Repository: uf3/uf3 (language: python)

Issue:
Cutoff fix
The cutoff that is used to create the supercell and/or construct neighbor lists during featurization has been fixed to be more memory efficient. I have verified that all the resulting feature values and the trained model of the Tungsten example are identical to those before the fix. I have also added a new unit test of a binary system in a periodic cell to further ensure that present and future changes to the featurization algorithm do not erroneously alter the results.

The cutoff that should be used to create the neighbor list of the largest `r_max` across all knot sequences of all interaction types that **involve the center atom**. For example, in the Tungsten example, the 3-body cutoff that is used is `[3.5, 3.5, 7.0]`. Previously it was using a cutoff of 7 Å to construct neighbor lists. As a result, the neighbor list contained 2 neighbor atoms that were as far as 14 Å apart! This pull request fixes this by correctly using a 3.5 Å cutoff.

Regarding performance, it reduced the featurization time of the Tungsten example from 17 minutes 47 seconds to 15 minutes 20 seconds (on my server with 16 cores), which is a 16% increase in speed. I would also guess that it required much less memory, although this was not verified.
Cutoff fix
The cutoff that is used to create the supercell and/or construct neighbor lists during featurization has been fixed to be more memory efficient. I have verified that all the resulting feature values and the trained model of the Tungsten example are identical to those before the fix. I have also added a new unit test of a binary system in a periodic cell to further ensure that present and future changes to the featurization algorithm do not erroneously alter the results.

The cutoff that should be used to create the neighbor list of the largest `r_max` across all knot sequences of all interaction types that **involve the center atom**. For example, in the Tungsten example, the 3-body cutoff that is used is `[3.5, 3.5, 7.0]`. Previously it was using a cutoff of 7 Å to construct neighbor lists. As a result, the neighbor list contained 2 neighbor atoms that were as far as 14 Å apart! This pull request fixes this by correctly using a 3.5 Å cutoff.

Regarding performance, it reduced the featurization time of the Tungsten example from 17 minutes 47 seconds to 15 minutes 20 seconds (on my server with 16 cores), which is a 16% increase in speed. I would also guess that it required much less memory, although this was not verified.


Discussion / hints:

Fix patch:
diff --git a/examples/tungsten_extxyz/pair_potential_demo.ipynb b/examples/tungsten_extxyz/pair_potential_demo.ipynb
index e6bdc11b..bc956a76 100644
--- a/examples/tungsten_extxyz/pair_potential_demo.ipynb
+++ b/examples/tungsten_extxyz/pair_potential_demo.ipynb
@@ -142,7 +142,7 @@
     ":-----------------|:--------------\n",
     "Preprocessing     | 10 seconds\n",
     "Pair distribution | 10 seconds\n",
-    "Featurization     | 5 core-minutes (parallelizable)\n",
+    "Featurization     | 2 core-minutes (parallelizable)\n",
     "Training          | 1 second\n",
     "Prediction        | 1 second\n",
     "Plotting          | 9 seconds"
diff --git a/examples/tungsten_extxyz/uf23_potential_demo.ipynb b/examples/tungsten_extxyz/uf23_potential_demo.ipynb
index 91434aec..9115eec7 100644
--- a/examples/tungsten_extxyz/uf23_potential_demo.ipynb
+++ b/examples/tungsten_extxyz/uf23_potential_demo.ipynb
@@ -143,7 +143,7 @@
     " Step         | Estimated Time \n",
     ":-------------|:--------------\n",
     "Preprocessing | 10 seconds\n",
-    "Featurization | 2.5 core-hours (parallelizable)\n",
+    "Featurization | 20 core-minutes (parallelizable)\n",
     "Training      | 4 seconds\n",
     "Prediction    | 3 seconds\n",
     "Plotting      | 10 seconds"
@@ -278,7 +278,7 @@
    "source": [
     "**Note: the demo's resolution and cutoffs (3.5-3.5-7.0Å, 6-6-12) are small to reduce runtime and filesize.**\n",
     "\n",
-    "**Results in the manuscript use (4.25-4.25-8.5Å, 10-10-20), requiring about 4 core-hours and 6 gb.**"
+    "**Results in the manuscript use (4.25-4.25-8.5Å, 10-10-20), requiring about 20 core-minutes and 6 gb.**"
    ]
   },
   {
diff --git a/uf3/forcefield/calculator.py b/uf3/forcefield/calculator.py
index 1742e378..8525cec6 100644
--- a/uf3/forcefield/calculator.py
+++ b/uf3/forcefield/calculator.py
@@ -225,16 +225,20 @@ def _energy_3b(self,
         dist_matrix, i_where, j_where = angles.identify_ij(atoms,
                                                            knot_sets,
                                                            supercell)
-        triplet_generator = angles.generate_triplets(
-            i_where, j_where, sup_comp, hashes, dist_matrix, knot_sets)
-
-        for triplet_batch in triplet_generator:
+        i_values, i_groups = angles.group_idx_by_center(i_where, j_where)
+
+        for i_value, i_group in zip(i_values, i_groups):
+            triplet_batch = angles.generate_triplets(i_value, i_group,
+                                                     sup_comp, hashes,
+                                                     dist_matrix,
+                                                     knot_sets,
+                                                     len(atoms))
             for interaction_idx in range(n_interactions):
                 interaction_data = triplet_batch[interaction_idx]
                 if interaction_data is None:
                     continue
                 spline = self.trio_potentials[trio_list[interaction_idx]]
-                i, r_l, r_m, r_n, ituples = interaction_data
+                r_l, r_m, r_n, ituples = interaction_data
                 component = spline(np.vstack([r_l, r_m, r_n]).T)
                 energy += np.sum(component)
         return energy
@@ -301,16 +305,19 @@ def _forces_3b(self, atoms, supercell):
                                                               knot_sets,
                                                               supercell,
                                                               square=True)
-        triplet_generator = angles.generate_triplets(
-            x_where, y_where, sup_comp, hashes, matrix, knot_sets)
+        i_values, i_groups = angles.group_idx_by_center(x_where, y_where)
 
-        for triplet_batch in triplet_generator:
+        for i_value, i_group in zip(i_values, i_groups):
+            triplet_batch = angles.generate_triplets(i_value, i_group,
+                                                    sup_comp, hashes,
+                                                    matrix, knot_sets,
+                                                    n_atoms)
             for interaction_idx in range(n_interactions):
                 interaction_data = triplet_batch[interaction_idx]
                 if interaction_data is None:
                     continue
                 spline = self.trio_potentials[trio_list[interaction_idx]]
-                i, r_l, r_m, r_n, ituples = interaction_data
+                r_l, r_m, r_n, ituples = interaction_data
                 drij_dr = distances.compute_direction_cosines(coords,
                                                               matrix,
                                                               ituples[:, 0],
diff --git a/uf3/representation/angles.py b/uf3/representation/angles.py
index 6eac2d5e..894f4e82 100644
--- a/uf3/representation/angles.py
+++ b/uf3/representation/angles.py
@@ -52,15 +52,16 @@ def featurize_energy_3b(geom: ase.Atoms,
     if len(i_where) == 0:
         return grids
 
-    triplet_generator = generate_triplets(
-        i_where, j_where, sup_comp, hashes, dist_matrix, knot_sets)
+    i_values, i_groups = group_idx_by_center(i_where, j_where)
 
-    for triplet_batch in triplet_generator:
+    for i_group, i_value in zip(i_groups, i_values):
+        triplet_batch = generate_triplets(i_value, i_group, sup_comp, hashes,
+                                          dist_matrix, knot_sets, len(geom))
         for interaction_idx in range(n_interactions):
             interaction_data = triplet_batch[interaction_idx]
             if interaction_data is None:
                 continue
-            i, r_l, r_m, r_n, ituples = interaction_data
+            r_l, r_m, r_n, ituples = interaction_data
             tuples_3b, idx_lmn = evaluate_triplet_distances(
                 r_l,
                 r_m,
@@ -181,16 +182,17 @@ def featurize_force_3b(geom: ase.Atoms,
                    for i in range(n_interactions)]
     if len(x_where) == 0:
         return force_grids
-    # process each atom's neighbors to limit memory requirement
-    triplet_generator = generate_triplets(
-        x_where, y_where, sup_comp, trio_hashes, matrix, knot_sets)
 
-    for triplet_batch in triplet_generator:
+    i_values, i_groups = group_idx_by_center(x_where, y_where)
+
+    for i_group, i_value in zip(i_groups, i_values):
+        triplet_batch = generate_triplets(i_value, i_group, sup_comp, trio_hashes,
+                                            matrix, knot_sets, n_atoms)
         for interaction_idx in range(n_interactions):
             interaction_data = triplet_batch[interaction_idx]
             if interaction_data is None:
                 continue
-            i, r_l, r_m, r_n, ituples = interaction_data
+            r_l, r_m, r_n, ituples = interaction_data
 
             tuples_3b, idx_lmn = evaluate_triplet_derivatives(
                 r_l,
@@ -310,6 +312,16 @@ def identify_ij(geom: ase.Atoms,
     knots_flat = np.concatenate([sequence for set_ in knot_sets
                                  for sequence in set_])
     r_min = max(np.min(knots_flat), 0)
+                
+    # For r_max, we need to consider the largest cutoff distance of all
+    # interactions involving the center atom.
+    # Given an n-body interaction, the number of distances in an n-tuplet is
+    # n(n-1)/2, which is the length of set_. Working backwards,
+    # n = (1+sqrt(1+8*len(set_)))/2. The distances involving the center atom
+    # is one less than this.
+    knots_flat = np.concatenate([sequence for set_ in knot_sets
+                                 for sequence in
+                                 set_[:int((1+np.sqrt(1+8*len(set_)))/2-1)]])
     r_max = np.max(knots_flat)
     sup_positions = supercell.get_positions()
     geo_positions = geom.get_positions()
@@ -384,23 +396,47 @@ def legacy_generate_triplets(i_where:np.ndarray,
         yield i, r_l, r_m, r_n, tuples
 
 
-def generate_triplets(i_where: np.ndarray,
-                      j_where: np.ndarray,
+def group_idx_by_center(i_where: np.ndarray,
+                        j_where: np.ndarray,
+                        ) -> Tuple[np.ndarray, List]:
+    """
+    Group neighbor atom indices by center atom, effectively creating a neighbor
+    list in a list format.
+
+    Args:
+        i_where (np.ndarray): sorted "i" indices (return value from 
+            `identify_ij`)
+        j_where (np.ndarray): sorted "j" indices (return value from 
+            `identify_ij`)
+
+    Returns:
+        i_values (np.ndarray[np.int_]): unique center atom indices
+        i_groups (List[np.ndarray]): list of neighbor indices grouped by center
+            atom
+    """
+    # find unique values of i
+    i_values, group_sizes = np.unique(i_where, return_counts=True)
+    # group j by values of i
+    i_groups = np.array_split(j_where, np.cumsum(group_sizes)[:-1])
+    return i_values, i_groups
+
+
+def generate_triplets(i_value: int,
+                      i_group: np.ndarray,
                       sup_composition: np.ndarray,
                       hashes: np.ndarray,
                       distance_matrix: np.ndarray,
-                      knot_sets: List[List[np.ndarray]]
+                      knot_sets: List[List[np.ndarray]],
+                      n_atoms: int,
                       ) -> List[Tuple]:
     """
-    Identify unique "i-j-k" tuples by combining provided i-j pairs, then
-    compute i-j, i-k, and j-k pair distances from i-j-k tuples,
-        distance matrix, and knot sequence for cutoffs.
-
-    TODO: refactor to break up into smaller, reusable functions
+    For a given center atom and its neighbors, identify unique "i-j-k" tuples
+    by combining provided i-j pairs, then compute i-j, i-k, and j-k pair
+    distances from i-j-k tuples, distance matrix, and knot sequence for cutoffs.
 
     Args:
-        i_where (np.ndarray): sorted "i" indices
-        j_where (np.ndarray): sorted "j" indices
+        i_value (int): center atom index
+        j_where (np.ndarray): array of neighbor indices of `i_value`
         sup_composition (np.ndarray): composition given by atomic numbers.
         hashes (np.ndarray): array of unique integer hashes for interactions.
         distance_matrix (np.ndarray): pair distance matrix.
@@ -409,69 +445,73 @@ def generate_triplets(i_where: np.ndarray,
     Returns:
         tuples_idx (np.ndarray): array of shape (n_triangles, 3)
     """
-    n_hashes = len(hashes)
-    # find unique values of i (sorted such that i < j)
-    i_values, group_sizes = np.unique(i_where, return_counts=True)
-    # group j by values of i
-    i_groups = np.array_split(j_where, np.cumsum(group_sizes)[:-1])
+    grouped_triplets = [None] * len(hashes)  # initialize return value
+
     # generate j-k combinations
-    for i in range(len(i_groups)):
-        j_arr, k_arr = np.meshgrid(i_groups[i], i_groups[i])
-
-        # Pick out unique neighbor pairs of central atom i
-        # ex: With center atom 0 and its neighbors [2, 1, 3],
-        # j_arr = [[2, 1, 3],
-        #          [2, 1, 3],
-        #          [2, 1, 3]]
-        # k_arr = [[2, 2, 2],
-        #          [1, 1, 1],
-        #          [3, 3, 3]]
-        # j_indices = [1, 2, 1]
-        # k_indices = [2, 3, 3]
-        # => unique pairs: (1, 2), (2, 3), (1, 3)
-        # The unique_pair_mask has filtered out pairs like (1, 1), (2, 1), (3, 2), etc.
-        unique_pair_mask = (j_arr < k_arr)
-        j_indices = j_arr[unique_pair_mask]
-        k_indices = k_arr[unique_pair_mask]
-        tuples = np.vstack((i_values[i] * np.ones(len(j_indices), dtype=int),
-                            j_indices, k_indices)).T  # array of unique triplets
-
-        comp_tuples = sup_composition[tuples]
-
-        sort_indices = np.argsort(comp_tuples[:, 1:],axis=1)  # to sort by atomic number
-        comp_tuples_slice = np.take_along_axis(comp_tuples[:, 1:],sort_indices,axis=1)
-        tuples_slice = np.take_along_axis(tuples[:, 1:],sort_indices,axis=1)
-
-        # sort comp_tuples and tuples the same way
-        comp_tuples = np.hstack((comp_tuples[:, [0]], comp_tuples_slice))
-        tuples = np.hstack((tuples[:, [0]], tuples_slice))
-
-        ijk_hash = composition.get_szudzik_hash(comp_tuples)
-
-        grouped_triplets = [None] * n_hashes
-        for j, hash_ in enumerate(hashes):
-            ituples = tuples[ijk_hash == hash_]
-            if len(ituples) == 0:
-                grouped_triplets[j] = None
-                continue
-            # extract distance tuples
-            r_l = distance_matrix[ituples[:, 0], ituples[:, 1]]
-            r_m = distance_matrix[ituples[:, 0], ituples[:, 2]]
-            r_n = distance_matrix[ituples[:, 1], ituples[:, 2]]
-            # mask by longest distance
-            l_mask = ((r_l >= knot_sets[j][0][0])
-                      & (r_l <= knot_sets[j][0][-1]))
-            m_mask = ((r_m >= knot_sets[j][1][0])
-                      & (r_m <= knot_sets[j][1][-1]))
-            n_mask = ((r_n >= knot_sets[j][2][0])
-                      & (r_n <= knot_sets[j][2][-1]))
-            dist_mask = np.logical_and.reduce([l_mask, m_mask, n_mask])
-            r_l = r_l[dist_mask]
-            r_m = r_m[dist_mask]
-            r_n = r_n[dist_mask]
-            ituples = ituples[dist_mask]
-            grouped_triplets[j] = i, r_l, r_m, r_n, ituples
-        yield grouped_triplets
+    if i_value >= n_atoms:
+        # If center atom is a ghost, guarantee that at least one neighbor is a real atom
+        # In the current UF3 code, the supercell is generated such that the ghost atoms
+        # have indices greater than or equal to n_atoms.
+        i_group_filtered = i_group[i_group < n_atoms]
+        if i_group_filtered.size == 0:
+            return grouped_triplets
+    else:
+        i_group_filtered = i_group
+    j_arr, k_arr = np.meshgrid(i_group_filtered, i_group)
+
+    # Pick out unique neighbor pairs of central atom i
+    # ex: With center atom 0 and its neighbors [2, 1, 3],
+    # j_arr = [[2, 1, 3],
+    #          [2, 1, 3],
+    #          [2, 1, 3]]
+    # k_arr = [[2, 2, 2],
+    #          [1, 1, 1],
+    #          [3, 3, 3]]
+    # j_indices = [1, 2, 1]
+    # k_indices = [2, 3, 3]
+    # => unique pairs: (1, 2), (2, 3), (1, 3)
+    # The unique_pair_mask has filtered out pairs like (1, 1), (2, 1), (3, 2), etc.
+    unique_pair_mask = (j_arr < k_arr)
+    j_indices = j_arr[unique_pair_mask]
+    k_indices = k_arr[unique_pair_mask]
+    tuples = np.vstack((i_value * np.ones(len(j_indices), dtype=int),
+                        j_indices, k_indices)).T  # array of unique triplets
+
+    comp_tuples = sup_composition[tuples]
+
+    sort_indices = np.argsort(comp_tuples[:, 1:],axis=1)  # to sort by atomic number
+    comp_tuples_slice = np.take_along_axis(comp_tuples[:, 1:],sort_indices,axis=1)
+    tuples_slice = np.take_along_axis(tuples[:, 1:],sort_indices,axis=1)
+
+    # sort comp_tuples and tuples the same way
+    comp_tuples = np.hstack((comp_tuples[:, [0]], comp_tuples_slice))
+    tuples = np.hstack((tuples[:, [0]], tuples_slice))
+
+    ijk_hash = composition.get_szudzik_hash(comp_tuples)
+
+    for j, hash_ in enumerate(hashes):
+        ituples = tuples[ijk_hash == hash_]
+        if len(ituples) == 0:
+            grouped_triplets[j] = None
+            continue
+        # extract distance tuples
+        r_l = distance_matrix[ituples[:, 0], ituples[:, 1]]
+        r_m = distance_matrix[ituples[:, 0], ituples[:, 2]]
+        r_n = distance_matrix[ituples[:, 1], ituples[:, 2]]
+        # mask by longest distance
+        l_mask = ((r_l >= knot_sets[j][0][0])
+                    & (r_l <= knot_sets[j][0][-1]))
+        m_mask = ((r_m >= knot_sets[j][1][0])
+                    & (r_m <= knot_sets[j][1][-1]))
+        n_mask = ((r_n >= knot_sets[j][2][0])
+                    & (r_n <= knot_sets[j][2][-1]))
+        dist_mask = np.logical_and.reduce([l_mask, m_mask, n_mask])
+        r_l = r_l[dist_mask]
+        r_m = r_m[dist_mask]
+        r_n = r_n[dist_mask]
+        ituples = ituples[dist_mask]
+        grouped_triplets[j] = r_l, r_m, r_n, ituples
+    return grouped_triplets
 
 
 def evaluate_triplet_distances(r_l: np.ndarray,
diff --git a/uf3/representation/bspline.py b/uf3/representation/bspline.py
index 0517577a..c3660cce 100644
--- a/uf3/representation/bspline.py
+++ b/uf3/representation/bspline.py
@@ -176,7 +176,8 @@ def get_cutoff(self):
             if isinstance(r_max, (float, np.floating, int, np.int64)):
                 values.append(r_max)
             else:
-                values.append(r_max[0])
+                # higher body interactions: get max r involving central atom
+                values.append( max(r_max[:len(interaction)-1]) )
         return max(values)
 
     def update_knots(self,
diff --git a/uf3/representation/distances.py b/uf3/representation/distances.py
index 9efa23cb..b5f756aa 100644
--- a/uf3/representation/distances.py
+++ b/uf3/representation/distances.py
@@ -112,6 +112,13 @@ def derivatives_by_interaction(geom: ase.Atoms,
     # cutoff distance of atoms in the unit cell.
     supercell = mask_supercell_with_radius(geom, supercell, r_cut)
     distance_matrix = get_distance_matrix(supercell, supercell)
+
+    # the bottom right block where both i and j are ghost atoms are unnecessary
+    n_supercell = len(supercell)
+    real_row_idx = np.arange(n_supercell).reshape(n_supercell, 1)
+    real_col_idx = np.arange(n_supercell).reshape(1, n_supercell)
+    real_mask = (real_row_idx < n_atoms) | (real_col_idx < n_atoms)
+
     sup_positions = supercell.get_positions()
     sup_composition = np.array(supercell.get_atomic_numbers())
     # loop through interactions
@@ -126,7 +133,7 @@ def derivatives_by_interaction(geom: ase.Atoms,
                                                     sup_composition)
         cut_mask = (distance_matrix > r_min) & (distance_matrix < r_max)
         # valid distances across configuration
-        interaction_mask = comp_mask & cut_mask
+        interaction_mask = real_mask & comp_mask & cut_mask
         distance_map[pair] = distance_matrix[interaction_mask]
         # corresponding derivatives, flattened
         x_where, y_where = np.where(interaction_mask)


Test patch:
diff --git a/tests/data/precalculated_ref/rattled_steel_features.json b/tests/data/precalculated_ref/rattled_steel_features.json
new file mode 100644
index 00000000..d53806ee
--- /dev/null
+++ b/tests/data/precalculated_ref/rattled_steel_features.json
@@ -0,0 +1,20810 @@
+{
+    "energy": [
+        0.0,
+        3.0,
+        8.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.32032130570560646,
+        3.0744661708179404,
+        2.435603376452374,
+        0.16960914702407962,
+        0.13089319090304166,
+        3.1937312866450913,
+        7.022469734309082,
+        2.208814386108232,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.15267295728894625,
+        5.226943841212066,
+        12.65840042322905,
+        6.716692956355414,
+        10.588765173322358,
+        25.490590348905826,
+        23.0788268802679,
+        25.950089659289496,
+        44.61057242515946,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.016983255163669345,
+        2.5340148845361132,
+        16.134097375827142,
+        23.194378968046667,
+        26.404482550456827,
+        51.18058507080134,
+        42.05898491272945,
+        29.506374952778117,
+        0.0,
+        0.0,
+        0.0,
+        1.5237782134324293e-07,
+        4.678962468031235e-05,
+        7.662145632564704e-05,
+        1.691188352134369e-05,
+        0.0,
+        0.0,
+        0.0,
+        0.00022905813911263268,
+        0.005974059379153385,
+        0.007470306282913151,
+        0.0014712616064696702,
+        0.0,
+        0.0,
+        0.0,
+        0.0006998367720728243,
+        0.011563282139700797,
+        0.011629557460627354,
+        0.0020058499718884557,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.00025004501893513694,
+        0.0034189357403626615,
+        0.0029734749686542825,
+        0.0012810369505082632,
+        0.0025763831330357905,
+        0.0042511737439982974,
+        0.0014462854841247627,
+        2.0481649814334085e-05,
+        0.008259681115799842,
+        0.1535455954977475,
+        0.1667889848676805,
+        0.030354016946240302,
+        0.0,
+        0.0,
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+}
\ No newline at end of file
diff --git a/tests/test_bsplines.py b/tests/test_bsplines.py
index a4b9766c..2b03ea67 100644
--- a/tests/test_bsplines.py
+++ b/tests/test_bsplines.py
@@ -569,3 +569,12 @@ def test_force_bspline():
     assert np.any(np.ptp(x, axis=0) > 0)  # but should not be entirely zero
     assert np.ptp(np.sum(x, axis=2)) < 1e-10  # values cancel across b-splines
     assert np.any(np.ptp(x, axis=2) > 0)  # but should not be entirely zero
+
+def test_bsplinebasis_r_cut():
+    element_list = ['Au']
+    chemistry = composition.ChemicalSystem(element_list, degree=3)
+    r_max_map = {('Au', 'Au'): 5.0,
+                 ('Au', 'Au', 'Au'): [5.1, 5.2, 10.3]}
+    bspline_handler = BSplineBasis(chemistry,
+                                   r_max_map=r_max_map)
+    assert bspline_handler.r_cut == 5.2
\ No newline at end of file
diff --git a/tests/test_distances.py b/tests/test_distances.py
index 103bdaaf..588e1912 100644
--- a/tests/test_distances.py
+++ b/tests/test_distances.py
@@ -114,9 +114,9 @@ def test_distance_derivatives(self, simple_unary):
                                                             r_max_map,
                                                             supercell,)
         d_aa = distances[('Au', 'Au')]
-        assert len(d_aa) == 542
+        assert len(d_aa) == 114
         dr_aa = derivatives[('Au', 'Au')]
-        assert dr_aa.shape == (2, 3, 542)
+        assert dr_aa.shape == (2, 3, 114)
 
 
 class TestBinary:
@@ -177,15 +177,15 @@ def test_distance_derivatives(self, simple_binary):
         d_aa = distances[('Ne', 'Ne')]
         d_ab = distances[('Ne', 'Xe')]
         d_bb = distances[('Xe', 'Xe')]
-        assert len(d_aa) == 660
-        assert len(d_ab) == 3102
-        assert len(d_bb) == 2142
+        assert len(d_aa) == 28
+        assert len(d_ab) == 146
+        assert len(d_bb) == 116
         dr_aa = derivatives[('Ne', 'Ne')]
         dr_ab = derivatives[('Ne', 'Xe')]
         dr_bb = derivatives[('Xe', 'Xe')]
-        assert dr_aa.shape == (2, 3, 660)
-        assert dr_ab.shape == (2, 3, 3102)
-        assert dr_bb.shape == (2, 3, 2142)
+        assert dr_aa.shape == (2, 3, 28)
+        assert dr_ab.shape == (2, 3, 146)
+        assert dr_bb.shape == (2, 3, 116)
 
 
 class TestUnaryLegacy:
diff --git a/tests/test_representation.py b/tests/test_representation.py
index fcd83019..333b6a93 100644
--- a/tests/test_representation.py
+++ b/tests/test_representation.py
@@ -1,5 +1,7 @@
 import pytest
 import ase
+import json
+import uf3
 from uf3.representation.process import *
 from uf3.representation import bspline
 from uf3.data import composition
@@ -66,6 +68,24 @@ def methane_structure():
             pbc=True,
             cell=[30, 30, 30])
     yield geom
+
+@pytest.fixture()
+def rattled_steel():
+    geom = ase.Atoms('Fe8C3',
+            positions=[[ 1.99342831e-01,  7.23471398e-02,  2.29537708e-01],
+                       [ 3.27460597e+00,  3.16932506e-03, -9.68273914e-02],
+                       [ 3.65842563e-01,  3.07348695e+00, -1.43894877e-01],
+                       [ 3.02851201e+00,  2.85731646e+00,  6.85404929e-03],
+                       [-1.60754569e-03, -3.82656049e-01,  2.57501643e+00],
+                       [ 2.80754249e+00, -3.02566224e-01,  2.88284947e+00],
+                       [-8.16048151e-02,  2.53753926e+00,  3.26312975e+00],
+                       [ 2.92484474e+00,  2.93350564e+00,  2.58505036e+00],
+                       [ 1.32612346e+00,  1.45718452e+00, -1.80198715e-01],
+                       [ 1.51013960e+00, -7.01277380e-02,  1.37666125e+00],
+                       [-7.03413224e-02,  1.80545564e+00,  1.43230056e+00]],
+            pbc=True,
+            cell=[5.74, 5.74, 5.74])
+    yield geom
     
 
 @pytest.fixture()
@@ -103,6 +123,12 @@ def binary_chemistry_3B():
     chemistry_config = composition.ChemicalSystem(element_list,degree=3)
     yield chemistry_config
 
+@pytest.fixture()
+def rattled_steel_chemistry():
+    element_list = ['Fe', 'C']
+    chemistry_config = composition.ChemicalSystem(element_list, degree=3)
+    yield chemistry_config
+
 @pytest.fixture()
 def methane_chem_config():
     chemistry_config = composition.ChemicalSystem(['H','C'],degree=3)
@@ -447,6 +473,50 @@ def test_evaluate_binary(self, water_chemistry, simple_water):
         assert x.shape == (2 + 6 * 3, 18 * 3 + 2)
         assert np.allclose(y[:10], [1.5, 4, 3, 0, 0, 1, 2, 2, 1, 0])
 
+    def test_evaluate_binary_pbc(self, rattled_steel_chemistry, rattled_steel):
+        n_atoms = len(rattled_steel)
+        r_min_map = {('Fe', 'Fe'): 0.1, ('Fe', 'C'): 0.1, ('C', 'C'): 0.1,
+                     ('Fe', 'Fe', 'Fe'): [1.5, 1.5, 1.5],
+                     ('Fe', 'Fe', 'C'): [1.5, 1.5, 1.5],
+                     ('Fe', 'C', 'C'): [1.5, 1.5, 1.5],
+                     ('C', 'Fe', 'Fe'): [1.5, 1.5, 1.5],
+                     ('C', 'Fe', 'C'): [1.5, 1.5, 1.5],
+                     ('C', 'C', 'C'): [1.5, 1.5, 1.5]}
+        r_max_map = {('Fe', 'Fe'): 6.0, ('Fe', 'C'): 6.0, ('C', 'C'): 6.0,
+                     ('Fe', 'Fe', 'Fe'): [5.0, 5.0, 10.0],
+                     ('Fe', 'Fe', 'C'): [5.0, 5.0, 10.0],
+                     ('Fe', 'C', 'C'): [5.0, 5.0, 10.0],
+                     ('C', 'Fe', 'Fe'): [5.0, 5.0, 10.0],
+                     ('C', 'Fe', 'C'): [5.0, 5.0, 10.0],
+                     ('C', 'C', 'C'): [5.0, 5.0, 10.0]}
+        resolution_map = {('Fe', 'Fe'): 12, ('Fe', 'C'): 12, ('C', 'C'): 12,
+                          ('Fe', 'Fe', 'Fe'): [4, 4, 8],
+                          ('Fe', 'Fe', 'C'): [4, 4, 8],
+                          ('Fe', 'C', 'C'): [4, 4, 8],
+                          ('C', 'Fe', 'Fe'): [4, 4, 8],
+                          ('C', 'Fe', 'C'): [4, 4, 8],
+                          ('C', 'C', 'C'): [4, 4, 8]}
+        bspline_config = bspline.BSplineBasis(rattled_steel_chemistry,
+                                              r_min_map=r_min_map,
+                                              r_max_map=r_max_map,
+                                              resolution_map=resolution_map,
+                                              knot_strategy='linear',
+                                              offset_1b=True,
+                                              leading_trim=0,
+                                              trailing_trim=3,)
+        bspline_handler = BasisFeaturizer(bspline_config)
+        eval_map = bspline_handler.evaluate_configuration(rattled_steel,
+                                                          energy=0,
+                                                          forces=np.zeros((3, n_atoms)))
+        pkg_directory = os.path.dirname(os.path.dirname(uf3.__file__))
+        data_directory = os.path.join(pkg_directory, "tests/data")
+        features_file = os.path.join(data_directory, "precalculated_ref",
+                                  "rattled_steel_features.json") 
+        with open(features_file, 'r') as f:
+            ref_features = json.load(f)
+        for key in eval_map:
+            # keys should be 'energy', 'fx0', 'fx1', ..., 'fx10', 'fy0', ..., 'fz10'
+            assert np.allclose(eval_map[key], np.array(ref_features[key]))
 
 def test_flatten_by_interactions():
     vector_map = {('A', 'A'): np.array([1, 1, 1]),