diff --git a/src/metatrain/utils/composition.py b/src/metatrain/utils/composition.py
index 272377c5..6a188bfc 100644
--- a/src/metatrain/utils/composition.py
+++ b/src/metatrain/utils/composition.py
@@ -73,10 +73,20 @@ def train_model(
         if fixed_weights is None:
             fixed_weights = {}
 
-        missing_types = sorted(set(get_atomic_types(datasets)) - set(self.atomic_types))
+        additional_types = sorted(
+            set(get_atomic_types(datasets)) - set(self.atomic_types)
+        )
+        if additional_types:
+            raise ValueError(
+                "Provided `datasets` contains unknown "
+                f"atomic types {additional_types}. "
+                f"Known types from initilaization are {self.atomic_types}."
+            )
+
+        missing_types = sorted(set(self.atomic_types) - set(get_atomic_types(datasets)))
         if missing_types:
             raise ValueError(
-                f"Provided `datasets` contains unknown atomic types {missing_types}. "
+                f"Provided `datasets` do not contain atomic types {missing_types}. "
                 f"Known types from initilaization are {self.atomic_types}."
             )
 
diff --git a/tests/utils/test_composition.py b/tests/utils/test_composition.py
index 5bb48be0..e9472197 100644
--- a/tests/utils/test_composition.py
+++ b/tests/utils/test_composition.py
@@ -1,6 +1,7 @@
 from pathlib import Path
 
 import metatensor.torch
+import pytest
 import torch
 from metatensor.torch import Labels, TensorBlock, TensorMap
 from metatensor.torch.atomistic import ModelOutput, System
@@ -239,3 +240,104 @@ def test_remove_composition():
     # In QM9 the composition contribution is very large: the standard deviation
     # of the energies is reduced by a factor of over 100 upon removing the composition
     assert std_after < 100.0 * std_before
+
+
+def test_composition_model_missing_types():
+    """
+    Test the error when there are too many or too types in the dataset
+    compared to those declared at initialization.
+    """
+
+    # Here we use three synthetic structures:
+    # - O atom, with an energy of 1.0
+    # - H2O molecule, with an energy of 5.0
+    # - H4O2 molecule, with an energy of 10.0
+    # The expected composition weights are 2.0 for H and 1.0 for O.
+
+    systems = [
+        System(
+            positions=torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float64),
+            types=torch.tensor([8]),
+            cell=torch.eye(3, dtype=torch.float64),
+        ),
+        System(
+            positions=torch.tensor(
+                [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype=torch.float64
+            ),
+            types=torch.tensor([1, 1, 8]),
+            cell=torch.eye(3, dtype=torch.float64),
+        ),
+        System(
+            positions=torch.tensor(
+                [
+                    [0.0, 0.0, 0.0],
+                    [1.0, 0.0, 0.0],
+                    [0.0, 1.0, 0.0],
+                    [0.0, 0.0, 1.0],
+                    [1.0, 0.0, 1.0],
+                    [0.0, 1.0, 1.0],
+                ],
+                dtype=torch.float64,
+            ),
+            types=torch.tensor([1, 1, 8, 1, 1, 8]),
+            cell=torch.eye(3, dtype=torch.float64),
+        ),
+    ]
+    energies = [1.0, 5.0, 10.0]
+    energies = [
+        TensorMap(
+            keys=Labels(names=["_"], values=torch.tensor([[0]])),
+            blocks=[
+                TensorBlock(
+                    values=torch.tensor([[e]], dtype=torch.float64),
+                    samples=Labels(names=["system"], values=torch.tensor([[i]])),
+                    components=[],
+                    properties=Labels(names=["energy"], values=torch.tensor([[0]])),
+                )
+            ],
+        )
+        for i, e in enumerate(energies)
+    ]
+    dataset = Dataset({"system": systems, "energy": energies})
+
+    composition_model = CompositionModel(
+        model_hypers={},
+        dataset_info=DatasetInfo(
+            length_unit="angstrom",
+            atomic_types=[1],
+            targets=TargetInfoDict(
+                {
+                    "energy": TargetInfo(
+                        quantity="energy",
+                        per_atom=False,
+                    )
+                }
+            ),
+        ),
+    )
+    with pytest.raises(
+        ValueError,
+        match="unknown atomic types",
+    ):
+        composition_model.train_model(dataset)
+
+    composition_model = CompositionModel(
+        model_hypers={},
+        dataset_info=DatasetInfo(
+            length_unit="angstrom",
+            atomic_types=[1, 8, 100],
+            targets=TargetInfoDict(
+                {
+                    "energy": TargetInfo(
+                        quantity="energy",
+                        per_atom=False,
+                    )
+                }
+            ),
+        ),
+    )
+    with pytest.raises(
+        ValueError,
+        match="do not contain atomic types",
+    ):
+        composition_model.train_model(dataset)