diff --git a/docs/examples/mqdt/mqdt_exp_qn.ipynb b/docs/examples/mqdt/mqdt_exp_qn.ipynb
index c7cc2624..33754252 100644
--- a/docs/examples/mqdt/mqdt_exp_qn.ipynb
+++ b/docs/examples/mqdt/mqdt_exp_qn.ipynb
@@ -51,9 +51,9 @@
      "text": [
       "Number of states in basis: 781\n",
       "States in basis:\n",
-      "-0.00011904947676354026*(RadialKet(nu=1.8192961066009463, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5)), 0.8659476823364967*(RadialKet(nu=1.8192961066009463, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=0.5, f_tot=0.5)), -0.5001345791717186*(RadialKet(nu=1.8193076771654273, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=0.0, j_r=0.5, f_tot=0.5))\n",
-      "0.6869979641992598*(RadialKet(nu=1.8389122687565478, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5)), 0.3634927778183226*(RadialKet(nu=1.8389122687565478, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=0.5, f_tot=0.5)), 0.6292112504238875*(RadialKet(nu=1.8389242176447407, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=0.0, j_r=0.5, f_tot=0.5))\n",
-      "1.0*(RadialKet(nu=2.056276554138905, potential=PotentialFei2009Ytterbium171(l_r=2)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=2, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5))\n",
+      "-0.00011904947676346527*(Yb171, RadialKet(nu=1.8192961066009463, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5)), 0.8659476823364968*(Yb171, RadialKet(nu=1.8192961066009463, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=0.5, f_tot=0.5)), -0.5001345791717184*(Yb171, RadialKet(nu=1.8193076771654273, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=0.0, j_r=0.5, f_tot=0.5))\n",
+      "0.6869979641992596*(Yb171, RadialKet(nu=1.8389122687565478, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5)), 0.3634927778183221*(Yb171, RadialKet(nu=1.8389122687565478, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=1.0, j_r=0.5, f_tot=0.5)), 0.629211250423888*(Yb171, RadialKet(nu=1.8389242176447407, potential=PotentialFei2009Ytterbium171(l_r=1)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=1, j_c=0.5, f_c=0.0, j_r=0.5, f_tot=0.5))\n",
+      "1.0*(Yb171, RadialKet(nu=2.056276554138905, potential=PotentialFei2009Ytterbium171(l_r=2)), FJ(i_c=0.5, s_c=0.5, l_c=0, s_r=0.5, l_r=2, j_c=0.5, f_c=1.0, j_r=1.5, f_tot=0.5))\n",
       "... \n"
      ]
     }
@@ -96,13 +96,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "e5b4c143",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -151,28 +151,7 @@
     "\n",
     "# lims\n",
     "ax.set_xlim(0, nu[-1])\n",
-    "\n",
-    "\n",
-    "# annotations\n",
-    "kwargs = {\"arrowprops\": {\"arrowstyle\": \"->\", \"facecolor\": \"black\"}, \"ha\": \"center\"}\n",
-    "\n",
-    "\n",
-    "def get_label(state: rydstate.RydbergStateMQDT, qns: list[str]) -> str:\n",
-    "    label = rf\"$\\left|\\nu={state.nu:.2f}\"\n",
-    "    for qn in qns:\n",
-    "        exp_qn = state.calc_exp_qn(qn)\n",
-    "        label += rf\", {legend_labels[qn]}={exp_qn:.2f}\"\n",
-    "    label += rf\", {legend_labels['f_tot']}=1/2 \\right\\rangle$\"\n",
-    "    return label\n",
-    "\n",
-    "\n",
-    "state = basis.states[97]\n",
-    "label = get_label(state, [\"f_c\"])\n",
-    "ax.annotate(label, xy=(state.nu, state.calc_exp_qn(\"f_c\")), xytext=(30, 1.1), **kwargs)\n",
-    "\n",
-    "state = basis.states[161]\n",
-    "label = get_label(state, [\"j_tot\"])\n",
-    "ax.annotate(label, xy=(state.nu, state.calc_exp_qn(\"j_tot\")), xytext=(110, 1.1), **kwargs)\n",
+    "ax.set_ylim(-0.05, 1.05)\n",
     "\n",
     "fig.tight_layout()\n",
     "plt.show()"
diff --git a/src/rydstate/angular/angular_ket.py b/src/rydstate/angular/angular_ket.py
index 87657bed..c75769c5 100644
--- a/src/rydstate/angular/angular_ket.py
+++ b/src/rydstate/angular/angular_ket.py
@@ -800,6 +800,15 @@ def _calc_prefactor_of_operator_in_coupled_scheme(
         prefactor = calc_prefactor_of_operator_in_coupled_scheme(f1, f2, f_tot, i1, i2, i_tot, kappa, operator_acts_on)
         return prefactor * self._calc_prefactor_of_operator_in_coupled_scheme(other, qn_combined, kappa)
 
+    def get_core_ket(self) -> CoreKet:
+        """Get the core ket corresponding to this ket, j_c and f_c might be unknown dependent on the coupling scheme."""
+        j_c = self.get_qn("j_c", allow_unknown=True)
+        f_c = self.get_qn("f_c", allow_unknown=True)
+        label = self.label
+        if label is None and (is_unknown(f_c) or is_unknown(j_c)):
+            label = Unknown
+        return CoreKet(i_c=self.i_c, s_c=self.s_c, l_c=self.l_c, j_c=j_c, f_c=f_c, label=label)
+
 
 class AngularKetLS(AngularKetBase[GenericT_Unknown], Generic[GenericT_Unknown]):
     """Spin ket in LS coupling."""
@@ -1128,7 +1137,3 @@ def sanity_check(self, msgs: list[str] | None = None) -> None:
             msgs.append(f"{self.f_c=}, {self.j_r=}, {self.f_tot=} don't satisfy spin addition rule.")
 
         super().sanity_check(msgs)
-
-    def get_core_ket(self) -> CoreKet:
-        """Get the core ket corresponding to this FJ ket."""
-        return CoreKet(i_c=self.i_c, s_c=self.s_c, l_c=self.l_c, j_c=self.j_c, f_c=self.f_c, label=self.label)
diff --git a/src/rydstate/angular/utils.py b/src/rydstate/angular/utils.py
index 95a0cdbb..7aeb473e 100644
--- a/src/rydstate/angular/utils.py
+++ b/src/rydstate/angular/utils.py
@@ -130,7 +130,7 @@ def try_trivial_spin_addition(
 ) -> float | Unknown:
     """Try to determine s_tot from s_1 and s_2 if it is not given.
 
-    If s_tot is None and cannot be uniquely determined from s_1 and s_2, raise an error.
+    If s_tot is None and cannot be uniquely determined from s_1 and s_2, return Unknown.
     Otherwise return s_tot or the trivial sum s_1 + s_2.
     """
     if s_tot is not None and not is_unknown(s_tot):
diff --git a/src/rydstate/basis/basis_mqdt.py b/src/rydstate/basis/basis_mqdt.py
index 9fd37d35..d2f335e3 100644
--- a/src/rydstate/basis/basis_mqdt.py
+++ b/src/rydstate/basis/basis_mqdt.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -131,7 +131,7 @@ def _init_states(
         self.states.sort(key=lambda state: state.nu)
 
 
-def get_mqdt_states_from_fmodel(  # noqa: C901
+def get_mqdt_states_from_fmodel(  # noqa: C901, PLR0912
     model: FModel,
     nu_range: tuple[float, float],
     m_range: tuple[float, float] | None | NotSet,
@@ -171,6 +171,15 @@ def get_mqdt_states_from_fmodel(  # noqa: C901
         )
         return []
 
+    coefficients_fj: list[float] = []
+    angular_kets_fj: list[AngularKetFJ[Any]] = []
+    number_kets_fj: list[int] = [0] * len(model.outer_channels)
+    for i, angular_ket in enumerate(model.outer_channels):
+        for coeff_fj, ket_fj in angular_ket.to_state("FJ"):
+            coefficients_fj.append(coeff_fj)
+            angular_kets_fj.append(ket_fj)
+            number_kets_fj[i] += 1
+
     states: list[RydbergStateMQDT] = []
     for nu in nu_list:
         det_mmat = model.calc_det_m_matrix(nu)
@@ -204,16 +213,24 @@ def get_mqdt_states_from_fmodel(  # noqa: C901
                 radial = RadialDummy(1.0, nui)
             radial_kets.append(radial)
 
+        radial_kets_fj = [
+            radial for radial, number in zip(radial_kets, number_kets_fj, strict=True) for _ in range(number)
+        ]
+        coefficients_all = [
+            coeff for coeff, number in zip(coefficients, number_kets_fj, strict=True) for _ in range(number)
+        ]
+        coefficients_all = np.array(coefficients_all) * np.array(coefficients_fj)
+
         energy_au = model.calc_energy_au(nu)
         for m in get_m_range(model.f_tot, m_range):
             rydberg_kets = [
                 RydbergKet(model.species, angular_ket.replace_m(m), radial_ket)
-                for angular_ket, radial_ket in zip(model.outer_channels, radial_kets, strict=True)
+                for angular_ket, radial_ket in zip(angular_kets_fj, radial_kets_fj, strict=True)
             ]
             states.append(
                 RydbergStateMQDT(
                     model.species,
-                    coefficients,
+                    coefficients_all,
                     rydberg_kets,
                     nu=nu,
                     energy_au=energy_au,
diff --git a/src/rydstate/rydberg_state/rydberg_base.py b/src/rydstate/rydberg_state/rydberg_base.py
index df52ac26..57ebf3de 100644
--- a/src/rydstate/rydberg_state/rydberg_base.py
+++ b/src/rydstate/rydberg_state/rydberg_base.py
@@ -69,8 +69,9 @@ def __init__(
             raise ValueError("RydbergState must be initialized with at least one state.")
         if len(coefficients) != len(rydberg_kets):
             raise ValueError("Length of coefficients and rydberg_kets must be the same.")
-        if len(set(rydberg_kets)) != len(rydberg_kets):
-            raise ValueError("RydbergState initialized with duplicate rydberg_kets.")
+        angular_kets = [rydberg_ket.angular for rydberg_ket in rydberg_kets]
+        if len(set(angular_kets)) != len(angular_kets):
+            raise ValueError("RydbergState initialized with duplicate angular kets.")
 
         if abs(self.norm - 1) > 1e-10:
             raise ValueError(
@@ -137,14 +138,16 @@ def to_coupling_scheme(self, coupling_scheme: CouplingScheme) -> RydbergState:
         return RydbergState(self.species, coefficients, rydberg_kets, nu=self.nu, energy_au=self._energy_au)
 
     def _free_memory(self) -> None:
-        """Release the cached radial and angular data to reduce memory usage.
+        """Release the cached radial, angular and core state data to reduce memory usage.
 
-        This drops the references to the (potentially large) radial wavefunctions of the rydberg kets.
+        This drops the references to the (potentially large) radial wavefunctions of the rydberg kets
+        (including the radial wavefunctions of the cached core states).
         After calling this, matrix elements, overlaps and expectation values can no longer be calculated for this state.
         """
         for rydberg_ket in self.rydberg_kets:
             rydberg_ket.__dict__.pop("radial", None)
             rydberg_ket.__dict__.pop("angular", None)
+            rydberg_ket.__dict__.pop("core_state", None)
         self.__dict__.pop("rydberg_kets", None)
 
     @property
diff --git a/src/rydstate/rydberg_state/rydberg_ket.py b/src/rydstate/rydberg_state/rydberg_ket.py
index 47e67d6e..ff189fb6 100644
--- a/src/rydstate/rydberg_state/rydberg_ket.py
+++ b/src/rydstate/rydberg_state/rydberg_ket.py
@@ -2,6 +2,7 @@
 
 import logging
 import math
+from functools import cached_property
 from typing import TYPE_CHECKING, Any, Literal, overload
 
 from rydstate.angular.angular_ket import AngularKetLS
@@ -13,6 +14,7 @@
 if TYPE_CHECKING:
     from rydstate.angular.angular_ket import AngularKetBase
     from rydstate.radial.radial_base import Radial
+    from rydstate.rydberg_state.rydberg_sqdt import RydbergStateSQDT
     from rydstate.units import MatrixElementOperator, PintFloat
 
 
@@ -165,46 +167,47 @@ def _calc_core_radial_matrix_element_au(self, other: RydbergKet, k_radial: int)
 
         The core electron is treated as the low-lying valence electron of the corresponding singly charged SQDT ion
         (e.g. Yb174_ion for Yb174):
-        the Rydberg electron is ignored and the radial matrix element is calculated between the two ion states,
+        the Rydberg electron is ignored and the radial matrix element is calculated between the two core states,
         where the principal quantum number of each core electron is given by the lowest allowed shell of the ion
         for the given l_c.
         """
+        if self.core_state is None or other.core_state is None:
+            return 0.0
+
+        return self.core_state.radial.calc_matrix_element(other.core_state.radial, k_radial, unit="a.u.")
+
+    @cached_property
+    def core_state(self) -> RydbergStateSQDT[Any] | None:
+        """Get the corresponding ion state of the Rydberg ket."""
         from rydstate.rydberg_state.rydberg_sqdt import RydbergStateSQDT  # noqa: PLC0415
 
-        species = self.species
-        ion_species = f"{species}_ion"
+        ion_species = f"{self.species}_ion"
         try:
             ion_sqdt = get_sqdt(ion_species)
         except ValueError:
             logger.warning(
-                "No SQDT data available for the ion species of %s "
-                "returning 0 for the dipole matrix element core contribution.",
-                species,
+                "No SQDT data available for the ion species of %s, "
+                "thus we cannot calculate the ion state and its matrix elements.",
+                self.species,
             )
-            return 0.0
+            return None
 
-        kets = {"self": self, "other": other}
-        ion_states: dict[str, RydbergStateSQDT[Any]] = {}
-        for ket_name, ket in kets.items():
-            l_c = ket.angular.l_c
-            j_c = ket.angular.get_qn("j_c", allow_unknown=True)
-            f_c = ket.angular.get_qn("f_c", allow_unknown=True)
-            if is_unknown(l_c) or is_unknown(j_c) or is_unknown(f_c):
-                return 0.0
+        l_c = self.angular.l_c
+        j_c = self.angular.get_qn("j_c", allow_unknown=True)
+        f_c = self.angular.get_qn("f_c", allow_unknown=True)
+        if is_unknown(l_c) or is_unknown(j_c) or is_unknown(f_c):
+            return None
 
-            angular_ket = AngularKetLS(l_r=l_c, j_tot=j_c, f_tot=f_c, species=ion_species)
+        angular_ket = AngularKetLS(l_r=l_c, j_tot=j_c, f_tot=f_c, species=ion_species)
 
-            # TODO: we should probably also store n_c for the core angular ket in the future
-            # for now, it is correct to assume that the core electron is
-            # in the lowest allowed shell of the ion for the given l_c
-            for n_c in range(l_c + 1, l_c + 15):
-                if ion_sqdt.is_allowed_shell(n_c, l_c, 0.5):
-                    ion_states[ket_name] = RydbergStateSQDT(ion_species, n_c, angular_ket=angular_ket, sqdt=ion_sqdt)
-                    break
-            else:  # no break
-                raise ValueError(f"No allowed shell found for ion species {ion_species} with l_c={l_c}.")
+        # TODO: we should probably also store n_c for the core angular ket in the future
+        # for now, it is correct to assume that the core electron is
+        # in the lowest allowed shell of the ion for the given l_c
+        for n_c in range(l_c + 1, l_c + 15):
+            if ion_sqdt.is_allowed_shell(n_c, l_c, 0.5):
+                return RydbergStateSQDT(ion_species, n_c, angular_ket=angular_ket, sqdt=ion_sqdt)
 
-        return ion_states["self"].radial.calc_matrix_element(ion_states["other"].radial, k_radial, unit="a.u.")
+        raise ValueError(f"No allowed shell found for ion species {ion_species} with l_c={l_c}.")
 
     @overload
     def calc_matrix_element(
diff --git a/src/rydstate/species/fmodel.py b/src/rydstate/species/fmodel.py
index ac26807d..e0884b58 100644
--- a/src/rydstate/species/fmodel.py
+++ b/src/rydstate/species/fmodel.py
@@ -13,7 +13,7 @@
 if TYPE_CHECKING:
     from types import ModuleType
 
-    from rydstate.angular.angular_ket import AngularKetBase, AngularKetFJ
+    from rydstate.angular.angular_ket import AngularKetBase, AngularKetFJ, AngularKetJJ, AngularKetLS
     from rydstate.angular.utils import AllKnown
     from rydstate.species.mqdt import MQDT
     from rydstate.species.utils import RydbergRitzParameters
@@ -40,7 +40,7 @@ class FModel:
     inner_channels: ClassVar[list[AngularKetBase[Any]]]
     """List of inner channels in the MQDT model."""
 
-    outer_channels: ClassVar[list[AngularKetFJ[Any]]]
+    outer_channels: ClassVar[list[AngularKetFJ[Any] | AngularKetJJ[Any] | AngularKetLS[Any]]]
     """List of outer channels in the MQDT model."""
 
     eigen_quantum_defects: ClassVar[list[RydbergRitzParameters]]
@@ -52,12 +52,6 @@ class FModel:
     Each entry is a tuple (i_idx, j_idx, params) where i_idx and j_idx are the indices of the involved channels
     and params are the parameters for the energy dependence of the angle (constant or polynomial coefficients)."""
 
-    manual_frame_transformation_outer_inner: ClassVar[NDArray | None] = None
-    """Optional manually specified frame transformation matrix Q mapping inner to outer channels.
-    This is mainly needed for models with unknown quantum numbers,
-    where the frame transformation cannot (yet) be computed from Wigner coefficients.
-    """
-
     def __init__(self, mqdt: MQDT) -> None:
         self.mqdt = mqdt
         self.element_properties = get_element_properties(self.species)
@@ -176,9 +170,6 @@ def calc_frame_transformation_outer_inner(self) -> NDArray:
             Unitary transformation matrix Q (n_outer, n_inner).
 
         """
-        if self.manual_frame_transformation_outer_inner is not None:
-            return self.manual_frame_transformation_outer_inner
-
         n = len(self.inner_channels)
         u = np.zeros((n, n))
 
diff --git a/src/rydstate/species/mqdt.py b/src/rydstate/species/mqdt.py
index c502cdf7..74f1780f 100644
--- a/src/rydstate/species/mqdt.py
+++ b/src/rydstate/species/mqdt.py
@@ -92,7 +92,11 @@ def reference_ionization_energy_au(self) -> float:
 
     def get_mqdt_models(self, outer_channel: AngularKetFJ[Any]) -> list[FModel]:
         """Return a list of MQDT models for the outer_channel."""
-        models = [model for model in self.models if any(ket == outer_channel for ket in model.outer_channels)]
+        models = [
+            model
+            for model in self.models
+            if any(abs(outer_channel.calc_reduced_overlap(ket)) > 0 for ket in model.outer_channels)
+        ]
         if len(models) == 0:
             models = [FModelSQDT(self.species, outer_channel, mqdt=self)]
         return models
diff --git a/src/rydstate/species/ytterbium/yb171_mqdt_fmodel_data.py b/src/rydstate/species/ytterbium/yb171_mqdt_fmodel_data.py
index dd10cf4e..9a8d8bd0 100644
--- a/src/rydstate/species/ytterbium/yb171_mqdt_fmodel_data.py
+++ b/src/rydstate/species/ytterbium/yb171_mqdt_fmodel_data.py
@@ -31,19 +31,9 @@ class Yb171_S05_HighN(FModel):
     outer_channels = [
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=0, j_r=0.5, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
-        AngularKetFJ(l_c=1, l_r=1, j_c=1.5, f_c=1, j_r=1.5, f_tot=0.5, species="Yb171"),
+        AngularKetJJ(l_c=1, l_r=1, j_c=1.5, j_r=1.5, j_tot=0, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(
-            l_c=1,
-            l_r=1,
-            j_c=0.5,
-            j_r=0.5,
-            f_tot=0.5,
-            parity=1,
-            allow_unknown=True,
-            label="f_c unknown",
-            species="Yb171",
-        ),  # just add two states f_c = 0 and f_c = 1?
+        AngularKetJJ(l_c=1, l_r=1, j_c=0.5, j_r=0.5, j_tot=0, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl c", species="Yb171"),
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=1, j_r=0.5, f_tot=0.5, species="Yb171"),
     ]
@@ -65,17 +55,6 @@ class Yb171_S05_HighN(FModel):
         (2, 4, 0.103123032),
         (0, 5, 0.137753117),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [1 / 2, 0, 0, 0, 0, 0, np.sqrt(3) / 2],
-            [0, 1, 0, 0, 0, 0, 0],
-            [0, 0, np.sqrt(2 / 3), 0, -np.sqrt(1 / 3), 0, 0],
-            [0, 0, 0, 1, 0, 0, 0],
-            [0, 0, np.sqrt(1 / 3), 0, np.sqrt(2 / 3), 0, 0],
-            [0, 0, 0, 0, 0, 1, 0],
-            [np.sqrt(3) / 2, 0, 0, 0, 0, 0, -1 / 2],
-        ]
-    )
 
 
 class Yb171_S15_HighN(FModel):
@@ -255,21 +234,9 @@ class Yb171_D15_HighN(FModel):
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=1, j_r=1.5, f_tot=1.5, species="Yb171"),
         AngularKetFJ(f_tot=1.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
         AngularKetFJ(f_tot=1.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(
-            l_c=1, l_r=1, f_tot=1.5, parity=1, allow_unknown=True, label="j_c and j_r unknown", species="Yb171"
-        ),
+        AngularKetLS(l_c=1, l_r=1, l_tot=2, s_tot=0, j_tot=2, f_tot=1.5, species="Yb171"),
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=0, j_r=1.5, f_tot=1.5, species="Yb171"),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [-np.sqrt(3 / 5), -np.sqrt(2 / 5), 0, 0, 0, 0],
-            [np.sqrt(3 / 5) / 2, -3 / (2 * np.sqrt(10)), 0, 0, 0, np.sqrt(5 / 2) / 2],
-            [0, 0, 1, 0, 0, 0],
-            [0, 0, 0, 1, 0, 0],
-            [0, 0, 0, 0, 1, 0],
-            [-1 / 2, np.sqrt(3 / 2) / 2, 0, 0, 0, np.sqrt(3 / 2) / 2],
-        ]
-    )
 
     eigen_quantum_defects = [
         [0.73056016, -0.108286264, 0],
@@ -309,19 +276,9 @@ class Yb171_D25_HighN(FModel):
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=1, j_r=1.5, f_tot=2.5, species="Yb171"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(l_c=1, l_r=1, f_tot=2.5, allow_unknown=True, label="j_c and j_r unknown", species="Yb171"),
+        AngularKetLS(l_c=1, l_r=1, l_tot=2, s_tot=0, j_tot=2, f_tot=2.5, species="Yb171"),  # "6pnp 1D2"
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=0, j_r=2.5, f_tot=2.5, species="Yb171"),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [np.sqrt(7 / 5) / 2, np.sqrt(7 / 30), 0, 0, 0, -np.sqrt(5 / 3) / 2],
-            [-np.sqrt(2 / 5), np.sqrt(3 / 5), 0, 0, 0, 0],
-            [0, 0, 1, 0, 0, 0],
-            [0, 0, 0, 1, 0, 0],
-            [0, 0, 0, 0, 1, 0],
-            [1 / 2, np.sqrt(1 / 6), 0, 0, 0, np.sqrt(7 / 3) / 2],
-        ]
-    )
 
     eigen_quantum_defects = [
         [0.73056016, -0.108286264, 0],
@@ -623,11 +580,9 @@ class Yb171_S05_LowN(FModel):
     outer_channels = [
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=0, j_r=0.5, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
-        AngularKetFJ(l_c=1, l_r=1, j_c=1.5, f_c=1, j_r=1.5, f_tot=0.5, species="Yb171"),
+        AngularKetJJ(l_c=1, l_r=1, j_c=1.5, j_r=1.5, j_tot=0, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(
-            l_c=1, l_r=1, j_c=0.5, j_r=0.5, f_tot=0.5, allow_unknown=True, label="f_c unknown", species="Yb171"
-        ),  # just add two states f_c = 0 and f_c = 1?
+        AngularKetJJ(l_c=1, l_r=1, j_c=0.5, j_r=0.5, j_tot=0, f_tot=0.5, species="Yb171"),
         AngularKetFJ(f_tot=0.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl c", species="Yb171"),
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=1, j_r=0.5, f_tot=0.5, species="Yb171"),
     ]
@@ -650,18 +605,6 @@ class Yb171_S05_LowN(FModel):
         (0, 5, 0.137709223),
     ]
 
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [1 / 2, 0, 0, 0, 0, 0, np.sqrt(3) / 2],
-            [0, 1, 0, 0, 0, 0, 0],
-            [0, 0, np.sqrt(2 / 3), 0, -np.sqrt(1 / 3), 0, 0],
-            [0, 0, 0, 1, 0, 0, 0],
-            [0, 0, np.sqrt(1 / 3), 0, np.sqrt(2 / 3), 0, 0],
-            [0, 0, 0, 0, 0, 1, 0],
-            [np.sqrt(3) / 2, 0, 0, 0, 0, 0, -1 / 2],
-        ]
-    )
-
 
 class Yb171_S15_LowN(FModel):
     species = "Yb171"
@@ -918,19 +861,9 @@ class Yb171_D15_LowN(FModel):
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=1, j_r=1.5, f_tot=1.5, species="Yb171"),
         AngularKetFJ(f_tot=1.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
         AngularKetFJ(f_tot=1.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(l_c=1, l_r=1, f_tot=1.5, allow_unknown=True, label="j_c and j_r unknown", species="Yb171"),
+        AngularKetLS(l_c=1, l_r=1, l_tot=2, s_tot=0, j_tot=2, f_tot=1.5, species="Yb171"),  # "6pnp 1D2"
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=0, j_r=1.5, f_tot=1.5, species="Yb171"),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [-np.sqrt(3 / 5), -np.sqrt(2 / 5), 0, 0, 0, 0],
-            [np.sqrt(3 / 5) / 2, -3 / (2 * np.sqrt(10)), 0, 0, 0, np.sqrt(5 / 2) / 2],
-            [0, 0, 1, 0, 0, 0],
-            [0, 0, 0, 1, 0, 0],
-            [0, 0, 0, 0, 1, 0],
-            [-1 / 2, np.sqrt(3 / 2) / 2, 0, 0, 0, np.sqrt(3 / 2) / 2],
-        ]
-    )
 
     eigen_quantum_defects = [
         [0.730541589, -0.0967938662, 0],
@@ -970,19 +903,9 @@ class Yb171_D25_LowN(FModel):
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=1, j_r=1.5, f_tot=2.5, species="Yb171"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb171"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb171"),
-        AngularKetFJ(l_c=1, l_r=1, f_tot=2.5, allow_unknown=True, label="j_c and j_r unknown", species="Yb171"),
+        AngularKetLS(l_c=1, l_r=1, l_tot=2, s_tot=0, j_tot=2, f_tot=2.5, species="Yb171"),  # "6pnp 1D2"
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, f_c=0, j_r=2.5, f_tot=2.5, species="Yb171"),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [np.sqrt(7 / 5) / 2, np.sqrt(7 / 30), 0, 0, 0, -np.sqrt(5 / 3) / 2],
-            [-np.sqrt(2 / 5), np.sqrt(3 / 5), 0, 0, 0, 0],
-            [0, 0, 1, 0, 0, 0],
-            [0, 0, 0, 1, 0, 0],
-            [0, 0, 0, 0, 1, 0],
-            [1 / 2, np.sqrt(1 / 6), 0, 0, 0, np.sqrt(7 / 3) / 2],
-        ]
-    )
 
     eigen_quantum_defects = [
         [0.730541589, -0.0967938662, 0],
diff --git a/src/rydstate/species/ytterbium/yb173_mqdt_fmodel_data.py b/src/rydstate/species/ytterbium/yb173_mqdt_fmodel_data.py
index 96d75d72..ae4e850d 100644
--- a/src/rydstate/species/ytterbium/yb173_mqdt_fmodel_data.py
+++ b/src/rydstate/species/ytterbium/yb173_mqdt_fmodel_data.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from rydstate.angular.angular_ket import AngularKetFJ, AngularKetLS
+from rydstate.angular.angular_ket import AngularKetFJ, AngularKetJJ, AngularKetLS
 from rydstate.angular.utils import Unknown
 from rydstate.species.fmodel import FModel
 
@@ -48,25 +48,12 @@ class Yb173_S25_HighN(FModel):
     outer_channels = [
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=2, j_r=0.5, f_tot=2.5, species="Yb173"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb173"),
-        AngularKetFJ(l_c=1, l_r=1, j_c=1.5, f_c=3, j_r=1.5, f_tot=2.5, species="Yb173"),
+        AngularKetJJ(l_c=1, l_r=1, j_c=1.5, j_r=1.5, j_tot=0, f_tot=2.5, species="Yb173"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb173"),
-        AngularKetFJ(
-            l_c=1, l_r=1, j_c=0.5, j_r=0.5, f_tot=2.5, allow_unknown=True, label="f_c unknown", species="Yb173"
-        ),  # just add two states f_c = 2 and f_c = 3?
+        AngularKetJJ(l_c=1, l_r=1, j_c=0.5, j_r=0.5, j_tot=0, f_tot=2.5, species="Yb173"),
         AngularKetFJ(f_tot=2.5, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl c", species="Yb173"),
         AngularKetFJ(l_c=0, l_r=0, j_c=0.5, f_c=3, j_r=0.5, f_tot=2.5, species="Yb173"),
     ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [np.sqrt(5) / 2 / np.sqrt(3), 0, 0, 0, 0, 0, np.sqrt(7) / 2 / np.sqrt(3)],
-            [0, 1, 0, 0, 0, 0, 0],
-            [0, 0, -np.sqrt(2 / 3), 0, np.sqrt(1 / 3), 0, 0],
-            [0, 0, 0, 1, 0, 0, 0],
-            [0, 0, np.sqrt(1 / 3), 0, np.sqrt(2 / 3), 0, 0],
-            [0, 0, 0, 0, 0, 1, 0],
-            [np.sqrt(7) / 2 / np.sqrt(3), 0, 0, 0, 0, 0, -np.sqrt(5) / 2 / np.sqrt(3)],
-        ]
-    )
 
     eigen_quantum_defects = [
         [0.357519763, 0.298712849, 0, 0, 0],
diff --git a/src/rydstate/species/ytterbium/yb174_mqdt_fmodel_data.py b/src/rydstate/species/ytterbium/yb174_mqdt_fmodel_data.py
index 2e7e1cc4..92a43cc1 100644
--- a/src/rydstate/species/ytterbium/yb174_mqdt_fmodel_data.py
+++ b/src/rydstate/species/ytterbium/yb174_mqdt_fmodel_data.py
@@ -219,19 +219,8 @@ class Yb174_D2_HighN(FModel):
         AngularKetFJ(l_c=0, l_r=2, j_c=0.5, j_r=1.5, f_tot=2, species="Yb174"),
         AngularKetFJ(f_tot=2, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl a", species="Yb174"),
         AngularKetFJ(f_tot=2, l_c=Unknown, parity=1, allow_unknown=True, label="4f13 5d 6snl b", species="Yb174"),
-        AngularKetFJ(
-            l_c=1, l_r=1, f_tot=2, allow_unknown=True, label="j_c and j_r unknown", species="Yb174"
-        ),  # Jc could be either 1/2 or 3/2
-    ]
-    manual_frame_transformation_outer_inner = np.array(
-        [
-            [np.sqrt(3 / 5), np.sqrt(2 / 5), 0, 0, 0],
-            [-np.sqrt(2 / 5), np.sqrt(3 / 5), 0, 0, 0],
-            [0, 0, 1, 0, 0],
-            [0, 0, 0, 1, 0],
-            [0, 0, 0, 0, 1],
-        ]
-    )
+        AngularKetLS(l_c=1, l_r=1, l_tot=2, s_tot=0, j_tot=2, species="Yb174"),  # "6pnp 1D2"
+    ]
 
     eigen_quantum_defects = [
         [0.729513646, -0.0377841183],
diff --git a/tests/test_mqdt_models.py b/tests/test_mqdt_models.py
index 78880a30..3441c5b1 100644
--- a/tests/test_mqdt_models.py
+++ b/tests/test_mqdt_models.py
@@ -4,15 +4,12 @@
 
 import numpy as np
 import pytest
-from rydstate.species import FModel, get_mqdt
+from rydstate.angular import AngularKetFJ
+from rydstate.angular.utils import is_unknown
+from rydstate.species import MQDT, FModel, get_all_subclasses, get_element_properties, get_mqdt
 
-
-def _all_fmodels() -> list[FModel]:
-    """Collect all concrete FModel subclasses."""
-    return [cls(get_mqdt(cls.species)) for cls in FModel.__subclasses__() if getattr(cls, "name", None) is not None]
-
-
-ALL_MODELS = _all_fmodels()
+ALL_MODELS = [cls(get_mqdt(cls.species)) for cls in FModel.__subclasses__() if getattr(cls, "name", None) is not None]
+ALL_MQDTS = [cls() for cls in get_all_subclasses(MQDT)]
 
 
 @pytest.fixture(params=ALL_MODELS, ids=lambda cls: cls.full_name)
@@ -20,6 +17,11 @@ def model(request: pytest.FixtureRequest) -> FModel:
     return request.param  # type: ignore[no-any-return]
 
 
+@pytest.fixture(params=ALL_MQDTS, ids=lambda mqdt: f"{mqdt.species}_{mqdt.tag}")
+def mqdt(request: pytest.FixtureRequest) -> MQDT:
+    return request.param  # type: ignore[no-any-return]
+
+
 def test_all_models_discovered() -> None:
     """Sanity check: we should find at least 80 FModel subclasses."""
     assert len(ALL_MODELS) >= 80
@@ -135,6 +137,15 @@ def test_at_least_one_real_channel(model: FModel) -> None:
     assert len(real_channels) >= 1, f"{model.full_name}: no real (non-dummy) channels"
 
 
+@pytest.mark.parametrize("channel_type", ["inner", "outer"])
+def test_channels_are_orthonormal(model: FModel, channel_type: str) -> None:
+    """The channels of a model must form an orthonormal set."""
+    channels = model.inner_channels if channel_type == "inner" else model.outer_channels
+    overlaps = np.array([[ket1.calc_reduced_overlap(ket2) for ket2 in channels] for ket1 in channels])
+    msg = f"{model.full_name}: {channel_type} channels are not orthonormal"
+    np.testing.assert_allclose(overlaps, np.eye(len(channels)), atol=1e-10, err_msg=msg)
+
+
 def test_inner_outer_unitary(model: FModel) -> None:
     """The frame transformation matrix from inner to outer channels must be unitary."""
     unitary = model.calc_frame_transformation_outer_inner()
@@ -148,3 +159,42 @@ def test_inner_outer_unitary(model: FModel) -> None:
     full = model.calc_frame_transformation(nu=30.5)
     msg = f"{model.full_name}: full frame transformation U=QR is not unitary"
     np.testing.assert_allclose(full.conj().T @ full, np.eye(full.shape[0]), atol=1e-10, err_msg=msg)
+
+
+def test_all_models_found_by_get_mqdt_models(mqdt: MQDT) -> None:
+    """Looping over all channels like BasisMQDT._init_models must find every model of the species."""
+    element_properties = get_element_properties(mqdt.species)
+    i_c = element_properties.i_c
+    s_c = element_properties.s_c
+    j_c = s_c
+    s_r = 0.5
+
+    known_l_r = [
+        ch.l_r
+        for model in ALL_MODELS
+        if model.species == mqdt.species
+        for ch in model.outer_channels
+        if not is_unknown(ch.l_r)
+    ]
+    max_l_r = max(known_l_r)
+
+    found_models: list[FModel] = []
+    for l_r in range(max_l_r + 1):
+        for j_r in np.arange(abs(l_r - s_r), l_r + s_r + 1):
+            for f_c in np.arange(abs(j_c - i_c), j_c + i_c + 1):
+                for f_tot in np.arange(abs(f_c - j_r), f_c + j_r + 1):
+                    channel = AngularKetFJ(
+                        l_r=l_r, j_r=float(j_r), f_c=float(f_c), f_tot=float(f_tot), species=mqdt.species
+                    )
+                    for model in mqdt.get_mqdt_models(channel):
+                        if model not in found_models:
+                            found_models.append(model)
+
+    # FModel instances are not cached, so compare the models by their (unique) full_name
+    found_model_names = [model.full_name for model in found_models]
+    missing = [
+        model.full_name
+        for model in ALL_MODELS
+        if model.species == mqdt.species and model.full_name not in found_model_names
+    ]
+    assert not missing, f"{mqdt!r}: {len(missing)} models not reachable via get_mqdt_models: {missing}"
diff --git a/tests/test_mqdt_references.py b/tests/test_mqdt_references.py
new file mode 100644
index 00000000..588e4c55
--- /dev/null
+++ b/tests/test_mqdt_references.py
@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+from rydstate import RydbergStateSQDT
+from rydstate.angular.utils import NotSet
+from rydstate.basis.basis_mqdt import get_mqdt_states_from_fmodel
+from rydstate.species import get_mqdt, get_potential_class
+
+if TYPE_CHECKING:
+    from rydstate.species import FModel
+    from rydstate.units import NDArray
+
+
+def _get_model(species: str, name: str) -> FModel:
+    """Return the model of the given species with the given name."""
+    return next(model for model in get_mqdt(species).models if model.name == name)
+
+
+_YB171_S05 = np.array(
+    [
+        [1 / 2, 0, 0, 0, 0, 0, np.sqrt(3) / 2],
+        [0, 1, 0, 0, 0, 0, 0],
+        [0, 0, np.sqrt(2 / 3), 0, -np.sqrt(1 / 3), 0, 0],
+        [0, 0, 0, 1, 0, 0, 0],
+        [0, 0, np.sqrt(1 / 3), 0, np.sqrt(2 / 3), 0, 0],
+        [0, 0, 0, 0, 0, 1, 0],
+        [np.sqrt(3) / 2, 0, 0, 0, 0, 0, -1 / 2],
+    ]
+)
+_YB171_D25 = np.array(
+    [
+        [np.sqrt(7 / 5) / 2, np.sqrt(7 / 30), 0, 0, 0, -np.sqrt(5 / 3) / 2],
+        [-np.sqrt(2 / 5), np.sqrt(3 / 5), 0, 0, 0, 0],
+        [0, 0, 1, 0, 0, 0],
+        [0, 0, 0, 1, 0, 0],
+        [0, 0, 0, 0, 1, 0],
+        [1 / 2, np.sqrt(1 / 6), 0, 0, 0, np.sqrt(7 / 3) / 2],
+    ]
+)
+_YB174_D2 = np.array(
+    [
+        [np.sqrt(3 / 5), np.sqrt(2 / 5), 0, 0, 0],
+        [-np.sqrt(2 / 5), np.sqrt(3 / 5), 0, 0, 0],
+        [0, 0, 1, 0, 0],
+        [0, 0, 0, 1, 0],
+        [0, 0, 0, 0, 1],
+    ]
+)
+
+# The frame transformations, which were previously hardcoded in the model data files
+# (as manual_frame_transformation_outer_inner, taken from the papers the models are based on).
+# We keep one reference per structurally distinct model: outer channels in JJ coupling (S05),
+# outer channels in LS coupling with i_c != 0 (D25) and with i_c = 0 (D2).
+REFERENCE_FRAME_TRANSFORMATIONS: list[tuple[str, str, NDArray]] = [
+    ("Yb171", "S F=1/2, nu > 26", _YB171_S05),
+    ("Yb171", "S F=1/2, 2 < nu < 26", _YB171_S05),
+    ("Yb171", "D F=5/2, nu > 30", _YB171_D25),
+    ("Yb171", "D F=5/2, 2 < nu < 30", _YB171_D25),
+    ("Yb174", "D J=2, nu > 5", _YB174_D2),
+]
+
+# Experimentally measured Yb174 levels (from the NIST data shipped with the species),
+# which lie inside the nu range of a multi-channel MQDT model: (model name, n, l_r, j_tot, s_tot).
+NIST_LEVELS: list[tuple[str, int, int, float, float]] = [
+    ("S J=0, nu > 2", 7, 0, 0.0, 0.0),  # 6s7s 1S0
+    ("S J=0, nu > 2", 8, 0, 0.0, 0.0),  # 6s8s 1S0
+    ("D J=2, nu > 5", 8, 2, 2.0, 1.0),  # 6s8d 3D2
+]
+
+
+def _equal_up_to_channel_signs(a: NDArray, b: NDArray, atol: float = 1e-10) -> bool:
+    """Check whether a = diag(row_signs) @ b @ diag(col_signs) for some sign vectors row_signs, col_signs.
+
+    Such sign flips only correspond to a different phase convention of the individual inner and outer channel kets.
+    The signs of the columns (inner channels) drop out of K = U Kbar U^T,
+    the signs of the rows (outer channels) only flip the sign of the corresponding channel coefficients.
+    """
+    if not np.allclose(np.abs(a), np.abs(b), atol=atol):
+        return False
+
+    ratios = np.where(np.abs(b) > atol, np.sign(a * b), 0)  # a[i, j] = row_signs[i] * b[i, j] * col_signs[j]
+    row_signs = np.zeros(a.shape[0])
+    col_signs = np.zeros(a.shape[1])
+    for start in range(a.shape[0]):
+        if row_signs[start] != 0:  # already fixed via a previous connected component
+            continue
+        row_signs[start] = 1  # the overall sign of each connected component is arbitrary
+        stack = [start]
+        while stack:  # propagate the sign through the connected component
+            i = stack.pop()
+            for j in np.flatnonzero(ratios[i]):
+                col_signs[j] = ratios[i, j] * row_signs[i]
+                for k in np.flatnonzero(ratios[:, j]):
+                    sign = ratios[k, j] * col_signs[j]
+                    if row_signs[k] == 0:
+                        row_signs[k] = sign
+                        stack.append(int(k))
+                    elif row_signs[k] != sign:
+                        return False
+    return True
+
+
+@pytest.mark.parametrize(("species", "name", "reference"), REFERENCE_FRAME_TRANSFORMATIONS)
+def test_frame_transformation_matches_reference(species: str, name: str, reference: NDArray) -> None:
+    """The calculated frame transformation must match the frame transformation given in the literature.
+
+    The frame transformation is calculated from the overlaps of the inner and outer channel kets,
+    so we check here that it still reproduces the published matrices.
+    The two may only differ by the sign convention of the individual inner and outer channel kets.
+    """
+    model = _get_model(species, name)
+    calculated = model.calc_frame_transformation_outer_inner()
+    assert _equal_up_to_channel_signs(reference, calculated), (
+        f"{model.full_name}: calculated frame transformation does not match the reference\n"
+        f"reference:\n{np.round(reference, 4)}\ncalculated:\n{np.round(calculated, 4)}"
+    )
+
+
+@pytest.mark.parametrize(("name", "n", "l_r", "j_tot", "s_tot"), NIST_LEVELS)
+def test_mqdt_energies_match_nist(name: str, n: int, l_r: int, j_tot: float, s_tot: float) -> None:
+    """The multi-channel models must reproduce the experimentally measured Yb174 levels.
+
+    This checks the whole MQDT pipeline (channel definitions, frame transformation, K-matrix, det(M) roots)
+    against experiment, without relying on any hardcoded numbers:
+    the experimental energies are taken from the NIST data shipped with the species
+    (RydbergStateSQDT uses them for the low lying states instead of the Rydberg-Ritz formula).
+
+    The models reproduce these levels to |dnu| < 3e-4, while e.g. mixing up two channels of the
+    frame transformation shifts them by |dnu| ~ 1e-1, i.e. the tolerance below is not tight, but still strict.
+    """
+    nu_experimental = RydbergStateSQDT("Yb174", n=n, l_r=l_r, s_tot=s_tot, j_tot=j_tot).nu
+
+    model = _get_model("Yb174", name)
+    assert len(model.inner_channels) > 1, f"{model.full_name}: not a multi-channel model"
+
+    nu_range = (nu_experimental - 0.5, nu_experimental + 0.5)
+    states = get_mqdt_states_from_fmodel(model, nu_range, NotSet, get_potential_class("Yb174"))
+    assert len(states) > 0, f"{model.full_name}: no states found around nu={nu_experimental}"
+
+    closest = min(states, key=lambda state: abs(state.nu - nu_experimental))
+    assert abs(closest.nu - nu_experimental) < 1e-3, (
+        f"{model.full_name}: the calculated nu={closest.nu} does not match "
+        f"the experimental nu={nu_experimental} of the {n=}, {l_r=}, {j_tot=}, {s_tot=} level"
+    )