diff --git a/tests/test_runners.py b/tests/test_runners.py
index 2c5e7d4fb..0b440c884 100644
--- a/tests/test_runners.py
+++ b/tests/test_runners.py
@@ -515,11 +515,9 @@ def test_optimize_with_default_autobatcher(
     """Test optimize with autobatcher."""
 
     def mock_estimate(*args, **kwargs) -> float:  # noqa: ARG001
-        return 10_000.0
+        return 200
 
-    monkeypatch.setattr(
-        "torch_sim.autobatching.estimate_max_memory_scaler", mock_estimate
-    )
+    monkeypatch.setattr("torch_sim.autobatching.determine_max_batch_size", mock_estimate)
 
     states = [ar_supercell_sim_state, fe_supercell_sim_state, ar_supercell_sim_state]
     triple_state = initialize_state(
diff --git a/torch_sim/autobatching.py b/torch_sim/autobatching.py
index e5e75c3d5..991230a49 100644
--- a/torch_sim/autobatching.py
+++ b/torch_sim/autobatching.py
@@ -853,6 +853,7 @@ def load_states(
 
         self.first_batch_returned = False
         self._first_batch = self._get_first_batch()
+        return self.max_memory_scaler
 
     def _get_next_states(self) -> list[SimState]:
         """Add states from the iterator until max_memory_scaler is reached.
@@ -928,19 +929,17 @@ def _get_first_batch(self) -> SimState:
         self.current_idx += [0]
         self.swap_attempts.append(0)  # Initialize attempt counter for first state
         self.iterator_idx += 1
-        # self.total_metric += first_metric
 
         # if max_metric is not set, estimate it
         has_max_metric = bool(self.max_memory_scaler)
         if not has_max_metric:
-            self.max_memory_scaler = estimate_max_memory_scaler(
+            n_batches = determine_max_batch_size(
+                first_state,
                 self.model,
-                [first_state],
-                [first_metric],
                 max_atoms=self.max_atoms_to_try,
                 scale_factor=self.memory_scaling_factor,
             )
-            self.max_memory_scaler = self.max_memory_scaler * 0.8
+            self.max_memory_scaler = n_batches * first_metric * 0.8
 
         states = self._get_next_states()
 
@@ -953,7 +952,6 @@ def _get_first_batch(self) -> SimState:
                 scale_factor=self.memory_scaling_factor,
             )
             self.max_memory_scaler = self.max_memory_scaler * self.max_memory_padding
-            print(f"Max metric calculated: {self.max_memory_scaler}")
         return concatenate_states([first_state, *states])
 
     def next_batch(
diff --git a/torch_sim/runners.py b/torch_sim/runners.py
index 148cafa14..be43147d4 100644
--- a/torch_sim/runners.py
+++ b/torch_sim/runners.py
@@ -89,7 +89,7 @@ def _configure_batches_iterator(
     elif autobatcher is False:
         batches = [(state, [])]
     else:
-        raise ValueError(
+        raise TypeError(
             f"Invalid autobatcher type: {type(autobatcher).__name__}, "
             "must be bool or BinningAutoBatcher."
         )
@@ -206,7 +206,7 @@ def _configure_in_flight_autobatcher(
     if isinstance(autobatcher, InFlightAutoBatcher):
         autobatcher.return_indices = True
         autobatcher.max_attempts = max_attempts
-    else:
+    elif isinstance(autobatcher, bool):
         if autobatcher:
             memory_scales_with = model.memory_scales_with
             max_memory_scaler = None
@@ -221,6 +221,11 @@ def _configure_in_flight_autobatcher(
             max_iterations=max_attempts,
             max_memory_padding=0.9,
         )
+    else:
+        raise TypeError(
+            f"Invalid autobatcher type: {type(autobatcher).__name__}, "
+            "must be bool or InFlightAutoBatcher."
+        )
     return autobatcher