
         b_data["natoms"] = self._natoms_vec
         return b_data
 
+        def _build_element_to_frames(self):
+        """Mapping element types to frame indexes"""
+        element_to_frames = {element: [] for element in range(self._ntypes)} 
+        for frame_idx in range(len(self)):
+            frame_data = self._data_system.get_item_torch(frame_idx)
+
+            elements = frame_data["atype"]  
+            for element in set(elements):
+                if len(element_to_frames[element]) < 10:  
+                    element_to_frames[element].append(frame_idx)
+        return element_to_frames
+
     def add_data_requirement(self, data_requirement: list[DataRequirementItem]) -> None:
         """Add data requirement for this data system."""
         for data_item in data_requirement:
diff --git a/deepmd/pt/utils/stat.py b/deepmd/pt/utils/stat.py
index 1c5e3f1c52..82cb816f7b 100644
--- a/deepmd/pt/utils/stat.py
+++ b/deepmd/pt/utils/stat.py
@@ -82,6 +82,41 @@ def make_stat_input(datasets, dataloaders, nbatches):
                 sys_stat[key] = torch.cat(sys_stat[key], dim=0)
         dict_to_device(sys_stat)
         lst.append(sys_stat)
+
+    all_elements = set()
+    if datasets and hasattr(datasets[0], 'element_to_frames'):
+        all_elements.update(datasets[0].element_to_frames.keys())
+        print('we want', all_elements)
+
+    collected_elements = set()
+    for sys_stat in lst:
+        if 'atype' in sys_stat:
+            collected_elements.update(np.unique(sys_stat['atype'].cpu().numpy()))
+            missing_elements = all_elements - collected_elements
+
+            for missing_element in missing_elements:
+                for i, dataset in enumerate(datasets):
+                    if hasattr(dataset, 'element_to_frames'):
+                        frame_indices = dataset.element_to_frames.get(missing_element, [])
+                        for frame_idx in frame_indices:
+                            if len(lst[i]['atype']) >= nbatches: 
+                                break
+                            frame_data = dataset[frame_idx]
+                            for key in frame_data:
+                                if key not in lst[i]:
+                                    lst[i][key] = []
+                                lst[i][key].append(frame_data[key])
+
+            collected_elements = set()
+            for sys_stat in lst:
+                if 'atype' in sys_stat:
+                    collected_elements.update(np.unique(sys_stat['atype'].cpu().numpy()))
+
+    for sys_stat in lst:
+        for key in sys_stat:
+            if isinstance(sys_stat[key], list) and isinstance(sys_stat[key][0], torch.Tensor):
+                sys_stat[key] = torch.cat(sys_stat[key], dim=0)
+
     return lst