diff --git a/rmgpy/data/kinetics/family.py b/rmgpy/data/kinetics/family.py
index d6cdcca108a..075996d60bf 100644
--- a/rmgpy/data/kinetics/family.py
+++ b/rmgpy/data/kinetics/family.py
@@ -959,6 +959,9 @@ def save_groups(self, path):
             f.write('reactantNum = {0}\n\n'.format(self.reactant_num))
         if self.product_num is not None:
             f.write('productNum = {0}\n\n'.format(self.product_num))
+            
+        if self.auto_generated is not None:
+            f.write('autoGenerated = {0}\n\n'.format(self.auto_generated))
 
         # Write the recipe
         f.write('recipe(actions=[\n')
@@ -3798,10 +3801,12 @@ def fix_labels_mol(mol, root_labels):
             if estimate_thermo:
                 for j, react in enumerate(r.item.reactants):
                     if rxns[i].reactants[j].thermo is None:
+                        react.generate_resonance_structures()
                         rxns[i].reactants[j].thermo = tdb.get_thermo_data(react)
 
                 for j, react in enumerate(r.item.products):
                     if rxns[i].products[j].thermo is None:
+                        react.generate_resonance_structures()
                         rxns[i].products[j].thermo = tdb.get_thermo_data(react)
 
             rxns[i].kinetics = r.data
@@ -3877,7 +3882,9 @@ def fix_labels_mol(mol, root_labels):
                     if estimate_thermo:
                         for r in rrev.reactants:
                             if r.thermo is None:
-                                r.thermo = tdb.get_thermo_data(deepcopy(r))
+                                therm_spc = deepcopy(r)
+                                therm_spc.generate_resonance_structures()
+                                r.thermo = tdb.get_thermo_data(therm_spc)
 
                     rev_rxns.append(rrev)
 
@@ -3916,7 +3923,9 @@ def fix_labels_mol(mol, root_labels):
                 if estimate_thermo:
                     for r in rrev.reactants:
                         if r.thermo is None:
-                            r.thermo = tdb.get_thermo_data(deepcopy(r))
+                            therm_spc = deepcopy(r)
+                            therm_spc.generate_resonance_structures()
+                            r.thermo = tdb.get_thermo_data(therm_spc)
                 rxns[i] = rrev
 
         if self.own_reverse and get_reverse:
diff --git a/rmgpy/test_data/testing_database/kinetics/families/intra_H_migration/groups.py b/rmgpy/test_data/testing_database/kinetics/families/intra_H_migration/groups.py
index d2e499e8a39..720f9d1443c 100644
--- a/rmgpy/test_data/testing_database/kinetics/families/intra_H_migration/groups.py
+++ b/rmgpy/test_data/testing_database/kinetics/families/intra_H_migration/groups.py
@@ -11,6 +11,8 @@
 
 reversible = True
 
+autoGenerated = False
+
 recipe(actions=[
     ['BREAK_BOND', '*2', 1, '*3'],
     ['FORM_BOND', '*1', 1, '*3'],
diff --git a/scripts/generateTree.py b/scripts/generateTree.py
index 34dd51ad552..fc9079cee50 100644
--- a/scripts/generateTree.py
+++ b/scripts/generateTree.py
@@ -73,7 +73,7 @@ def main():
         path=dbdir,
         thermo_libraries=['Klippenstein_Glarborg2016', 'BurkeH2O2', 'thermo_DFT_CCSDTF12_BAC', 'DFT_QCI_thermo',
                          'primaryThermoLibrary', 'primaryNS', 'NitrogenCurran', 'NOx2018', 'FFCM1(-)',
-                         'SulfurLibrary', 'SulfurGlarborgH2S'],
+                         'SulfurLibrary', 'SulfurGlarborgH2S', 'SABIC_aromatics'],
         transport_libraries=[],
         reaction_libraries=[],
         seed_mechanisms=[],