Add init parameters to Unified LJ example

examples/mliap/mliap_unified_lj_Ar.py

@@ -49,7 +49,7 @@ lmp.commands_string(before_loading)
 # Define the model however you like. In this example
 # we simply import the unified L-J example from mliap
 from lammps.mliap.mliap_unified_lj import MLIAPUnifiedLJ
-unified = MLIAPUnifiedLJ()
+unified = MLIAPUnifiedLJ(["Ar"])
 
 # You can also load the model from a pickle file.
 # import pickle

examples/mliap/pickle_mliap_unified_lj_Ar.py

@@ -5,5 +5,5 @@ from lammps.mliap.mliap_unified_lj import MLIAPUnifiedLJ
 
 
 if __name__ == '__main__':
-    unified = MLIAPUnifiedLJ()
+    unified = MLIAPUnifiedLJ(["Ar"])
     unified.pickle('mliap_unified_lj_Ar.pkl')

python/lammps/mliap/mliap_unified_lj.py

@@ -5,17 +5,17 @@ import numpy as np
 class MLIAPUnifiedLJ(MLIAPUnified):
     """Test implementation for MLIAPUnified."""
 
-    def __init__(self):
+    def __init__(self, element_types, epsilon=1.0, sigma=1.0, rcutfac=1.25):
         super().__init__()
-        self.element_types = ["Ar"]
+        self.element_types = element_types
         self.ndescriptors = 1
         self.nparams = 3
         # Mimicking the LJ pair-style:
         # pair_style lj/cut 2.5
         # pair_coeff * * 1 1
-        self.epsilon = 1.0
-        self.sigma = 1.0
-        self.rcutfac = 1.25
+        self.epsilon = epsilon
+        self.sigma = sigma
+        self.rcutfac = rcutfac
 
     def compute_gradients(self, data):
         """Test compute_gradients."""