diff --git a/chgnet/model/layers.py b/chgnet/model/layers.py
index 94e8248b..f087ecc4 100644
--- a/chgnet/model/layers.py
+++ b/chgnet/model/layers.py
@@ -250,6 +250,7 @@ def forward(
         new_bond_feas = aggregate(
             bond_update, bond_graph[:, 1], average=False, num_owner=len(bond_feas)
         )
+
         # New bond features
         if self.use_mlp_out:
             new_bond_feas = self.mlp_out(new_bond_feas)
diff --git a/chgnet/trainer/trainer.py b/chgnet/trainer/trainer.py
index 2b46b6af..68cab025 100644
--- a/chgnet/trainer/trainer.py
+++ b/chgnet/trainer/trainer.py
@@ -143,16 +143,23 @@ def __init__(
             self.scheduler = ExponentialLR(self.optimizer, **scheduler_params)
             self.scheduler_type = "exp"
         elif scheduler in ["CosineAnnealingLR", "CosLR", "Cos", "cos"]:
+            scheduler_params = kwargs.pop("scheduler_params", {"decay_fraction": 1e-2})
+            decay_fraction = scheduler_params.pop("decay_fraction")
             self.scheduler = CosineAnnealingLR(
                 self.optimizer,
                 T_max=10 * epochs,  # Maximum number of iterations.
-                eta_min=1e-2 * learning_rate,
+                eta_min=decay_fraction * learning_rate,
             )
             self.scheduler_type = "cos"
         elif scheduler in ["CosRestartLR"]:
-            scheduler_params = kwargs.pop("scheduler_params", {"T_0": 10, "T_mult": 2})
+            scheduler_params = kwargs.pop(
+                "scheduler_params", {"decay_fraction": 1e-2, "T_0": 10, "T_mult": 2}
+            )
+            decay_fraction = scheduler_params.pop("decay_fraction")
             self.scheduler = CosineAnnealingWarmRestarts(
-                self.optimizer, eta_min=1e-2 * learning_rate, **scheduler_params
+                self.optimizer,
+                eta_min=decay_fraction * learning_rate,
+                **scheduler_params,
             )
             self.scheduler_type = "cosrestart"
         else: