diff --git a/deepchem/utils/dft_utils/__init__.py b/deepchem/utils/dft_utils/__init__.py
index 70912f975b..8212fb7236 100644
--- a/deepchem/utils/dft_utils/__init__.py
+++ b/deepchem/utils/dft_utils/__init__.py
@@ -11,7 +11,11 @@
 try:
     from deepchem.utils.dft_utils.hamilton.intor.lattice import Lattice
 
-    from deepchem.utils.dft_utils.datastruct import ZType
+    from deepchem.utils.dft_utils.data.datastruct import ZType
+    from deepchem.utils.dft_utils.data.datastruct import AtomPosType
+    from deepchem.utils.dft_utils.data.datastruct import AtomZsType
+    from deepchem.utils.dft_utils.data.datastruct import SpinParam
+    from deepchem.utils.dft_utils.data.datastruct import ValGrad
 
     from deepchem.utils.dft_utils.hamilton.orbparams import BaseOrbParams
     from deepchem.utils.dft_utils.hamilton.orbparams import QROrbParams
diff --git a/deepchem/utils/dft_utils/api/parser.py b/deepchem/utils/dft_utils/api/parser.py
index 706badb994..5ac949822f 100644
--- a/deepchem/utils/dft_utils/api/parser.py
+++ b/deepchem/utils/dft_utils/api/parser.py
@@ -1,6 +1,6 @@
 from typing import Union, Tuple
 import torch
-from deepchem.utils.dft_utils.datastruct import AtomZsType, AtomPosType
+from deepchem.utils.dft_utils import AtomZsType, AtomPosType
 from deepchem.utils import get_atomz
 
 __all__ = ["parse_moldesc"]
diff --git a/deepchem/utils/dft_utils/data/datastruct.py b/deepchem/utils/dft_utils/data/datastruct.py
new file mode 100644
index 0000000000..395495c4e0
--- /dev/null
+++ b/deepchem/utils/dft_utils/data/datastruct.py
@@ -0,0 +1,135 @@
+"""
+Density Functional Theory Data Structure Utilities
+"""
+from typing import Union, TypeVar, Generic, Optional, Callable, List
+from dataclasses import dataclass
+import torch
+import numpy as np
+
+__all__ = ["ZType"]
+
+T = TypeVar('T')
+P = TypeVar('P')
+
+# type of the atom Z
+ZType = Union[int, float, torch.Tensor]
+
+# input types
+AtomZsType = Union[List[str], List[ZType], torch.Tensor]
+AtomPosType = Union[List[List[float]], np.ndarray, torch.Tensor]
+
+
+@dataclass
+class SpinParam(Generic[T]):
+    """Data structure to store different values for spin-up and spin-down electrons.
+    Examples
+    --------
+    >>> import torch
+    >>> from deepchem.utils.dft_utils import SpinParam
+    >>> dens_u = torch.ones(1)
+    >>> dens_d = torch.zeros(1)
+    >>> sp = SpinParam(u=dens_u, d=dens_d)
+    >>> sp.u
+    tensor([1.])
+    >>> sp.sum()
+    tensor([1.])
+    >>> sp.reduce(torch.multiply)
+    tensor([0.])
+    """
+
+    def __init__(self, u: T, d: T):
+        """Initialize the SpinParam object.
+        Parameters
+        ----------
+        u: any type
+            The parameters that corresponds to the spin-up electrons.
+        d: any type
+            The parameters that corresponds to the spin-down electrons.
+        """
+        self.u = u
+        self.d = d
+
+    def __repr__(self) -> str:
+        """Return the string representation of the SpinParam object."""
+        return f"SpinParam(u={self.u}, d={self.d})"
+
+    def sum(self):
+        """Returns the sum of up and down parameters."""
+
+        return self.u + self.d
+
+    def reduce(self, fcn: Callable) -> T:
+        """Reduce up and down parameters with the given function."""
+
+        return fcn(self.u, self.d)
+
+
+@dataclass
+class ValGrad:
+    """Data structure that contains local information about density profiles.
+    Data structure used as a umbrella class for density profiles and the
+    derivative of the potential w.r.t. density profiles.
+    Examples
+    --------
+    >>> import torch
+    >>> from deepchem.utils.dft_utils import ValGrad
+    >>> dens = torch.ones(1)
+    >>> grad = torch.zeros(1)
+    >>> lapl = torch.ones(1)
+    >>> kin = torch.ones(1)
+    >>> vg = ValGrad(value=dens, grad=grad, lapl=lapl, kin=kin)
+    >>> vg + vg
+    ValGrad(value=tensor([2.]), grad=tensor([0.]), lapl=tensor([2.]), kin=tensor([2.]))
+    >>> vg * 5
+    ValGrad(value=tensor([5.]), grad=tensor([0.]), lapl=tensor([5.]), kin=tensor([5.]))
+    """
+
+    def __init__(self,
+                 value: torch.Tensor,
+                 grad: Optional[torch.Tensor] = None,
+                 lapl: Optional[torch.Tensor] = None,
+                 kin: Optional[torch.Tensor] = None):
+        """Initialize the ValGrad object.
+        Parameters
+        ----------
+        value: torch.Tensor
+            Tensors containing the value of the local information.
+        grad: torch.Tensor or None
+            If tensor, it represents the gradient of the local information with
+            shape ``(..., 3)`` where ``...`` should be the same shape as ``value``.
+        lapl: torch.Tensor or None
+            If tensor, represents the laplacian value of the local information.
+            It should have the same shape as ``value``.
+        kin: torch.Tensor or None
+            If tensor, represents the local kinetic energy density.
+            It should have the same shape as ``value``.
+        """
+        self.value = value
+        self.grad = grad
+        self.lapl = lapl
+        self.kin = kin
+
+    def __add__(self, b):
+        """Add two ValGrad objects together."""
+        return ValGrad(
+            value=self.value + b.value,
+            grad=self.grad + b.grad if self.grad is not None else None,
+            lapl=self.lapl + b.lapl if self.lapl is not None else None,
+            kin=self.kin + b.kin if self.kin is not None else None,
+        )
+
+    def __mul__(self, f: Union[float, int, torch.Tensor]):
+        """Multiply the ValGrad object with a scalar."""
+        if isinstance(f, torch.Tensor):
+            assert f.numel(
+            ) == 1, "ValGrad multiplication with tensor can only be done with 1-element tensor"
+
+        return ValGrad(
+            value=self.value * f,
+            grad=self.grad * f if self.grad is not None else None,
+            lapl=self.lapl * f if self.lapl is not None else None,
+            kin=self.kin * f if self.kin is not None else None,
+        )
+
+    def __repr__(self):
+        return f"ValGrad(value={self.value}, grad={self.grad}, lapl={self.lapl}, kin={self.kin})"
diff --git a/deepchem/utils/dft_utils/datastruct.py b/deepchem/utils/dft_utils/datastruct.py
deleted file mode 100644
index adaee17a9b..0000000000
--- a/deepchem/utils/dft_utils/datastruct.py
+++ /dev/null
@@ -1,17 +0,0 @@
-"""
-Density Functional Theory Data Structure Utilities
-"""
-from typing import Union, TypeVar, List
-import numpy as np
-import torch
-
-__all__ = ["ZType"]
-
-T = TypeVar('T')
-P = TypeVar('P')
-
-# type of the atom Z
-ZType = Union[int, float, torch.Tensor]
-# input types
-AtomZsType = Union[List[str], List[ZType], torch.Tensor]
-AtomPosType = Union[List[List[float]], np.ndarray, torch.Tensor]
diff --git a/deepchem/utils/test/test_dft_utils.py b/deepchem/utils/test/test_dft_utils.py
index 1c911ea003..c9f9d7862f 100644
--- a/deepchem/utils/test/test_dft_utils.py
+++ b/deepchem/utils/test/test_dft_utils.py
@@ -105,3 +105,45 @@ def test_parse_moldesc():
         torch.tensor(
             [[0.86625, 0.00000, 0.00000], [-0.86625, 0.00000, 0.00000]],
             dtype=torch.float64))
+
+
+@pytest.mark.torch
+def test_spin_param():
+    """Test SpinParam object."""
+    from deepchem.utils.dft_utils import SpinParam
+    dens_u = torch.ones(1)
+    dens_d = torch.zeros(1)
+    sp = SpinParam(u=dens_u, d=dens_d)
+
+    assert torch.allclose(sp.u, dens_u)
+    assert torch.allclose(sp.d, dens_d)
+    assert torch.allclose(sp.sum(), torch.tensor([1.]))
+    assert torch.allclose(sp.reduce(torch.multiply), torch.tensor([0.]))
+
+
+@pytest.mark.torch
+def test_val_grad():
+    """Test ValGrad object."""
+    from deepchem.utils.dft_utils import ValGrad
+    dens = torch.ones(1)
+    grad = torch.zeros(1)
+    lapl = torch.ones(1)
+    kin = torch.ones(1)
+    vg = ValGrad(value=dens, grad=grad, lapl=lapl, kin=kin)
+
+    assert torch.allclose(vg.value, dens)
+    assert torch.allclose(vg.grad, grad)
+    assert torch.allclose(vg.lapl, lapl)
+    assert torch.allclose(vg.kin, kin)
+
+    vg2 = vg + vg
+    assert torch.allclose(vg2.value, torch.tensor([2.]))
+    assert torch.allclose(vg2.grad, torch.tensor([0.]))
+    assert torch.allclose(vg2.lapl, torch.tensor([2.]))
+    assert torch.allclose(vg2.kin, torch.tensor([2.]))
+
+    vg5 = vg * 5
+    assert torch.allclose(vg5.value, torch.tensor([5.]))
+    assert torch.allclose(vg5.grad, torch.tensor([0.]))
+    assert torch.allclose(vg5.lapl, torch.tensor([5.]))
+    assert torch.allclose(vg5.kin, torch.tensor([5.]))
diff --git a/docs/source/api_reference/utils.rst b/docs/source/api_reference/utils.rst
index 80200b9053..9088210363 100644
--- a/docs/source/api_reference/utils.rst
+++ b/docs/source/api_reference/utils.rst
@@ -285,6 +285,12 @@ The utilites here are used to create an object that contains information about a
 .. autoclass:: deepchem.utils.dft_utils.Lattice
   :members:
 
+.. autoclass:: deepchem.utils.dft_utils.SpinParam
+  :members:
+
+.. autoclass:: deepchem.utils.dft_utils.ValGrad
+  :members:
+
 .. autoclass:: deepchem.utils.dftutils.KSCalc
   :members: