diff --git a/torchmdnet/models/model.py b/torchmdnet/models/model.py
index a2a80f901..015d629f7 100644
--- a/torchmdnet/models/model.py
+++ b/torchmdnet/models/model.py
@@ -378,6 +378,10 @@ def forward(
         assert z.dim() == 1 and z.dtype == torch.long
         batch = torch.zeros_like(z) if batch is None else batch
 
+        # trick to incorporate SPICE pqs
+        # set charge: true in yaml ((?) currently I do it)
+        q = extra_args["pq"]
+        
         if self.derivative:
             pos.requires_grad_(True)
         # run the potentially wrapped representation model
diff --git a/torchmdnet/models/tensornet.py b/torchmdnet/models/tensornet.py
index a2006c9e7..2b830ce19 100644
--- a/torchmdnet/models/tensornet.py
+++ b/torchmdnet/models/tensornet.py
@@ -239,7 +239,8 @@ def forward(
         # Total charge q is a molecule-wise property. We transform it into an atom-wise property, with all atoms belonging to the same molecule being assigned the same charge q
         if q is None:
             q = torch.zeros_like(z, device=z.device, dtype=z.dtype)
-        else:
+        # if not atom-wise, make atom-wise (pq is already atom-wise)
+        if z.shape != q.shape:
             q = q[batch]
         zp = z
         if self.static_shapes: