diff --git a/src/fairchem/core/models/escn/escn.py b/src/fairchem/core/models/escn/escn.py
index 0a6c7064d..bedbf12c5 100644
--- a/src/fairchem/core/models/escn/escn.py
+++ b/src/fairchem/core/models/escn/escn.py
@@ -173,14 +173,18 @@ def __init__(
             )
 
         # Initialize the transformations between spherical and grid representations
-        self.SO3_grid = nn.ModuleList()
-        for lval in range(max(self.lmax_list) + 1):
-            SO3_m_grid = nn.ModuleList()
-            for m in range(max(self.lmax_list) + 1):
-                SO3_m_grid.append(SO3_Grid(lval, m, resolution=resolution))
-
-            self.SO3_grid.append(SO3_m_grid)
-
+        assert self.num_resolutions == 1, "Only one resolution is supported"
+        self.SO3_grid = nn.ModuleDict()
+        self.SO3_grid["lmax_lmax"] = SO3_Grid(self.lmax_list[0], self.lmax_list[0], resolution=resolution)
+        self.SO3_grid["lmax_mmax"] = SO3_Grid(self.lmax_list[0], self.mmax_list[0], resolution=resolution)
+        # self.SO3_grid = nn.ModuleList()
+        # for lval in range(max(self.lmax_list) + 1):
+        #     SO3_m_grid = nn.ModuleList()
+        #     for m in range(max(self.lmax_list) + 1):
+        #         SO3_m_grid.append(SO3_Grid(lval, m, resolution=resolution))
+
+        #     self.SO3_grid.append(SO3_m_grid)
+        # import pdb;pdb.set_trace()
         self.mappingReduced = CoefficientMapping(self.lmax_list, self.mmax_list)
 
         # Initialize the blocks for each layer of the GNN
@@ -655,8 +659,8 @@ def forward(
         max_lmax = max(self.lmax_list)
 
         # Project to grid
-        x_grid_message = x_message.to_grid(self.SO3_grid, lmax=max_lmax)
-        x_grid = x.to_grid(self.SO3_grid, lmax=max_lmax)
+        x_grid_message = x_message.to_grid(self.SO3_grid["lmax_lmax"])
+        x_grid = x.to_grid(self.SO3_grid["lmax_lmax"])
         x_grid = torch.cat([x_grid, x_grid_message], dim=3)
 
         # Perform point-wise convolution
@@ -665,7 +669,7 @@ def forward(
         x_grid = self.fc3_sphere(x_grid)
 
         # Project back to spherical harmonic coefficients
-        x_message._from_grid(x_grid, self.SO3_grid, lmax=max_lmax)
+        x_message._from_grid(x_grid, self.SO3_grid["lmax_lmax"])
 
         # Return aggregated messages
         return x_message
@@ -780,7 +784,7 @@ def forward(
         x_target.embedding = x_source.embedding + x_target.embedding
 
         # Point-wise spherical non-linearity
-        x_target._grid_act(self.SO3_grid, self.act, self.mappingReduced.res_size)
+        x_target._grid_act(self.SO3_grid["lmax_mmax"], self.act, self.mappingReduced.res_size)
 
         # Rotate back the irreps
         x_target._rotate_inv(SO3_edge_rot, self.mappingReduced.res_size)
diff --git a/src/fairchem/core/models/escn/so3.py b/src/fairchem/core/models/escn/so3.py
index cb0f9d223..0c674e288 100644
--- a/src/fairchem/core/models/escn/so3.py
+++ b/src/fairchem/core/models/escn/so3.py
@@ -274,12 +274,8 @@ def _grid_act(self, SO3_grid, act, res_size) -> None:
             num_coefficients = res_size[i]
 
             x_res = self.embedding[:, offset : offset + num_coefficients].contiguous()
-            to_grid_mat = SO3_grid[self.lmax_list[i]][
-                self.mmax_list[i]
-            ].get_to_grid_mat(self.device)
-            from_grid_mat = SO3_grid[self.lmax_list[i]][
-                self.mmax_list[i]
-            ].get_from_grid_mat(self.device)
+            to_grid_mat = SO3_grid.get_to_grid_mat(self.device)
+            from_grid_mat = SO3_grid.get_from_grid_mat(self.device)
 
             x_grid = torch.einsum("bai,zic->zbac", to_grid_mat, x_res)
             x_grid = act(x_grid)
@@ -293,8 +289,8 @@ def to_grid(self, SO3_grid, lmax: int = -1) -> torch.Tensor:
         if lmax == -1:
             lmax = max(self.lmax_list)
 
-        to_grid_mat_lmax = SO3_grid[lmax][lmax].get_to_grid_mat(self.device)
-        grid_mapping = SO3_grid[lmax][lmax].mapping
+        to_grid_mat_lmax = SO3_grid.get_to_grid_mat(self.device)
+        grid_mapping = SO3_grid.mapping
 
         offset = 0
         x_grid = torch.tensor([], device=self.device)
@@ -316,12 +312,9 @@ def to_grid(self, SO3_grid, lmax: int = -1) -> torch.Tensor:
         return x_grid
 
     # Compute irreps from grid representation
-    def _from_grid(self, x_grid, SO3_grid, lmax: int = -1) -> None:
-        if lmax == -1:
-            lmax = max(self.lmax_list)
-
-        from_grid_mat_lmax = SO3_grid[lmax][lmax].get_from_grid_mat(self.device)
-        grid_mapping = SO3_grid[lmax][lmax].mapping
+    def _from_grid(self, x_grid, SO3_grid) -> None:
+        from_grid_mat_lmax = SO3_grid.get_from_grid_mat(self.device)
+        grid_mapping = SO3_grid.mapping
 
         offset = 0
         offset_channel = 0
diff --git a/tests/core/models/test_escn_compiles.py b/tests/core/models/test_escn_compiles.py
index 89ba8502a..e6d0e0d8f 100644
--- a/tests/core/models/test_escn_compiles.py
+++ b/tests/core/models/test_escn_compiles.py
@@ -118,7 +118,10 @@ def test_escn_compiles(self):
         torch._dynamo.config.optimize_ddp = False
         # torch._dynamo.explain(model)(data)
         # assert False
-        compiled_model(data)
+        output = compiled_model(data)
+        expected_energy, expected_forces = expected_energy_forces()
+        assert torch.allclose(output["energy"], expected_energy)
+        assert torch.allclose(output["forces"].mean(0), expected_forces)
 
     def test_rotation_invariance(self) -> None:
         random.seed(1)