diff --git a/dpgen/auto_test/Interstitial.py b/dpgen/auto_test/Interstitial.py
index a4b06ae1c..b3b6db911 100644
--- a/dpgen/auto_test/Interstitial.py
+++ b/dpgen/auto_test/Interstitial.py
@@ -13,6 +13,20 @@
 from dpgen.auto_test.reproduce import make_repro, post_repro
 
 
+def _smallest_nonzero_distance(distance_matrix):
+    """Return the minimum distance between two distinct atoms.
+
+    Selecting by matrix index, instead of by value, keeps a real zero distance
+    caused by coincident atoms while excluding the zero-valued diagonal.
+    """
+    distances = np.asarray(distance_matrix)
+    atom_pairs = np.triu_indices_from(distances, k=1)
+    pair_distances = distances[atom_pairs]
+    if pair_distances.size == 0:
+        raise ValueError("distance matrix does not contain a pair of atoms")
+    return float(np.min(pair_distances))
+
+
 class Interstitial(Property):
     def __init__(self, parameter, inter_param=None):
         parameter["reproduce"] = parameter.get("reproduce", False)
@@ -191,7 +205,9 @@ def make_confs(self, path_to_work, path_to_equi, refine=False):
                         temp = jj.get_supercell_structure(
                             sc_mat=np.diag(self.supercell, k=0)
                         )
-                        smallest_distance = list(set(temp.distance_matrix.ravel()))[1]
+                        smallest_distance = _smallest_nonzero_distance(
+                            temp.distance_matrix
+                        )
                         if (
                             "conf_filters" in self.parameter
                             and "min_dist" in self.parameter["conf_filters"]
diff --git a/tests/auto_test/test_interstitial_distance.py b/tests/auto_test/test_interstitial_distance.py
new file mode 100644
index 000000000..ef661c618
--- /dev/null
+++ b/tests/auto_test/test_interstitial_distance.py
@@ -0,0 +1,32 @@
+import unittest
+
+import numpy as np
+
+from dpgen.auto_test.Interstitial import _smallest_nonzero_distance
+
+
+class TestInterstitialDistance(unittest.TestCase):
+    def test_smallest_nonzero_distance(self):
+        distance_matrix = np.array(
+            [
+                [0.0, 2.5, 1.25],
+                [2.5, 0.0, 3.0],
+                [1.25, 3.0, 0.0],
+            ]
+        )
+        self.assertEqual(_smallest_nonzero_distance(distance_matrix), 1.25)
+
+    def test_exact_overlap_is_retained(self):
+        """An off-diagonal zero represents an invalid atomic overlap."""
+        distance_matrix = np.array(
+            [
+                [0.0, 0.0, 1.25],
+                [0.0, 0.0, 2.5],
+                [1.25, 2.5, 0.0],
+            ]
+        )
+        self.assertEqual(_smallest_nonzero_distance(distance_matrix), 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()