Merge pull request mala-project#561 from RandomDefaultUser/TEM_2D

MALA for 2D materials

docs/source/basic_usage/more_data.rst

@@ -24,9 +24,18 @@ create data for MALA. In order to do so
 Make sure to use enough k-points in the DFT calculation (LDOS sampling
 requires denser k-grids then regular DFT calculations) and an appropriate
 energy grid when calculating the LDOS. See the `initial MALA publication <https://www.doi.org/10.1103/PhysRevB.104.035120>`_
-for more information on this topic. Lastly, when calculating
-the LDOS with ``pp.x``, make sure to set ``use_gauss_ldos=.true.`` in the
-``inputpp`` section.
+for more information on this topic.
+
+Also be aware that due to error cancellation in the total free energy, using
+regular SCF accuracy may be not be sufficient to accurately sample the LDOS.
+If you work with systems which include regions of small electronic density
+(e.g., non-metallic systems, 2D systems, etc.) the MALA team strongly advises
+to reduce the SCF threshold by roughly three orders of magnitude. I.e., if the
+default SCF accuracy in Quantum ESPRESSO is 1e-6, one should use 1e-9 for such
+systems.
+
+Lastly, when calculating the LDOS with ``pp.x``, make sure to set
+``use_gauss_ldos=.true.`` in the ``inputpp`` section.
 
 
 Data conversion

docs/source/basic_usage/predictions.rst

@@ -6,6 +6,12 @@ This guide follows the examples ``ex05_run_predictions.py`` and
 ``ex06_ase_calculator.py``. In the :ref:`advanced section <production>` on
 this topic, performance tweaks and extended access to observables are covered.
 
+.. note::
+    If you are working with a 2D-system, and you have explicitly calculated
+    training data as a 2D-system in Quantum ESPRESSO, make sure to set
+    ``parameters.target.assume_two_dimensional = True`` before any prediction.
+
+
 In order to get direct access to electronic structure via ML, MALA uses
 the ``Predictor`` class. Provided that the trained model was saved with
 all the necessary information on the bispectrum descriptors and the LDOS,

external_modules/total_energy_module/total_energy.f90

@@ -250,11 +250,14 @@ SUBROUTINE init_run_setup(calculate_eigts)
      CALL ggen( dfftp, gamma_only, at, bg, gcutm, ngm_g, ngm, &
        g, gg, mill, ig_l2g, gstart )
   END IF
+
+
+  IF (do_cutoff_2D) CALL cutoff_fact()
+
   !
   !  This seems to be needed by set_rhoc()
   !
   CALL gshells ( lmovecell )
-
   !
   ! ... allocate memory for structure factors
   !

mala/common/parameters.py

@@ -510,6 +510,13 @@ class ParametersTargets(ParametersBase):
 
         kMax : float
             Maximum wave vector up to which to calculate the SSF.
+
+    assume_two_dimensional : bool
+        If True, the total energy calculations will be performed without
+        periodic boundary conditions in z-direction, i.e., the cell will
+        be truncated in the z-direction. NOTE: This parameter may be
+        moved up to a global parameter, depending on whether descriptor
+        calculation may benefit from it.
     """
 
     def __init__(self):
@@ -523,6 +530,7 @@ def __init__(self):
         self.rdf_parameters = {"number_of_bins": 500, "rMax": "mic"}
         self.tpcf_parameters = {"number_of_bins": 20, "rMax": "mic"}
         self.ssf_parameters = {"number_of_bins": 100, "kMax": 12.0}
+        self.assume_two_dimensional = False
 
     @property
     def restrict_targets(self):

mala/targets/density.py

@@ -942,12 +942,21 @@ def __setup_total_energy_module(
             if qe_pseudopotentials is None:
                 qe_pseudopotentials = self.qe_pseudopotentials
 
+            if self.parameters.assume_two_dimensional:
+                qe_input_data["assume_isolated"] = "2D"
+
+                # In the 2D case, the Gamma point approximation introduces
+                # errors in the Ewald and Hartree energy for some reason.
+                kpoints = [1, 1, 1]
+            else:
+                kpoints = self.kpoints
+
             self.write_tem_input_file(
                 atoms_Angstrom,
                 qe_input_data,
                 qe_pseudopotentials,
                 self.grid_dimensions,
-                self.kpoints,
+                kpoints,
             )
 
         # initialize the total energy module.