- Updated change_log

 - Renamed IPL to InversePowerLawPotential in calculator and tests
 - Corrected spelling mistake

ChangeLog.md

@@ -5,7 +5,7 @@ v0.6.0 (10Sep20)
 -----------------------
 
 - Numerical computation of the Hessian matrix 
-- Calculator for polydisperse systems 
+- Calculator for polydisperse systems in which particles interact via a pair potential 
 - Analytic computation of Hessian for polydisperse systems
 - Bug fix in tests eam_calculator_forces_hessian 
 

matscipy/calculators/polydisperse/__init__.py

@@ -21,4 +21,4 @@
 
 from __future__ import absolute_import
 
-from .calculator import IPL, Polydisperse
+from .calculator import InversePowerLawPotential, Polydisperse

matscipy/calculators/polydisperse/calculator.py

@@ -44,10 +44,10 @@
 ###
 
 
-class IPL():
+class InversePowerLawPotential():
     """
     Functional form for a smoothed inverse-power-law potential (IPL)
-    with an exponent of 10.
+    with an repulsive exponent of 10.
 
     Parameters
     ----------
@@ -64,7 +64,7 @@ class IPL():
     q : int
         Smooth the potential up to the q-th derivative.
         For q=0 the potential is smoothed, for q=1 the potential
-        and its first derivative are zero at the cutoff.
+        and its first derivative are zero at the cutoff,...
 
     Reference:
     ----------

tests/polydisperse_calculator.py

@@ -1,4 +1,4 @@
-#! /usr/bin/env pytho
+#! /usr/bin/env python
 
 # ======================================================================
 # matscipy - Python materials science tools
@@ -41,7 +41,7 @@
 
 import matscipytest
 import matscipy.calculators.polydisperse as calculator
-from matscipy.calculators.polydisperse import IPL, Polydisperse
+from matscipy.calculators.polydisperse import InversePowerLawPotential, Polydisperse
 from matscipy.hessian_finite_differences import fd_hessian
 
 ###
@@ -61,15 +61,15 @@ def test_forces_dimer(self):
                                      )
         atomic_configuration.set_array("size", np.array([1.3, 2.22]), dtype=float)
         atomic_configuration.set_masses(masses=np.repeat(1.0, len(atomic_configuration)))
-        calc = Polydisperse(IPL(1.0, 1.4, 0.1, 3, 1, 2.22))
+        calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
         atomic_configuration.set_calculator(calc)
         f = atomic_configuration.get_forces()
         fn = calc.calculate_numerical_forces(atomic_configuration, d=0.0001)
         self.assertArrayAlmostEqual(f, fn, tol=self.tol)
 
     def test_forces_random_structure(self):
         atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
-        calc = Polydisperse(IPL(1.0, 1.4, 0.1, 3, 1, 2.22))
+        calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
         atoms.set_masses(masses=np.repeat(1.0, len(atoms)))       
         atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
         atoms.set_calculator(calc)
@@ -83,7 +83,7 @@ def test_symmetry_sparse(self):
 
         """
         atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
-        calc = Polydisperse(IPL(1.0, 1.4, 0.1, 3, 1, 2.22))
+        calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
         atoms.set_masses(masses=np.repeat(1.0, len(atoms)))       
         atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
         atoms.set_calculator(calc)
@@ -98,7 +98,7 @@ def test_hessian_random_structure(self):
         Test the computation of the Hessian matrix 
         """
         atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
-        calc = Polydisperse(IPL(1.0, 1.4, 0.1, 3, 1, 2.22))
+        calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
         atoms.set_masses(masses=np.repeat(1.0, len(atoms)))       
         atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
         atoms.set_calculator(calc)
@@ -118,7 +118,7 @@ def test_hessian_divide_by_masses(self):
         atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
         masses_n = np.random.randint(1, 10, size=len(atoms))
         atoms.set_masses(masses=masses_n)
-        calc = Polydisperse(IPL(1.0, 1.4, 0.1, 3, 1, 2.22))
+        calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
         atoms.set_calculator(calc)
         dyn = FIRE(atoms)
         dyn.run(fmax=1e-5)