diff --git a/examples/ChargedPhases.ipynb b/examples/ChargedPhases.ipynb
index f3c6f718e..7dd0b1817 100644
--- a/examples/ChargedPhases.ipynb
+++ b/examples/ChargedPhases.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "da3a3b01-0a48-430c-922b-a4c8490ed1bf",
    "metadata": {
     "execution": {
@@ -23,7 +23,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2700x1800 with 1 Axes>"
       ]
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "10f656ee",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "1701f44a",
    "metadata": {},
    "outputs": [
@@ -78,7 +78,7 @@
        "Text(0.5, 1.0, 'Phase fractions vs T @ X(O) = 0.55')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "e36ddfb1",
    "metadata": {},
    "outputs": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "565bf2b1-6a8f-481b-9364-d7c14acc4ba2",
    "metadata": {
     "execution": {
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "47092ac7-19f0-41f9-ab07-02e5ff33be08",
    "metadata": {
     "execution": {
@@ -190,10 +190,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x23435911340>"
+       "<matplotlib.legend.Legend at 0x1a4e73ac6a0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "bd252708-d84a-4eef-b382-3722abcb5723",
    "metadata": {
     "execution": {
diff --git a/examples/TernaryExamples.ipynb b/examples/TernaryExamples.ipynb
index c1ffa5f88..008cf3375 100644
--- a/examples/TernaryExamples.ipynb
+++ b/examples/TernaryExamples.ipynb
@@ -34,8 +34,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 32.8 s\n",
-      "Wall time: 32.9 s\n"
+      "CPU times: total: 39.7 s\n",
+      "Wall time: 40.4 s\n"
      ]
     },
     {
diff --git a/pycalphad/mapping/plotting.py b/pycalphad/mapping/plotting.py
index f0d8e592c..e61e03518 100644
--- a/pycalphad/mapping/plotting.py
+++ b/pycalphad/mapping/plotting.py
@@ -420,7 +420,7 @@ def plot_ternary(strategy: TernaryStrategy, x: v.StateVariable = None, y: v.Stat
     if ax is None:
         fig, ax = plt.subplots(1, 1, subplot_kw={'projection': "triangular"})
 
-    plot_binary(strategy, x, y, ax, tielines=tielines, label_node=label_nodes, legend_generator=legend_generator, tieline_color=tieline_color, tie_triangle_color=tie_triangle_color, *args, **kwargs)
+    plot_binary(strategy, x, y, ax, tielines=tielines, label_nodes=label_nodes, legend_generator=legend_generator, tieline_color=tieline_color, tie_triangle_color=tie_triangle_color, *args, **kwargs)
     ax.set_xlim([0,1])
     ax.set_ylim([0,1])
 
diff --git a/pycalphad/mapping/strategy/binary_strategy.py b/pycalphad/mapping/strategy/binary_strategy.py
index da4de6309..c1bdf33fe 100644
--- a/pycalphad/mapping/strategy/binary_strategy.py
+++ b/pycalphad/mapping/strategy/binary_strategy.py
@@ -207,3 +207,17 @@ def _determine_start_direction(self, node: Node, exit_point: Point, proposed_dir
                 return exit_point, axis_var, d, av_delta
 
         return None
+    
+    def _process_new_node(self, zpf_line: ZPFLine, new_node: Node):
+        """
+        Post global eq check after finding node to ensure it's not a metastable node
+        """
+        _log.info("Checking if new node is metastable")
+        cs_result = zeq._find_global_min_cs(new_node, system_info=self.system_info, pdens=self.GLOBAL_MIN_PDENS, tol=self.GLOBAL_MIN_TOL, num_candidates=self.GLOBAL_MIN_NUM_CANDIDATES)
+        if cs_result is None:
+            _log.info("Global eq check on new node passed")
+            super()._process_new_node(zpf_line, new_node)
+        else:
+            _log.info("Global eq check failed. New node is metastable. Removing current zpf line.")
+            self.zpf_lines.pop(-1)
+
diff --git a/pycalphad/mapping/strategy/ternary_strategy.py b/pycalphad/mapping/strategy/ternary_strategy.py
index b3002219c..98770f95c 100644
--- a/pycalphad/mapping/strategy/ternary_strategy.py
+++ b/pycalphad/mapping/strategy/ternary_strategy.py
@@ -295,7 +295,7 @@ def _process_new_node(self, zpf_line: ZPFLine, new_node: Node):
         # Do a global equilibrium check before attempting to add the new node
         global_eq_check, test_point = self._check_full_global_equilibrium(new_node, add_global_point_if_false = False)
         if test_point is None:
-            _log.info("Global eq check could not be determined")
+            _log.info("Global eq check on new node could not be determined")
 
             zpf_line.current_delta *= self.DELTA_SCALE
             if zpf_line.current_delta / self.axis_delta[zpf_line.axis_var] < self.MIN_DELTA_RATIO:
@@ -310,12 +310,12 @@ def _process_new_node(self, zpf_line: ZPFLine, new_node: Node):
 
         else:
             if global_eq_check:
-                _log.info("Global eq check passed")
+                _log.info("Global eq check on new node passed")
                 return super()._process_new_node(zpf_line, new_node)
             else:
                 zpf_line_phases = zpf_line.stable_phases_with_multiplicity
                 test_node_phases = test_point.stable_phases_with_multiplicity
-                _log.info(f"Global eq check failed. Comparing zpf line {zpf_line_phases} with node phases {test_node_phases}")
+                _log.info(f"Global eq check failed. New node is metastable. Comparing zpf line {zpf_line_phases} with node phases {test_node_phases}")
                 if len(set(test_node_phases) - set(zpf_line_phases)) == 1:
                     _log.info("Node can be added as a proper node")
                     new_node = self._create_node_from_point(test_point, zpf_line.points[-1], None, None)
@@ -324,7 +324,7 @@ def _process_new_node(self, zpf_line: ZPFLine, new_node: Node):
                     zpf_line.current_delta *= self.DELTA_SCALE
                     if zpf_line.current_delta / self.axis_delta[zpf_line.axis_var] < self.MIN_DELTA_RATIO:
                         zpf_line.status = ZPFState.FAILED
-                        _log.info("Node is added as a starting point")
+                        _log.info("Node is added as a starting point. Removing current zpf line.")
                         new_node = self._create_node_from_point(test_point, None, None, None)
                         if not self.node_queue.add_node(new_node):
                             _log.info(f"Node {new_node.fixed_phases}, {new_node.free_phases} has already been added")
diff --git a/pycalphad/mapping/zpf_equilibrium.py b/pycalphad/mapping/zpf_equilibrium.py
index ea161ffb7..e849f5430 100644
--- a/pycalphad/mapping/zpf_equilibrium.py
+++ b/pycalphad/mapping/zpf_equilibrium.py
@@ -70,15 +70,15 @@ def update_equilibrium_with_new_conditions(point: Point, new_conditions: dict[v.
     new_point = Point(new_conditions, np.array(results.chemical_potentials), [cs for cs in comp_sets if cs.fixed], [cs for cs in comp_sets if not cs.fixed])
     return new_point, orig_cs
 
-def find_global_min_point(point: Point, system_info: dict, pdens = 500, tol = 1e-5, num_candidates = 1):
+def _find_global_min_cs(point: Point, system_info: dict, pdens = 500, tol = 1e-5, num_candidates = 1):
     """
+    Finds potential composition set for global min check
+
     For each possible phase:
         1. Sample DOF and find CS that maximizes driving force
         2. Create a DormantPhase with CS and compute driving force with potentials at equilibrium
             Or check the top N CS that maximizes driving force and compute driving force and take max
-        3. If driving force is positive, then new phase is stable
-        4. Check that the new CS doesn"t match with a currently stable CS
-        4. Hope that this works on miscibility gaps
+        3. If driving force is positive, then new phase may be stable in when attempting to find a new node
 
     Parameters
     ----------
@@ -98,11 +98,11 @@ def find_global_min_point(point: Point, system_info: dict, pdens = 500, tol = 1e
 
     Returns
     -------
-    new_point : Point with added composition set
+    (cs, dG) : (potential new composition set, driving force of composition set)
 
     or
 
-    None : if point is global min or global min was unable to be found
+    None : if no composition set was found below the current chemical potential hyperplane
     """
     dbf = system_info["dbf"]
     comps = system_info["comps"]
@@ -148,6 +148,49 @@ def find_global_min_point(point: Point, system_info: dict, pdens = 500, tol = 1e
     if dG < tol:
         return None
     else:
+        return cs, dG
+    
+def find_global_min_point(point: Point, system_info: dict, pdens = 500, tol = 1e-5, num_candidates = 1):
+    """
+    Checks global min on current point and attempts to find a new point 
+    with a new composition set if current point is not global min
+
+    1. Find potential new composition set for global min
+    2. If no composition set is found, then return
+    3. If composition set is found, check that the new CS doesn't match
+       with a currently stable CS
+    4. Hope that this works on miscibility gaps
+
+    Parameters
+    ----------
+    point : Point
+        Point to check global min
+    system_info : dict
+        Dictionary containing information for pycalphad.calculate
+    pdens : int
+        Sampling density
+    tol : float
+        Tolerance for whether a new CS is considered stable
+    num_candidates : int
+        Number of candidate CS to check driving force
+        To avoid redundant calculations, this will only check driving force
+        for unique phases. So setting this to a high number will not significantly
+        affect performance
+
+    Returns
+    -------
+    new_point : Point with added composition set
+
+    or
+
+    None : if point is global min or global min was unable to be found
+    """
+    min_cs_result = _find_global_min_cs(point, system_info, pdens, tol, num_candidates)
+    if min_cs_result is None:
+        return None
+    else:
+        cs, dG = min_cs_result
+        
         _log.info(f'Global min potentially detected. {point.stable_phases} + {cs.phase_record.phase_name} with dG = {dG}')
         if _detect_degenerate_phase(point, cs):
             new_point = Point(point.global_conditions, point.chemical_potentials, point.fixed_composition_sets, point.free_composition_sets)