corrections

Author: sofroniewn

dqc/api/properties.py

@@ -348,7 +348,7 @@ def _hessian_pos(qc: BaseQCCalc) -> torch.Tensor:
     atompos = system.atompos
 
     # check if the atompos requires grad
-    _check_differentiability(atompos, "atom positions", "hessian")
+    _check_differentiability(atompos, "atom positions", "optimal geometry")
 
     # calculate the jacobian
     jac_e_pos = _jac(ene, atompos, create_graph=True)  # (natoms * ndim)
@@ -500,8 +500,8 @@ def _get_energy(atompos: torch.Tensor) -> torch.Tensor:
         return ene
 
     # get the minimal enery position
-    minpos = xitorch.optimize.minimize(_get_energy, atompos, method="gd", maxiter=200,
-                                    step=1e-2)
+    minpos = xitorch.optimize.minimize(_get_energy, atompos, method="gd", maxiter=200, 
+                                       step=1e-2)
 
     return minpos
 

dqc/system/mol.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 from typing import List, Union, Optional, Tuple, Dict
 import warnings
 import torch
@@ -294,7 +295,16 @@ def getparamnames(self, methodname: str, prefix: str = "") -> List[str]:
         else:
             raise KeyError("Unknown methodname: %s" % methodname)
 
-    def make_copy(self, **kwargs) -> "Mol":
+    def make_copy(self, **kwargs) -> Mol:
+        """
+        Returns a copy of the system identical to the orginal expect for new
+        parameters set in the kwargs.
+
+        Arguments
+        ---------
+        kwargs
+            Must be the same kwargs as Mol.
+        """
         # create dictionary of all parameters
         parameters = {
             'moldesc': (self.atomzs, self.atompos),

dqc/system/sol.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 from typing import Optional, Tuple, Union, List, Dict
 import torch
 import numpy as np
@@ -242,7 +243,16 @@ def requires_grid(self) -> bool:
     def getparamnames(self, methodname: str, prefix: str = "") -> List[str]:
         pass
 
-    def make_copy(self, **kwargs) -> "Sol":
+    def make_copy(self, **kwargs) -> Sol:
+        """
+        Returns a copy of the system identical to the orginal expect for new
+        parameters set in the kwargs.
+
+        Arguments
+        ---------
+        kwargs
+            Must be the same kwargs as Sol.
+        """
         # create dictionary of all parameters
         parameters = {
             'soldesc': (self.atomzs, self.atompos),

dqc/test/test_properties.py

@@ -449,18 +449,23 @@ def test_optimal_geometry(h2o_qc):
 
     # create a new h2o with poor initial geometry
     h2o_init = torch.tensor([
-        [0.0, 0.0, 0.215],
+        [0.0, 0.0, 0.214],
         [0.0, 1.475, -0.863],
         [0.0, -1.475, -0.863],
     ], dtype=dtype).requires_grad_()
 
+    # use bond length to assess optimal geometry as they are rotation invariant
+    def bond_length(h2o):
+        # Get the bond lengths of an h20 molecule
+        return torch.stack([(h2o[0] - h2o[1]).norm(), (h2o[0] - h2o[2]).norm()])
+
     # check starting geometry is not optimal
-    assert not torch.allclose(h2o_init, pyscf_h2o_opt, rtol=2e-4)
+    assert not torch.allclose(bond_length(h2o_init), bond_length(pyscf_h2o_opt), rtol=2e-4)
 
     # optimize geometry
     system = h2o_qc.get_system()
     new_system = system.make_copy(moldesc=(system.atomzs, system.atompos))
     new_qc = h2o_qc.__class__(new_system).run()
     h2o_opt = optimal_geometry(new_qc)
 
-    assert torch.allclose(h2o_opt, pyscf_h2o_opt, rtol=2e-4)
+    assert torch.allclose(bond_length(h2o_opt), bond_length(pyscf_h2o_opt), rtol=2e-4)