diff --git a/.idea/dictionaries/Titus.xml b/.idea/dictionaries/Titus.xml
index ff0cf04..d2062e7 100644
--- a/.idea/dictionaries/Titus.xml
+++ b/.idea/dictionaries/Titus.xml
@@ -18,6 +18,7 @@
       <w>maxiter</w>
       <w>molality</w>
       <w>ndarray</w>
+      <w>pitzer</w>
       <w>pred</w>
       <w>quah</w>
       <w>rbfopt</w>
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index c433b0b..7e2fcce 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,11 +2,10 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="f4439dc0-6756-4612-8f7d-596d8949f300" name="Default Changelist" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/dictionaries/Titus.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/dictionaries/Titus.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml" beforeDir="false" afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/data/xmls/twophase.xml" beforeDir="false" afterPath="$PROJECT_DIR$/data/xmls/twophase.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/iterative_fitter_monitor.py" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/iterative_fitter_monitor.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/outputs/temp.xml" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/outputs/temp.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/docs/Examples/iterative_fitter.py" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/iterative_fitter.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/llepe/llepe.py" beforeDir="false" afterPath="$PROJECT_DIR$/llepe/llepe.py" afterDir="false" />
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@@ -350,7 +349,14 @@
       <option name="project" value="LOCAL" />
       <updated>1594785960460</updated>
     </task>
-    <option name="localTasksCounter" value="26" />
+    <task id="LOCAL-00026" summary="Added new feature to include custom_object_dicts where user can optimize parameters in the dependent_params_dict.">
+      <created>1594852623912</created>
+      <option name="number" value="00026" />
+      <option name="presentableId" value="LOCAL-00026" />
+      <option name="project" value="LOCAL" />
+      <updated>1594852623912</updated>
+    </task>
+    <option name="localTasksCounter" value="27" />
     <servers />
   </component>
   <component name="Vcs.Log.Tabs.Properties">
@@ -384,7 +390,8 @@
     <MESSAGE value="add outputs folder to examples" />
     <MESSAGE value="updated iterative_fitter_monitor.py" />
     <MESSAGE value="updated iterative_fitter.py to have more feedback" />
-    <option name="LAST_COMMIT_MESSAGE" value="updated iterative_fitter.py to have more feedback" />
+    <MESSAGE value="Added new feature to include custom_object_dicts where user can optimize parameters in the dependent_params_dict." />
+    <option name="LAST_COMMIT_MESSAGE" value="Added new feature to include custom_object_dicts where user can optimize parameters in the dependent_params_dict." />
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@@ -426,46 +433,46 @@
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+    <state width="1899" height="348" key="GridCell.Tab.0.bottom" timestamp="1594852783300">
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@@ -517,11 +524,6 @@
           <line>9</line>
           <option name="timeStamp" value="1" />
         </line-breakpoint>
-        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
-          <url>file://$PROJECT_DIR$/docs/examples/iterative_fitter.py</url>
-          <line>26</line>
-          <option name="timeStamp" value="5" />
-        </line-breakpoint>
       </breakpoints>
       <default-breakpoints>
         <breakpoint type="python-exception">
diff --git a/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml b/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
index 934bb8a..00ea6ee 100644
--- a/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
+++ b/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
@@ -33,7 +33,7 @@
       <thermo>
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
-          <h0 units="J/mol" updated="Updated at 8:35 7-15-2020">-1459900.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-1376877.1544988335</h0>
           <s0 units="J/mol/K"> 558.9824 </s0>
           <cp0 units="J/mol/K"> 0.0</cp0>
         </const_cp>
@@ -50,7 +50,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 8:35 7-15-2020">-5178185.714285715</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4926549.797810851</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -67,7 +67,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 23:12 7-14-2020">-5177400.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4935519.640701385</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -85,7 +85,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 23:48 7-14-2020">-5177400.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4928317.781440989</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -103,7 +103,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 1:32 7-15-2020">-5177400.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4927428.65973482</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -120,7 +120,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 2:1 7-15-2020">-5177400.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4935155.356789877</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -138,7 +138,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 3:3 7-15-2020">-5178028.571428572</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4944228.17930387</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -157,7 +157,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 2:24 7-15-2020">-5177400.0</h0>
+          <h0 units="J/mol" updated="Updated at 20:24 7-15-2020">-4925606.187988869</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -245,10 +245,10 @@
         </binarySaltParameters>
 
 		<binarySaltParameters anion="Cl-" cation="Pr+++">
-          <beta0 updated="Updated at 23:15 7-14-2020"> 1.229318338775545, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 23:15 7-14-2020"> 7.705236327609295, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.5879108393945309, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 5.4483234694357385, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 23:15 7-14-2020"> -0.020355000251503143, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -261,10 +261,10 @@
         </binarySaltParameters>
 		
         <binarySaltParameters anion="Cl-" cation="Nd+++">
-          <beta0 updated="Updated at 8:35 7-15-2020"> 0.5877, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 8:35 7-15-2020"> 5.206, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.7459775851223264, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 7.68392131299453, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 8:35 7-15-2020"> -0.01969, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -277,10 +277,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="La+++">
-          <beta0 updated="Updated at 1:35 7-15-2020"> 1.199616118936196, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 1:35 7-15-2020"> 13.377018086968107, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.5929999713109059, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 5.2769992484445485, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 1:35 7-15-2020"> -0.023995730879246852, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -293,10 +293,10 @@
         </binarySaltParameters>
 		
         <binarySaltParameters anion="Cl-" cation="Dy+++">
-          <beta0 updated="Updated at 2:4 7-15-2020"> 0.6129037619837437, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 2:4 7-15-2020"> 5.359070120380359, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.612950248542597, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 5.3599329374557385, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 2:4 7-15-2020"> -0.01969999915450973, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -309,10 +309,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Ce+++">
-          <beta0 updated="Updated at 23:50 7-14-2020"> 0.4845924897449592, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 23:50 7-14-2020"> 0.49100000000000005, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.6005000182766684, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 4.910000193972841, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 23:50 7-14-2020"> -0.026227452256417736, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -325,10 +325,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Y+++">
-          <beta0 updated="Updated at 3:3 7-15-2020"> 0.6248, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 3:3 7-15-2020"> 5.66, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.9656352678202726, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 8.514180603037651, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 3:3 7-15-2020"> -0.01563, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -341,10 +341,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Sm+++">
-          <beta0 updated="Updated at 2:25 7-15-2020"> 0.5977999158894437, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 2:25 7-15-2020"> 5.278997937589517, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 20:24 7-15-2020"> 0.5977991228263208, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 20:24 7-15-2020"> 5.278978156246855, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 2:25 7-15-2020"> -0.019920000010410537, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 20:24 7-15-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
diff --git a/docs/Examples/iterative_fitter.py b/docs/Examples/iterative_fitter.py
index 9092deb..a91ee43 100644
--- a/docs/Examples/iterative_fitter.py
+++ b/docs/Examples/iterative_fitter.py
@@ -1,36 +1,92 @@
-from scipy.optimize import curve_fit
 import llepe
 import pandas as pd
 import numpy as np
+import json
 
 
-def linear(x, a, b):
-    return a * x + b
+def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
+    new_lp_df = lp_df.copy()
+    index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
+    new_lp_df.at[index, mini_lin_param] = input_val
+    return new_lp_df
 
 
 species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
-pitzer_param_list = ['beta0', 'beta1', 'Cphi']
+pitzer_param_list = ['beta0', 'beta1']
+lin_param_list = ['slope', 'intercept']
 meas_pitzer_param_df = pd.read_csv("../../data/csvs/may_pitzer_params.csv")
+pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
+with open(pitzer_params_filename) as file:
+    pitzer_params_dict = json.load(file)
+ext_h0_filename = "../../data/jsons/min_h0_guess_ext_h0.txt"
+with open(ext_h0_filename) as file:
+    ext_h0_dict = json.load(file)
 labeled_data = pd.read_csv("../../data/csvs/"
                            "zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv")
 exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
 xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
+lin_param_df = pd.read_csv("../../data/csvs"
+                           "/zeroes_removed_min_h0_pitzer_lin_params.csv")
+estimator_params = {'exp_data': exp_data,
+                    'phases_xml_filename': xml_file,
+                    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
+                    'aq_solvent_name': 'H2O(L)',
+                    'extractant_name': '(HA)2(org)',
+                    'diluant_name': 'dodecane',
+                    'complex_names': ['{0}(H(A)2)3(org)'.format(species)
+                                      for species in species_list],
+                    'extracted_species_ion_names': ['{0}+++'.format(species)
+                                                    for species in
+                                                    species_list],
+                    'aq_solvent_rho': 1000.0,
+                    'extractant_rho': 960.0,
+                    'diluant_rho': 750.0,
+                    'temp_xml_file_path': 'outputs/temp.xml',
+                    'objective_function': llepe.lmse_perturbed_obj
+                    }
+estimator = llepe.LLEPE(**estimator_params)
+
+
+def ext_to_complex(h0, custom_obj_dict, mini_species):
+    linear_params = custom_obj_dict['lin_param_df']
+    row = linear_params[linear_params['species'] == mini_species]
+    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]
+
+
+dependant_params_dict = {}
+for species, complex_name in zip(species_list,
+                                 estimator_params['complex_names']):
+    inner_dict = {'upper_element_name': 'species',
+                  'upper_attrib_name': 'name',
+                  'upper_attrib_value': complex_name,
+                  'lower_element_name': 'h0',
+                  'lower_attrib_name': None,
+                  'lower_attrib_value': None,
+                  'input_format': '{0}',
+                  'function': ext_to_complex,
+                  'kwargs': {"mini_species": species},
+                  'independent_params': '(HA)2(org)_h0'}
+    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
+estimator.update_xml(pitzer_params_dict)
+estimator.set_custom_objects_dict({'lin_param_df': lin_param_df})
+estimator.set_dependant_params_dict(dependant_params_dict)
+estimator.update_xml(ext_h0_dict,
+                     dependant_params_dict=dependant_params_dict)
 eps = 1e-4
 mini_eps = 1e-8
-x_guesses = [[-5178500.0, -1459500.0],
-             [-5178342.857142857, -1460300.0],
-             [-5178342.857142857, -1459500.0],
-             [-5178342.857142857, -1458300.0],
-             [-5178185.714285715, -1459900.0],
-             [-5178185.714285715, -1459500.0],
-             [-5178185.714285715, -1459100.0],
-             [-5178185.714285715, -1458300.0],
-             [-5178028.571428572, -1459900.0],
-             [-5178028.571428572, -1459100.0],
-             [-5178028.571428572, -1458300.0],
-             [-5177557.142857143, -1459900.0],
-             [-5177400.0, -1460300.0]]
-pitzer_guess_df = meas_pitzer_param_df.copy()
+pitzer_guess_dict = {'species': [],
+                     'beta0': [],
+                     'beta1': []}
+for species in species_list:
+    pitzer_guess_dict['species'].append(species)
+    for param in pitzer_param_list:
+        mini_dict = pitzer_params_dict['{0}_{1}'.format(species, param)]
+        value = mini_dict['input_value']
+        pitzer_guess_dict[param].append(value)
+pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+ext_h0_guess = ext_h0_dict['(HA)2(org)_h0']['input_value']
+lin_guess_df = lin_param_df.copy()
+
 ignore_list = []
 optimizer = 'scipy_minimize'
 output_dict = {'iter': [0],
@@ -49,127 +105,78 @@ while rel_diff > 1e-4:
     best_obj = 1e20
     output_dict['iter'].append(i)
     for species in species_list:
+        print(species)
         lower_species = species.lower()
-        opt_values = {
-            '(HA)2(org)_h0': [],
-            '{0}(H(A)2)3(org)_h0'.format(species): [],
-            'beta0': [],
-            'beta1': [],
-            'Cphi': [],
-            'obj_value': [],
-            'guess': []}
-        for ind1, x_guess in enumerate(x_guesses):
-            print(ind1)
-            info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
-                                           'upper_attrib_name': 'name',
-                                           'upper_attrib_value': '(HA)2(org)',
-                                           'lower_element_name': 'h0',
-                                           'lower_attrib_name': None,
-                                           'lower_attrib_value': None,
-                                           'input_format': '{0}',
-                                           'input_value': x_guess[1]},
-                         '{0}(H(A)2)3(org)_h0'.format(species): {
-                             'upper_element_name': 'species',
-                             'upper_attrib_name': 'name',
-                             'upper_attrib_value': '{0}(H(A)2)3(org)'.format(
-                                 species),
-                             'lower_element_name': 'h0',
-                             'lower_attrib_name': None,
-                             'lower_attrib_value': None,
-                             'input_format': '{0}',
-                             'input_value': x_guess[0]},
-                         }
-            for pitzer_param in pitzer_param_list:
-                if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
-                    pitzer_row = pitzer_guess_df[
-                        pitzer_guess_df['species'] == species]
-                    inner_dict = {'upper_element_name': 'binarySaltParameters',
-                                  'upper_attrib_name': 'cation',
-                                  'upper_attrib_value':
-                                      '{0}+++'.format(species),
-                                  'lower_element_name': pitzer_param,
-                                  'lower_attrib_name': None,
-                                  'lower_attrib_value': None,
-                                  'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
-                                  'input_value':
-                                      pitzer_row[pitzer_param].values[0]
-                                  }
-                    info_dict['{0}_{1}'.format(
-                        species, pitzer_param)] = inner_dict
-            llepe_params = {
-                'exp_data': exp_data,
-                'phases_xml_filename': xml_file,
-                'opt_dict': info_dict,
-                'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
-                'aq_solvent_name': 'H2O(L)',
-                'extractant_name': '(HA)2(org)',
-                'diluant_name': 'dodecane',
-                'complex_names': ['{0}(H(A)2)3(org)'.format(species)
-                                  for species in species_list],
-                'extracted_species_ion_names': ['{0}+++'.format(species)
-                                                for species in species_list],
-                'aq_solvent_rho': 1000.0,
-                'extractant_rho': 960.0,
-                'diluant_rho': 750.0,
-                'objective_function': llepe.lmse_perturbed_obj,
-                'optimizer': optimizer,
-                'temp_xml_file_path': 'outputs/temp.xml'
-            }
-            estimator = llepe.LLEPE(**llepe_params)
-            estimator.update_xml(llepe_params['opt_dict'])
-            obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}
-            bounds = [(1e-1, 1e1)] * len(info_dict)
-            optimizer_kwargs = {"method": 'l-bfgs-b',
-                                "bounds": bounds}
-            opt_dict, obj_value = estimator.fit(
-                objective_kwargs=obj_kwargs,
-                optimizer_kwargs=optimizer_kwargs)
-            if obj_value < best_obj:
-                best_obj = obj_value
-            keys = list(opt_dict.keys())
-            info1 = [opt_dict[key]['input_value'] for key in keys]
-            info1.append(obj_value)
-            info1.append(x_guess)
-            opt_values_keys = opt_values.keys()
-            for ind, key in enumerate(opt_values_keys):
-                opt_values[key].append(info1[ind])
-        opt_value_df = pd.DataFrame(opt_values)
-        p_opt, p_cov = curve_fit(linear,
-                                 opt_value_df['(HA)2(org)_h0'].values,
-                                 opt_value_df['{0}(H(A)2)3(org)_h0'.format(
-                                     species)].values)
-        slope, intercept = p_opt
-        output_dict['{0}_slope'.format(species)].append(slope)
-        output_dict['{0}_intercept'.format(species)].append(intercept)
-        min_h0_df = opt_value_df[
-            opt_value_df['(HA)2(org)_h0']
-            == opt_value_df['(HA)2(org)_h0'].min()]
-        update_pitzer_dict = {}
-        for pitzer_param in pitzer_param_list:
-            key_name = '{0}_{1}'.format(species, pitzer_param)
-            output_dict[key_name].append(min_h0_df[pitzer_param].values[0])
-
-            inner_dict = {'upper_element_name': 'binarySaltParameters',
-                          'upper_attrib_name': 'cation',
-                          'upper_attrib_value':
-                              '{0}+++'.format(species),
-                          'lower_element_name': pitzer_param,
-                          'lower_attrib_name': None,
-                          'lower_attrib_value': None,
-                          'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
-                          'input_value':
-                              min_h0_df[pitzer_param].values[0]
-                          }
-            update_pitzer_dict['{0}_{1}'.format(
-                species, pitzer_param)] = inner_dict
-        estimator.update_xml(update_pitzer_dict)
+        info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                                       'upper_attrib_name': 'name',
+                                       'upper_attrib_value': '(HA)2(org)',
+                                       'lower_element_name': 'h0',
+                                       'lower_attrib_name': None,
+                                       'lower_attrib_value': None,
+                                       'input_format': '{0}',
+                                       'input_value': ext_h0_guess}}
 
+        for pitzer_param in pitzer_param_list:
+            if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
+                pitzer_row = pitzer_guess_df[
+                    pitzer_guess_df['species'] == species]
+                inner_dict = {'upper_element_name': 'binarySaltParameters',
+                              'upper_attrib_name': 'cation',
+                              'upper_attrib_value':
+                                  '{0}+++'.format(species),
+                              'lower_element_name': pitzer_param,
+                              'lower_attrib_name': None,
+                              'lower_attrib_value': None,
+                              'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
+                              'input_value':
+                                  pitzer_row[pitzer_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, pitzer_param)] = inner_dict
+        for lin_param in lin_param_list:
+            if '{0}_{1}'.format(species, lin_param) not in ignore_list:
+                lin_row = lin_guess_df[lin_guess_df['species'] == species]
+                inner_dict = {'custom_object_name': 'lin_param_df',
+                              'function': mod_lin_param_df,
+                              'kwargs': {'mini_species': species,
+                                         'mini_lin_param': lin_param},
+                              'input_value': lin_row[lin_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, lin_param)] = inner_dict
+        estimator.set_opt_dict(info_dict)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(info_dict)
+        obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}
+        bounds = [(1e-1, 1e1)] * len(info_dict)
+        optimizer_kwargs = {"method": 'l-bfgs-b',
+                            "bounds": bounds}
+        opt_dict, obj_value = estimator.fit(
+            objective_kwargs=obj_kwargs,
+            optimizer_kwargs=optimizer_kwargs)
+        if obj_value < best_obj:
+            best_obj = obj_value
+        keys = list(opt_dict.keys())
+        for lin_param in lin_param_list:
+            mini_dict = opt_dict['{0}_{1}'.format(species, lin_param)] 
+            value = mini_dict['input_value']
+            output_dict['{0}_{1}'.format(species, lin_param)].append(value)
+        for pitzer_param in pitzer_param_list:
+            mini_dict = opt_dict['{0}_{1}'.format(species, pitzer_param)]
+            value = mini_dict['input_value']
+            output_dict['{0}_{1}'.format(species, pitzer_param)].append(value)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(opt_dict)
     pitzer_guess_dict = {'species': [],
                          'beta0': [],
                          'beta1': [],
-                         'Cphi': []}
+                         }
+    lin_guess_dict = {'species': [],
+                      'slope': [],
+                      'intercept:': []}
     for species in species_list:
         pitzer_guess_dict['species'].append(species)
+        lin_guess_dict['species'].append(species)
         for pitzer_param in pitzer_param_list:
             value_list = output_dict['{0}_{1}'.format(species, pitzer_param)]
             value = value_list[-1]
@@ -182,7 +189,20 @@ while rel_diff > 1e-4:
                     np.abs(value_list[-3]))
                 if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
                     ignore_list.append('{0}_{1}'.format(species, pitzer_param))
+        for lin_param in lin_param_list:
+            value_list = output_dict['{0}_{1}'.format(species, lin_param)]
+            value = value_list[-1]
+            lin_guess_dict[lin_param].append(value)
+            if i > 2:
+                mini_rel_diff1 = np.abs(value_list[-1]
+                                        - value_list[-2]) / (
+                                     np.abs(value_list[-2]))
+                mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
+                    np.abs(value_list[-3]))
+                if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
+                    ignore_list.append('{0}_{1}'.format(species, lin_param))
     pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+    lin_guess_df = pd.DataFrame(lin_guess_dict)
 
     output_dict['best_obj'].append(best_obj)
     output_df = pd.DataFrame(output_dict)
@@ -191,4 +211,4 @@ while rel_diff > 1e-4:
     rel_diff = np.sum(np.abs(new_row - old_row) / np.abs(old_row))
     output_dict['rel_diff'].append(rel_diff)
     output_df = pd.DataFrame(output_dict)
-    output_df.to_csv('outputs/iterative_fitter_output_df.csv')
+    output_df.to_csv('outputs/iterative_fitter_output.csv')
diff --git a/llepe/llepe.py b/llepe/llepe.py
index d397ce6..2b49ef7 100644
--- a/llepe/llepe.py
+++ b/llepe/llepe.py
@@ -1105,7 +1105,8 @@ class LLEPE:
         if phases_xml_filename is None:
             phases_xml_filename = self._phases_xml_filename
         new_dict = copy.deepcopy(info_dict)
-        dep_dict = dependant_params_dict
+        dep_dict = copy.deepcopy(dependant_params_dict)
+        custom_objects_dict = copy.deepcopy(self._custom_objects_dict)
 
         if dep_dict is not None:
             new_dict.update(dep_dict)
@@ -1123,10 +1124,12 @@ class LLEPE:
                 ind_vals = [new_dict[ind_param_name]['input_value']
                             for ind_param_name in ind_param_names]
                 if mod_kwargs is None:
-                    new_dict[param_name]['input_value'] = mod_func(ind_vals)
-                else:
                     new_dict[param_name]['input_value'] = \
                         mod_func(ind_vals,
+                                 custom_objects_dict)
+                else:
+                    new_dict[param_name]['input_value'] = \
+                        mod_func(ind_vals, custom_objects_dict,
                                  **mod_kwargs)
         tree = ET.parse(phases_xml_filename)
         root = tree.getroot()