diff --git a/mtenn/conversion_utils/e3nn.py b/mtenn/conversion_utils/e3nn.py
index 7f303fe..9f338ef 100644
--- a/mtenn/conversion_utils/e3nn.py
+++ b/mtenn/conversion_utils/e3nn.py
@@ -17,23 +17,26 @@ def __init__(self, *args, model=None, **kwargs):
             super(E3NN, self).__init__(*args, **kwargs)
             self.model_parameters = kwargs
         else:
-            # this will need changing to include  model features of e3nn
-            atomref = model.atomref.weight.detach().clone()
-            model_params = (
-                model.hidden_channels,
-                model.num_filters,
-                model.num_interactions,
-                model.num_gaussians,
-                model.cutoff,
-                model.max_num_neighbors,
-                model.readout,
-                model.dipole,
-                model.mean,
-                model.std,
-                atomref,
-            )
-            super(E3NN, self).__init__(*model_params)
-            self.model_parameters = model_params
+            model_kwargs = {
+                "irreps_in": model.irreps_in,
+                "irreps_hidden": model.irreps_hidden,
+                "irreps_out": model.irreps_out,
+                "irreps_node_attr": model.irreps_node_attr,
+                "irreps_edge_attr": model.irreps_edge_attr,
+                "layers": len(model.layers) - 1,
+                "max_radius": model.max_radius,
+                "number_of_basis": model.number_of_basis,
+                "num_nodes": model.num_nodes,
+                "reduce_output": model.reduce_output,
+            }
+            # These need a bit of work to get
+            # Use last layer bc guaranteed to be present and is just a Convolution
+            conv = model.layers[-1]
+            model_kwargs["radial_layers"] =  len(conv.fc.hs) - 2
+            model_kwargs["radial_neurons"] =  conv.fc.hs[1]
+            model_kwargs["num_neighbors"] =  conv.num_neighbors
+            super(E3NN, self).__init__(**model_kwargs)
+            self.model_parameters = model_kwargs
             self.load_state_dict(model.state_dict())
 
     def forward(self, data):