diff --git a/src/simmate/apps/badelf/badelf_tools/acf.py b/src/simmate/apps/badelf/badelf_tools/acf.py
deleted file mode 100644
index e36f62b0e..000000000
--- a/src/simmate/apps/badelf/badelf_tools/acf.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import warnings
-from pathlib import Path
-
-import pandas
-from pymatgen.io.vasp import Potcar
-from pymatgen.io.vasp.outputs import Chgcar
-
-
-def ACF(directory: Path = None, filename="ACF.dat"):
-    # grab working directory if one wasn't provided
-    if not directory:
-        directory = Path.cwd()
-
-    # convert to path obj
-    filename = directory / filename
-
-    # open the file, grab the lines, and then close it
-    with filename.open() as file:
-        lines = file.readlines()
-
-    # establish the headers. Note that I ignore the '#' column as this is
-    # just site index.
-    headers = ("x", "y", "z", "charge", "min_dist", "atomic_vol")
-
-    # create a list that we will load data into
-    bader_data = []
-    # The first 2 lines are header and the final 4 lines are the footer. This is always
-    # true so we don't need to iterate through those. The data we want is between the
-    # header and footer so that's what we loop through.
-    for line in lines[2:-4]:
-        # By running strip, we convert the line from a string to a list of
-        # The values are all still strings, so we convert them to int/floats
-        # before saving. I add [1:] because the first value is just '#' which
-        # is site index and we dont need.
-        line_data = [eval(value) for value in line.split()[1:]]
-        # add the line data to our ouput
-        bader_data.append(line_data)
-
-    # convert the list to a pandas dataframe
-    dataframe = pandas.DataFrame(
-        data=bader_data,
-        columns=headers,
-    )
-
-    # Extra data is included in the footer that we can grab too. For each line, the data
-    # is a float that is at the end of the line, hence the split()[-1].
-    extra_data = {
-        "vacuum_charge": float(lines[-3].split()[-1]),
-        "vacuum_volume": float(lines[-2].split()[-1]),
-        "nelectrons": float(lines[-1].split()[-1]),
-    }
-
-    # The remaining code is to analyze the results and calculate extra
-    # information such as the final oxidation states. This requires extra
-    # files to be present, such as from a vasp calculation
-
-    potcar_filename = directory / "POTCAR"
-    chgcar_filename = directory / "CHGCAR"
-    chgcar_empty_filename = directory / "CHGCAR_electride"  # SPECIAL CASE
-
-    # check if the required vasp files are present before doing the workup
-    if potcar_filename.exists() and (
-        chgcar_filename.exists() or chgcar_empty_filename.exists()
-    ):
-        # load the electron counts used by VASP from the POTCAR files
-        # OPTIMIZE: this can be much faster if I have a reference file
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            potcars = Potcar.from_file(potcar_filename)
-        nelectron_data = {}
-        # the result is a list because there can be multiple element potcars
-        # in the file (e.g. for NaCl, POTCAR = POTCAR_Na + POTCAR_Cl)
-        for potcar in potcars:
-            nelectron_data.update({potcar.element: potcar.nelectrons})
-
-        # SPECIAL CASE: in scenarios where empty atoms are added to the structure,
-        # we should grab that modified structure instead of the one from the POSCAR.
-        # the empty file will always take preference
-        if chgcar_empty_filename.exists():
-            chgcar = Chgcar.from_file(chgcar_empty_filename)
-            structure = chgcar.structure
-            # We typically use helium ("He") as the empty atom, so we will
-            # need to add this to our element list for oxidation analysis.
-            # We use 0 for electron count because this is an 'empty' atom, and
-            # not actually Hydrogen
-            nelectron_data.update({"He": 0})
-
-        # otherwise, grab the structure from the CHGCAR
-        # OPTIMIZE: consider grabbing from the POSCAR or CONTCAR for speed
-        else:
-            chgcar = Chgcar.from_file(chgcar_filename)
-            structure = chgcar.structure
-
-        # Calculate the oxidation state of each site where it is simply the
-        # change in number of electrons associated with it from vasp potcar vs
-        # the bader charge I also add the element strings for filtering functionality
-        elements = []
-        oxi_state_data = []
-        for site, site_charge in zip(structure, dataframe.charge.values):
-            element_str = site.specie.name
-            elements.append(element_str)
-            oxi_state = nelectron_data[element_str] - site_charge
-            oxi_state_data.append(oxi_state)
-
-        # add the new column to the dataframe
-        dataframe = dataframe.assign(
-            oxidation_state=oxi_state_data,
-            element=elements,
-        )
-        # !!! There are multiple ways to do this, but I don't know which is best
-        # dataframe["oxidation_state"] = pandas.Series(
-        #     oxi_state_data, index=dataframe.index)
-
-    return dataframe, extra_data
diff --git a/src/simmate/apps/badelf/core/badelf.py b/src/simmate/apps/badelf/core/badelf.py
index a7f7ea569..6b9ac6071 100644
--- a/src/simmate/apps/badelf/core/badelf.py
+++ b/src/simmate/apps/badelf/core/badelf.py
@@ -17,11 +17,11 @@
 from pymatgen.io.vasp import Potcar
 from scipy.constants import Avogadro
 
-from simmate.apps.badelf.badelf_tools.acf import ACF
 from simmate.apps.badelf.core.electride_finder import ElectrideFinder
 from simmate.apps.badelf.core.grid import Grid
 from simmate.apps.badelf.core.partitioning import PartitioningToolkit
 from simmate.apps.badelf.core.voxel_assignment import VoxelAssignmentToolkit
+from simmate.apps.bader.outputs import ACF
 from simmate.workflows.utilities import get_workflow
 
 # BUG: we shouldn't fully turning off warnings. This should be used within a context.