diff --git a/mlff/nn/layer/so3krates_layer_sparse.py b/mlff/nn/layer/so3krates_layer_sparse.py
index 616ef63..b765b6d 100644
--- a/mlff/nn/layer/so3krates_layer_sparse.py
+++ b/mlff/nn/layer/so3krates_layer_sparse.py
@@ -90,7 +90,9 @@ def __call__(self,
                                     idx_i=idx_i,
                                     idx_j=idx_j)  # (num_nodes, num_features), (num_nodes, num_orders)
 
-        x = x + x_att  # (num_nodes, num_features)
+        x = nn.silu(
+            nn.Dense(num_features)(x) + nn.Dense(num_features)(x_att)
+        )  # (num_nodes, num_features)
         ev = ev + ev_att  # (num_nodes, num_orders)
 
         if self.layer_normalization_1:
@@ -155,7 +157,7 @@ def __dict_repr__(self) -> Dict[str, Dict[str, Any]]:
                 }
 
 
-class AttentionBlock_(nn.Module):
+class AttentionBlock(nn.Module):
     degrees: Sequence[int]
     num_heads: int = 4
     num_features_head: int = 32
@@ -214,6 +216,8 @@ def __call__(self,
         contraction_fn = make_l0_contraction_fn(self.degrees, dtype=ev.dtype)
         degree_repeat_fn = make_degree_repeat_fn(self.degrees, axis=-1)
 
+        rbf_ij = rbf_ij * jnp.expand_dims(cut, axis=-1)
+
         w_ij = nn.Dense(
             features=tot_num_features,
             name='radial_filter_layer_2'
@@ -235,7 +239,7 @@ def __call__(self,
             )(
                 self.activation_fn(
                     nn.Dense(
-                        features=tot_num_features // 4,
+                        features=tot_num_features // 2,
                         name='spherical_filter_layer_1')(contraction_fn(ev_j - ev_i))
                 )
             )  # (num_pairs, tot_num_features)
@@ -262,8 +266,7 @@ def __call__(self,
         k_j = self.qk_non_linearity(jnp.einsum('Hij, NHj -> NHi', Wk, x_H))[idx_j]
         # (num_pairs, tot_num_heads, num_features_head)
 
-        alpha_ij = (q_i * w_ij * k_j).sum(axis=-1) / jnp.sqrt(self.num_features_head).astype(x.dtype) * jnp.expand_dims(cut,
-                                                                                                               axis=-1)
+        alpha_ij = mask.safe_scale((q_i * w_ij * k_j).sum(axis=-1), jnp.expand_dims(cut, axis=-1))
         # (num_pairs, tot_num_heads)
 
         alpha1_ij, alpha2_ij = jnp.split(alpha_ij, indices_or_sections=np.array([self.num_heads]), axis=-1)
@@ -335,12 +338,12 @@ def __call__(self, x, ev, *args, **kwargs):
         degree_repeat_fn = make_degree_repeat_fn(self.degrees, axis=-1)
 
         y = jnp.concatenate([x, contraction_fn(ev)], axis=-1)  # shape: (N, num_features+num_degrees)
-        # y = self.activation_fn(y)
-        # y = nn.Dense(
-        #     features=num_features // 2,
-        #     name='mlp_layer_1'
-        # )(y)  # (N, num_features // 2)
-        # y = self.activation_fn(y)
+        y = self.activation_fn(y)
+        y = nn.Dense(
+            features=num_features,
+            name='mlp_layer_1'
+        )(y)  # (N, num_features)
+        y = self.activation_fn(y)
         y = nn.Dense(
             features=num_features + num_degrees,
             kernel_init=self.last_layer_kernel_init,
@@ -354,7 +357,7 @@ def __call__(self, x, ev, *args, **kwargs):
         return cx, degree_repeat_fn(cev) * ev
 
 
-class AttentionBlock(nn.Module):
+class AttentionBlock_(nn.Module):
     degrees: Sequence[int]
     num_heads: int = 4
     num_features_head: int = 32