diff --git a/matsciml/datasets/utils.py b/matsciml/datasets/utils.py
index a47b25c6..46e68758 100644
--- a/matsciml/datasets/utils.py
+++ b/matsciml/datasets/utils.py
@@ -607,12 +607,11 @@ def element_types():
 
 
 class Edge:
-    def __init__(
-        self, src: int, dst: int, image: np.ndarray, is_undirected: bool = False
-    ):
+    def __init__(self, src: int, dst: int, image: np.ndarray):
         """
         Initializes the Edge object with the source, destination,
         and image, ensuring directionality and handling self-loops.
+
         Parameters
         ----------
         src : int
@@ -622,17 +621,15 @@ def __init__(
         image : np.ndarray
             1D vector of three elements as a ``np.ndarray``.
         """
-        if is_undirected:
-            if src > dst:
-                # Enforce directed order
-                src, dst, image = dst, src, -image
+        if src > dst:
+            # Enforce directed order
+            src, dst, image = dst, src, -image
         if src == dst:
             # For self-loops, enforce a canonical form of the image
             image = self._canonicalize_image(image)
         self.src = src
         self.dst = dst
         self.image = image
-        self.is_undirected = is_undirected
 
     @staticmethod
     def _canonicalize_image(image: np.ndarray) -> np.ndarray:
@@ -686,10 +683,7 @@ def __eq__(self, other) -> bool:
         """
         if not isinstance(other, Edge):
             return False
-        if self.is_undirected:
-            node_eq = self.directed_index == other.directed_index
-        else:
-            node_eq = (self.src == other.src) and (self.dst == other.dst)
+        node_eq = self.directed_index == other.directed_index
         return node_eq and np.array_equal(self.image, other.image)
 
     def __str__(self) -> str:
@@ -854,32 +848,41 @@ def _all_sites_have_neighbors(neighbors):
         raise ValueError(
             f"No neighbors detected for structure with cutoff {cutoff}; {structure}"
         )
-    keep = set()
-    # only keeps undirected edges that are unique through set
-    for src_idx, dst_sites in enumerate(neighbors):
-        for site in dst_sites:
-            keep.add(
-                Edge(
-                    src_idx,
-                    site.index,
-                    np.array(site.image),
-                    is_undirected,
+    # if we assume undirected edges, apply a filter
+    if is_undirected:
+        keep = set()
+        # only keeps undirected edges that are unique through set
+        for src_idx, dst_sites in enumerate(neighbors):
+            for site in dst_sites:
+                keep.add(
+                    Edge(
+                        src_idx,
+                        site.index,
+                        np.array(site.image),
+                    )
                 )
-            )
-    # now only keep the edges after the first loop
-    all_src, all_dst, all_images = [], [], []
-    num_atoms = len(structure.atomic_numbers)
-    counter = {index: 0 for index in range(num_atoms)}
-    for edge in keep:
-        # stop adding edges if either src/dst have accumulated enough neighbors
-        if counter[edge.src] > max_neighbors or counter[edge.dst] > max_neighbors:
-            pass
-        else:
-            all_src.append(edge.src)
-            all_dst.append(edge.dst)
-            all_images.append(edge.image)
-            counter[edge.src] += 1
-            counter[edge.dst] += 1
+        # now only keep the edges after the first loop
+        all_src, all_dst, all_images = [], [], []
+        num_atoms = len(structure.atomic_numbers)
+        counter = {index: 0 for index in range(num_atoms)}
+        for edge in keep:
+            # stop adding edges if either src/dst have accumulated enough neighbors
+            if counter[edge.src] > max_neighbors or counter[edge.dst] > max_neighbors:
+                pass
+            else:
+                all_src.append(edge.src)
+                all_dst.append(edge.dst)
+                all_images.append(edge.image)
+                counter[edge.src] += 1
+                counter[edge.dst] += 1
+    # alternatively, just add the edges as is from pymatgen
+    else:
+        all_src, all_dst, all_images = [], [], []
+        for src_idx, dst_sites in enumerate(neighbors):
+            for site in dst_sites:
+                all_src.append(src_idx)
+                all_dst.append(site.index)
+                all_images.append(site.image)
     if any([len(obj) == 0 for obj in [all_src, all_dst, all_images]]):
         raise ValueError(
             f"No images or edges to work off for cutoff {cutoff}."
@@ -963,24 +966,28 @@ def calculate_ase_periodic_shifts(
     )
     # not really needed but good sanity check
     assert np.all(distances <= cutoff_radius)
-    keep = set()
-    # only keeps undirected edges that are unique
-    for src, dst, image in zip(all_src, all_dst, all_images):
-        keep.add(Edge(src=src, dst=dst, image=image, is_undirected=is_undirected))
-
-    all_src, all_dst, all_images = [], [], []
-    num_atoms = len(atoms)
-    counter = {index: 0 for index in range(num_atoms)}
-    for edge in keep:
-        # obey max_neighbors by not adding any more edges
-        if counter[edge.src] > max_neighbors or counter[edge.dst] > max_neighbors:
-            pass
-        else:
-            all_src.append(edge.src)
-            all_dst.append(edge.dst)
-            all_images.append(edge.image)
-            counter[edge.src] += 1
-            counter[edge.dst] += 1
+
+    # in the undirected case, we will filter out
+    # half of the edges as src/dst == dst/src for a given image
+    if is_undirected:
+        keep = set()
+        # only keeps undirected edges that are unique
+        for src, dst, image in zip(all_src, all_dst, all_images):
+            keep.add(Edge(src=src, dst=dst, image=image))
+
+        all_src, all_dst, all_images = [], [], []
+        num_atoms = len(atoms)
+        counter = {index: 0 for index in range(num_atoms)}
+        for edge in keep:
+            # obey max_neighbors by not adding any more edges
+            if counter[edge.src] > max_neighbors or counter[edge.dst] > max_neighbors:
+                pass
+            else:
+                all_src.append(edge.src)
+                all_dst.append(edge.dst)
+                all_images.append(edge.image)
+                counter[edge.src] += 1
+                counter[edge.dst] += 1
 
     frac_coords = torch.from_numpy(atoms.get_scaled_positions()).float()
     coords = torch.from_numpy(atoms.positions).float()