materialsgenomefoundation · nury12n · Sep 17, 2024 · Sep 17, 2024
diff --git a/examples/01_Binary_Precipitation.ipynb b/examples/01_Binary_Precipitation.ipynb
@@ -146,7 +146,7 @@
       "\tbeta\t0.000e+00\t\t0.0000\t\t0.0000e+00\t5.7737e+03\n",
       "\n",
       "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n",
-      "3675\t1.8e+06\t\t20.0\t\t723\t\t0.0126\n",
+      "3675\t1.8e+06\t\t12.0\t\t723\t\t0.0126\n",
       "\n",
       "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n",
       "\tbeta\t1.374e+22\t\t1.5504\t\t6.1126e-09\t3.2902e+02\n",

diff --git a/examples/02_Multicomponent_Precipitation.ipynb b/examples/02_Multicomponent_Precipitation.ipynb
diff --git a/examples/06_Single_Phase_Diffusion.ipynb b/examples/06_Single_Phase_Diffusion.ipynb
@@ -115,10 +115,10 @@
      "text": [
       "Iteration\tSim Time (h)\tRun time (s)\n",
       "0\t\t0.0e+00\t\t0.0\n",
-      "100\t\t2.9e+01\t\t3.2\n",
-      "200\t\t5.7e+01\t\t6.4\n",
-      "300\t\t8.6e+01\t\t8.7\n",
-      "349\t\t1.0e+02\t\t9.3\n"
+      "100\t\t2.9e+01\t\t2.5\n",
+      "200\t\t5.7e+01\t\t5.2\n",
+      "300\t\t8.6e+01\t\t7.0\n",
+      "349\t\t1.0e+02\t\t7.5\n"
      ]
     }
    ],

diff --git a/examples/09_Thermodynamics.ipynb b/examples/09_Thermodynamics.ipynb
@@ -156,7 +156,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x14345dd6a60>"
+       "<matplotlib.legend.Legend at 0x13a9d934ca0>"
       ]
      },
      "execution_count": 5,
@@ -226,7 +226,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1434b483c40>"
+       "<matplotlib.legend.Legend at 0x13aa2016700>"
       ]
      },
      "execution_count": 6,
@@ -312,7 +312,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1434c889cd0>"
+       "<matplotlib.legend.Legend at 0x13aa4399d60>"
       ]
      },
      "execution_count": 7,
@@ -403,7 +403,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1434c93c340>"
+       "<matplotlib.legend.Legend at 0x13aa45ad310>"
       ]
      },
      "execution_count": 8,

diff --git a/kawin/tests/datasets.py b/kawin/tests/datasets.py
@@ -64,6 +64,62 @@
 PARAMETER DQ(FCC_A1&ZR,*:VA;0) 298.15 -242000; 6000 N !
 """
 
+ALZR_TDB_NO_MOB = """
+$Al-Zr database without any mobility parameters
+$From T. Wang, Z. Jin, J. Zhao, Journal of Phase Equilibria, 22 (2001) p. 544
+$
+TEMP_LIM 298.15 6000 !
+$Element     Standard state       mass [g/mol]     H_298      S_298
+ELEMENT  /-   ELECTRON_GAS        0.0             0.0         0.0 !
+ELEMENT  VA   VACUUM              0.0000E+00      0.0000E+00  0.0000E+00 !
+ELEMENT  AL   FCC_A1              2.6982E+01      4.5773E+03  2.8322E+01 !
+ELEMENT  ZR   HCP_A3              9.1224E+01      5.5663E+03  3.9181E+01 !
+$
+$
+ TYPE_DEFINITION % SEQ *!
+$
+$PHASE AL3ZR
+PHASE AL3ZR % 2 0.75 0.25 !
+CONST AL3ZR : AL : ZR : !
+$
+$PHASE FCC_A1
+PHASE FCC_A1 % 2 1 1 !
+CONST FCC_A1 : AL%,ZR : VA : !
+$
+$
+$
+$
+$UNARY DATA
+$
+$AL (FCC_A1)
+FUNCTION GHSERAL   298.15 -7976.15+137.093038*T-24.3671976*T*LOG(T)
+             -1.884662E-3*T**2-0.877664E-6*T**3+74092*T**(-1);
+         700.00 Y -11276.24+223.048446*T-38.5844296*T*LOG(T)
+              +18.531982E-3*T**2-5.764227E-6*T**3+74092*T**(-1);
+         933.47 Y -11278.378+188.684153*T-31.748192*T*LOG(T)
+                              -1230.524E25*T**(-9);  2900.00 N !
+$
+$ ZIRCONIUM (GHSERZR FOR HCP_A3)
+$
+FUNCTION GHSERZR 130.00 -7827.595+125.64905*T-24.1618*T*LOG(T)
+                            -4.37791E-3*T**2+34971*T**(-1);
+            2128.00 Y -26085.921+262.724183*T-42.144*T*LOG(T)
+                            -1342.895E28*T**(-9); 6000.00 N !
+
+$
+$                                                                       PHASE FCC_A1
+$
+PARAMETER G(FCC_A1,AL:VA;0) 298.15 GHSERAL; 6000.00 N !
+PARAMETER G(FCC_A1,ZR:VA;0) 298.15 7600.00-0.9*T+GHSERZR; 6000.00 N !
+PARAMETER G(FCC_A1,AL,ZR:VA;0)  298.15 -152947+21.3*T;    6000.00  N !
+
+$
+$                                                                       PHASE AL3ZR
+$
+PARAMETER G(AL3ZR,AL:ZR;0)  298.15 -47381 - 24.373*T + 3.894*T*LOG(T)
+                              +0.75*GHSERAL+0.25*GHSERZR;  6000.00  N !
+"""
+
 NICRAL_TDB = """
 
 $ The parameters of the following database follows the publication

diff --git a/kawin/tests/test_thermodynamics.py b/kawin/tests/test_thermodynamics.py
@@ -28,6 +28,9 @@
 AlCrNiTherm.setDFSamplingDensity(2000)
 AlCrNiTherm.setEQSamplingDensity(500)
 
+def test_load_database_without_mobility():
+    therm_nomob = BinaryThermodynamics(ALZR_TDB_NO_MOB, ['AL', 'ZR'], ['FCC_A1', 'AL3ZR'])
+
 def test_DG_binary():
     '''
     Checks value of binary driving force calculation

diff --git a/kawin/thermo/Thermodynamics.py b/kawin/thermo/Thermodynamics.py
@@ -56,6 +56,7 @@ class GeneralThermodynamics:
     '''
 
     gOffset = 1      #Small value to add to precipitate phase for when order/disorder models are used
+    stateVariables = sorted([v.GE, v.N, v.P, v.T], key=str)
 
     def __init__(self, database, elements, phases, drivingForceMethod = 'tangent', parameters = None):
         if isinstance(database, str):
@@ -129,11 +130,11 @@ def _buildThermoModels(self):
         #This may be unnecessary as already disordered phase models will not be affected, but I guess just in case the matrix phase happens to be an ordered solution
         param_keys, _ = extract_parameters(self._parameters)
         self.models = {self.phases[0]: Model(self.db, self.elements, self.phases[0], parameters=param_keys)}
-        self.models[self.phases[0]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str)
+        self.models[self.phases[0]].state_variables = self.stateVariables
 
         for i in range(1, len(self.phases)):
             self.models[self.phases[i]] = ExtraGibbsModel(self.db, self.elements, self.phases[i], parameters=param_keys)
-            self.models[self.phases[i]].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str)
+            self.models[self.phases[i]].state_variables = self.stateVariables
 
         self.phase_records = PhaseRecordFactory(self.db, self.elements, 
                                                        self.models[self.phases[0]].state_variables, 
@@ -169,12 +170,13 @@ def _buildMobilityModels(self):
 
             if len(phase_mob_params[p]) > 0 or len(phase_diff_params[p]) > 0:
                 self.mobModels[p] = MobilityModel(self.db, self.elements, p, parameters=param_keys)
-                self.mobModels[p].state_variables = sorted([v.T, v.P, v.N, v.GE], key=str)
+                self.mobModels[p].state_variables = self.stateVariables
 
         mob_phases = list(self.mobModels.keys())
-        self.mob_phase_records = PhaseRecordFactory(self.db, self.elements, 
-                                                       self.mobModels[mob_phases[0]].state_variables, 
-                                                       self.mobModels, parameters=self._parameters)
+        if len(mob_phases) > 0:
+            self.mob_phase_records = PhaseRecordFactory(self.db, self.elements, 
+                                                        self.stateVariables, 
+                                                        self.mobModels, parameters=self._parameters)
 
         for p in self.mobModels:
             if len(phase_mob_params[p]) > 0: