diff --git a/mofa/assemble.py b/mofa/assemble.py
index 0ab61eac..25e7bf33 100644
--- a/mofa/assemble.py
+++ b/mofa/assemble.py
@@ -1,19 +1,18 @@
 """Functions for assembling a MOF structure"""
-import ase
+from typing import Sequence
 
+from .model import NodeDescription, LigandDescription, MOFRecord
 
-def assemble_mof(
-        node: object,
-        linker: str,
-        topology: object
-) -> ase.Atoms:
-    """Generate a MOF structure from a recipe
+
+def assemble_mof(nodes: Sequence[NodeDescription], ligands: Sequence[LigandDescription], topology: str) -> MOFRecord:
+    """Generate a new MOF from the description of the nodes, ligands and toplogy
 
     Args:
-        node: Atomic structure of the nodes
-        linker: SMILES string defining the linker object
-        topology: Description of the network structure
+        nodes: Descriptions of each node
+        ligands: Description of the ligands
+        topology: Name of the topology
+
     Returns:
-        Description of the 3D structure of the MOF
+        A new MOF record
     """
     raise NotImplementedError()
diff --git a/mofa/generator.py b/mofa/generator.py
index 9b46823d..075069bc 100644
--- a/mofa/generator.py
+++ b/mofa/generator.py
@@ -1,9 +1,10 @@
 """Functions pertaining to training and running the generative model"""
 from pathlib import Path
 
+from ase.io import write
 import ase
 
-from mofa.model import MOFRecord
+from mofa.model import MOFRecord, LigandDescription
 
 
 def train_generator(
@@ -25,15 +26,25 @@ def train_generator(
 
 def run_generator(
         model: str | Path,
+        fragment_template: LigandDescription,
         molecule_sizes: list[int],
-        num_samples: int
+        num_samples: int,
+        fragment_spacing: float | None = None,
 ) -> list[ase.Atoms]:
     """
     Args:
         model: Path to the starting weights
-        molecule_sizes: Number of heavy atoms in the linker molecules to generate
+        fragment_template: Template to be filled with linker atoms
+        molecule_sizes: Number of heavy atoms in the linker to generate
         num_samples: Number of samples of molecules to generate
+        fragment_spacing: Starting distance between the fragments
     Returns:
         3D geometries of the generated linkers
     """
+
+    # Create the template input
+    blank_template = fragment_template.generate_template(spacing_distance=fragment_spacing)
+    write('test.sdf', blank_template)
+
+    # Run the generator
     raise NotImplementedError()
diff --git a/mofa/model.py b/mofa/model.py
index 8e57fea6..4d140bd1 100644
--- a/mofa/model.py
+++ b/mofa/model.py
@@ -47,6 +47,17 @@ def linker_atoms(self) -> list[int]:
         """All atoms which are not part of a fragment"""
         raise NotImplementedError()
 
+    def generate_template(self, spacing_distance: float | None = None) -> ase.Atoms:
+        """Generate a version of the ligand with only the fragments at the end
+
+        Args:
+            spacing_distance: Distance to enforce between the fragments. Set to ``None``
+                to keep the current distance
+        Returns:
+            The template with the desired spacing
+        """
+        raise NotImplementedError()
+
 
 @dataclass
 class MOFRecord:
diff --git a/mofa/simulation/lammps.py b/mofa/simulation/lammps.py
index 5831ec2a..a066e7c9 100644
--- a/mofa/simulation/lammps.py
+++ b/mofa/simulation/lammps.py
@@ -1,6 +1,8 @@
 """Simulation operations that involve LAMMPS"""
 import ase
 
+from mofa.model import MOFRecord
+
 
 class LAMMPSRunner:
     """Interface for running pre-defined LAMMPS workflows
@@ -12,11 +14,11 @@ class LAMMPSRunner:
     def __init__(self, lammps_command: str):
         self.lammps_command: str = lammps_command
 
-    def run_molecular_dynamics(self, mof: ase.Atoms, timesteps: int, report_frequency: int) -> list[ase.Atoms]:
+    def run_molecular_dynamics(self, mof: MOFRecord, timesteps: int, report_frequency: int) -> list[ase.Atoms]:
         """Run a molecular dynamics trajectory
 
         Args:
-            mof: Starting structure
+            mof: Record describing the MOF. Includes the structure in CIF format, which includes the bonding information used by UFF
             timesteps: Number of timesteps to run
             report_frequency: How often to report structures
         Returns:
diff --git a/run_serial_workflow.py b/run_serial_workflow.py
new file mode 100644
index 00000000..d3168605
--- /dev/null
+++ b/run_serial_workflow.py
@@ -0,0 +1,69 @@
+"""An example of the workflow which runs on a single node"""
+import logging
+import sys
+from argparse import ArgumentParser
+
+import torch
+
+from mofa.generator import run_generator
+from mofa.model import MOFRecord
+from mofa.scoring.geometry import MinimumDistance
+from mofa.simulation.lammps import LAMMPSRunner
+
+if __name__ == "__main__":
+    # Make the argument parser
+    parser = ArgumentParser()
+
+    group = parser.add_argument_group(title='MOF Settings', description='Options related to the MOF type being generatored')
+    group.add_argument('--mof-template', default=None, help='Path to a MOF we are going to be altering')
+
+    group = parser.add_argument_group(title='Generator Settings', description='Options related to how the generation is performed')
+    group.add_argument('--generator-path', default=None, help='Path to the PyTorch files describing model architecture and weights')
+    group.add_argument('--molecule-sizes', nargs='+', dtype=int, default=(10, 11, 12), help='Sizes of molecules we should generate')
+    group.add_argument('--num-samples', dtype=int, default=16, help='Number of molecules to generate at each size')
+
+    args = parser.parse_args()
+
+    # TODO: Make a run directory
+
+    # Turn on logging
+    logger = logging.getLogger('main')
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+    logger.addHandler(handler)
+    logger.setLevel(logging.INFO)
+
+    # Load a pretrained generator from disk and use it to create ligands
+    template_mof = MOFRecord.from_file(cif_path=args.mof_template)
+    model = torch.load(args.generator_path)
+    generated_ligand_xyzs = run_generator(
+        model,
+        fragment_template=template_mof.ligands[0],
+        molecule_sizes=args.molecule_sizes,
+        num_samples=args.num_samples
+    )
+    logger.info(f'Generated {len(generated_ligand_xyzs)} ligands')
+
+    # Initial quality checks and post-processing on the generated ligands
+    validated_ligand_xyzs = []
+    for generated_xyz in generated_ligand_xyzs:
+        if False:  # TODO (wardlt): Add checks for detecting fragmented molecules, valency checks, ...
+            pass
+        validated_ligand_xyzs.append(add_hydrogens_to_ligand(generated_xyz))  # TODO (wardlt): Add a function which adds H's to the XYZ file
+    logger.info(f'Screened generated ligands. {validated_ligand_xyzs} pass quality checks')
+
+    # Combine them with the template MOF to create new MOFs
+    new_mofs = []
+    for new_ligand in validated_ligand_xyzs:
+        new_mof = template_mof.replace_ligand(new_ligand)
+        new_mofs.append(new_mof)
+    logger.info(f'Generated {len(new_mofs)} new MOFs')
+
+    # Score the MOFs
+    scorer = MinimumDistance()  # TODO (wardlt): Add or replace with a CGCNN that predicts absorption
+    scores = [scorer.score_mof(new_mof) for new_mof in new_mofs]
+    logger.info(f'Scored all {len(new_mofs)} MOFs')
+
+    # Run LAMMPS on the top MOF
+    ranked_mofs: list[tuple[float, MOFRecord]] = sorted(zip(scores, new_mofs))
+    LAMMPSRunner('lmp_serial').run_molecular_dynamics(ranked_mofs[-1][1], 100, 1)