diff --git a/mace_jax/data/utils.py b/mace_jax/data/utils.py
index 7558cf0..c16706c 100644
--- a/mace_jax/data/utils.py
+++ b/mace_jax/data/utils.py
@@ -194,9 +194,7 @@ def load_from_xyz(
 
 class AtomicNumberTable:
     def __init__(self, zs: Sequence[int]):
-        zs = list(zs)
-        # integers
-        assert all(isinstance(z, int) for z in zs)
+        zs = [int(z) for z in zs]
         # unique
         assert len(zs) == len(set(zs))
         # sorted
diff --git a/mace_jax/modules/__init__.py b/mace_jax/modules/__init__.py
index 4f91339..32002b4 100644
--- a/mace_jax/modules/__init__.py
+++ b/mace_jax/modules/__init__.py
@@ -7,7 +7,7 @@
     RadialEmbeddingBlock,
     ScaleShiftBlock,
 )
-from .loss import WeightedEnergyForcesStressLoss
+from .loss import WeightedEnergyForcesStressLoss, uber_loss
 from .message_passing import MessagePassingConvolution
 from .models import MACE
 from .symmetric_contraction import SymmetricContraction
@@ -21,6 +21,7 @@
     "RadialEmbeddingBlock",
     "ScaleShiftBlock",
     "WeightedEnergyForcesStressLoss",
+    "uber_loss",
     "MessagePassingConvolution",
     "MACE",
     "SymmetricContraction",
diff --git a/mace_jax/modules/loss.py b/mace_jax/modules/loss.py
index 54b4357..89bbf48 100644
--- a/mace_jax/modules/loss.py
+++ b/mace_jax/modules/loss.py
@@ -62,3 +62,10 @@ def __repr__(self):
             f"forces_weight={self.forces_weight:.3f}, "
             f"stress_weight={self.stress_weight:.3f})"
         )
+
+
+def uber_loss(x, t=1.0):
+    x_center = jnp.where(jnp.abs(x) <= 1.01 * t, x, 0.0)
+    center = x_center**2 / (2 * t)
+    sides = jnp.abs(x) - t / 2
+    return jnp.where(jnp.abs(x) <= t, center, sides)
diff --git a/mace_jax/tools/train.py b/mace_jax/tools/train.py
index 09cd76c..6a0bd5d 100644
--- a/mace_jax/tools/train.py
+++ b/mace_jax/tools/train.py
@@ -22,6 +22,7 @@ def train(
     optimizer_state: Dict[str, Any],
     steps_per_interval: int,
     ema_decay: Optional[float] = None,
+    progress_bar: bool = True,
 ):
     """
     for interval, params, optimizer_state, ema_params in train(...):
@@ -33,6 +34,7 @@ def train(
     logging.info("Started training")
 
     @partial(jax.pmap, in_axes=(None, 0), out_axes=0)
+    # @partial(jax.vmap, in_axes=(None, 0), out_axes=0)
     def grad_fn(params, graph: jraph.GraphsTuple):
         # graph is assumed to be padded by jraph.pad_with_graphs
         mask = jraph.get_graph_padding_mask(graph)  # [n_graphs,]
@@ -85,6 +87,7 @@ def interval_loader():
             interval_loader(),
             desc=f"Train interval {interval}",
             total=steps_per_interval,
+            disable=not progress_bar,
         )
         for graph in p_bar:
             num_updates += 1