add Mesh, NumericalIntegration and MolecularHamiltonianSubTerms

src/nomad_simulations/schema_packages/model_method.py

@@ -1221,3 +1221,16 @@ class DMFT(ModelMethodElectronic):
 
     def normalize(self, archive: 'EntryArchive', logger: 'BoundLogger') -> None:
         super().normalize(archive, logger)
+
+
+class MolecularHamiltonianSubTerms(BaseModelMethod):
+    type=Quantity(
+        type=MEnum('coulomb', 'exchange'),
+        description="""
+        Typical sub-terms of the molecular hamiltonian.
+        Relativistic effects, SOC, .....
+        """,
+    )
+
+    def normalize(self, archive: 'EntryArchive', logger: 'BoundLogger') -> None:
+        super().normalize(archive, logger)

src/nomad_simulations/schema_packages/numerical_settings.py

@@ -52,66 +52,45 @@ class Smearing(NumericalSettings):
 
 class Mesh(ArchiveSection):
     """
-    A base section used to specify the settings of a sampling mesh.
-    It supports uniformly-spaced meshes and symmetry-reduced representations.
+    A base section used to define the mesh or space partitioning over which a discrete numerical integration is performed.
     """
 
-    spacing = Quantity(
-        type=MEnum('Equidistant', 'Logarithmic', 'Tan'),
-        shape=['dimensionality'],
+    dimensionality = Quantity(
+        type=np.int32,
+        default=3,
         description="""
-        Identifier for the spacing of the Mesh. Defaults to 'Equidistant' if not defined. It can take the values:
-
-        | Name      | Description                      |
-        | --------- | -------------------------------- |
-        | `'Equidistant'`  | Equidistant grid (also known as 'Newton-Cotes') |
-        | `'Logarithmic'`  | log distance grid |
-        | `'Tan'`  | Non-uniform tan mesh for grids. More dense at low abs values of the points, while less dense for higher values |
+        Dimensionality of the mesh: 1, 2, or 3. Defaults to 3.
         """,
     )
 
-    quadrature = Quantity(
-        type=MEnum(
-            'Gauss-Legendre',
-            'Gauss-Laguerre',
-            'Clenshaw-Curtis',
-            'Newton-Cotes',
-            'Gauss-Hermite',
-        ),
+    kind = Quantity(
+        type=MEnum('equidistant', 'logarithmic', 'tan'),
+        shape=['dimensionality'],
         description="""
-        Quadrature rule used for integration of the Mesh. This quantity is relevant for 1D meshes:
+        Kind of mesh identifying the spacing in each of the dimensions specified by `dimensionality`. It can take the values:
 
         | Name      | Description                      |
         | --------- | -------------------------------- |
-        | `'Gauss-Legendre'` | Quadrature rule for integration using Legendre polynomials |
-        | `'Gauss-Laguerre'` | Quadrature rule for integration using Laguerre polynomials |
-        | `'Clenshaw-Curtis'`  | Quadrature rule for integration using Chebyshev polynomials using discrete cosine transformations |
-        | `'Gauss-Hermite'`  | Quadrature rule for integration using Hermite polynomials |
-        """,
-    )  # ! @JosePizarro3 I think that this is separate from the spacing
-
-    n_points = Quantity(
-        type=np.int32,
-        description="""
-        Number of points in the mesh.
+        | `'equidistant'`  | Equidistant grid (also known as 'Newton-Cotes') |
+        | `'logarithmic'`  | log distance grid |
+        | `'Tan'`  | Non-uniform tan mesh for grids. More dense at low abs values of the points, while less dense for higher values |
         """,
     )
 
-    dimensionality = Quantity(
+    grid = Quantity(
         type=np.int32,
-        default=3,
+        shape=['dimensionality'],
         description="""
-        Dimensionality of the mesh: 1, 2, or 3. Defaults to 3.
+        Amount of mesh point sampling along each axis.
         """,
     )
 
-    grid = Quantity(
+    n_points = Quantity(
         type=np.int32,
-        shape=['dimensionality'],
         description="""
-        Amount of mesh point sampling along each axis. See `type` for the axes definition.
+        Number of points in the mesh.
         """,
-    )  # ? @JosePizzaro3: should the mesh also contain its boundary information
+    )
 
     points = Quantity(
         type=np.complex128,
@@ -126,23 +105,73 @@ class Mesh(ArchiveSection):
         shape=['n_points'],
         description="""
         The amount of times the same point reappears. A value larger than 1, typically indicates
-        a symmetry operation that was applied to the `Mesh`. This quantity is equivalent to `weights`:
-
-            multiplicities = n_points * weights
+        a symmetry operation that was applied to the `Mesh`.
         """,
     )
 
-    weights = Quantity(
-        type=np.float64,
-        shape=['n_points'],
+    pruning = Quantity(
+        type=MEnum('fixed', 'adaptive'),
         description="""
-        Weight of each point. A value smaller than 1, typically indicates a symmetry operation that was
-        applied to the mesh. This quantity is equivalent to `multiplicities`:
+        Pruning method applied for reducing the amount of points in the Mesh. This is typically
+        used for numerical integration near the core levels in atoms.
+        In the fixed grid methods, the number of angular grid points is predetermined for
+        ranges of radial grid points, while in the adaptive methods, the angular grid is adjusted
+        on-the-fly for each radial point according to some accuracy criterion.
+        """
+    )
+
+    def normalize(self, archive: 'EntryArchive', logger: 'BoundLogger') -> None:
+        super().normalize(archive, logger)
 
-            weights = multiplicities / n_points
+        if self.dimensionality not in [1, 2, 3]:
+            logger.error('`dimensionality` meshes different than 1, 2, or 3 are not supported.')
+
+
+class NumericalIntegration(NumericalSettings):
+    """
+    Numerical integration settings used to resolve the following type of integrals by discrete
+    numerical integration:
+
+    ```math
+    \int_{\vec{r}_a}^{\vec{r}_b} d^3 \vec{r} F(\vec{r}) \approx \sum_{n=a}^{b} w(\vec{r}_n) F(\vec{r}_n)
+    ```
+
+    Here, $F$ can be any type of function which would define the type of rules that can be applied
+    to solve such integral (e.g., 1D Gaussian quadrature rule or multi-dimensional `angular` rules like the
+    Lebedev quadrature rule).
+
+    These multidimensional integral has a `Mesh` defined over which the integration is performed, i.e., the
+    $\vec{r}_n$ points.
+    """
+
+    coordinate = Quantity(
+        type=MEnum('all', 'radial', 'angular'),
+        description="""
+        Coordinate over which the integration is performed. `all` means the integration is performed in
+        all the space. `radial` and `angular` describe cases where the integration is performed for
+        functions which can be splitted into radial and angular distributions (e.g., orbital wavefunctions).
         """,
     )
 
+    integration_rule = Quantity(
+        type=str,  # ? extend to MEnum?
+        description="""
+        Integration rule used. This can be any 1D Gaussian quadrature rule or multi-dimensional `angular` rules,
+        e.g., Lebedev quadrature rule (see e.g., Becke, Chem. Phys. 88, 2547 (1988)).
+        """
+    )
+
+    weight_partitioning = Quantity(
+        type=str,
+        description="""
+        Approximation applied to the weight when doing the numerical integration.
+        See e.g., C. W. Murray, N. C. Handy
+        and G. J. Laming, Mol. Phys. 78, 997 (1993).
+        """
+    )
+
+    mesh = SubSection(sub_section=Mesh.m_def)
+
     def normalize(self, archive: 'EntryArchive', logger: 'BoundLogger') -> None:
         super().normalize(archive, logger)
 
@@ -908,4 +937,4 @@ class GTOIntegralDecomposition(NumericalSettings):
     )
 
     def normalize(self, archive: 'EntryArchive', logger: 'BoundLogger') -> None:
-        super().normalize(archive, logger)
+        super().normalize(archive, logger)