diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index dfd9934..c3725ee 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -3,6 +3,8 @@
 from dpnegf.negf.surface_green import selfEnergy
 import logging
 import os
+import shutil
+import tempfile
 from dpnegf.utils.constants import Boltzmann, eV2J
 import numpy as np
 from dpnegf.negf.bloch import Bloch
@@ -592,7 +594,8 @@ def _get_safe_n_jobs(lead_L, lead_R, requested_n_jobs=-1, max_memory_fraction=0.
 
 
 def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
-                            self_energy_save_path=None, n_jobs=-1, batch_size=200):
+                            self_energy_save_path=None, n_jobs=-1, batch_size=200,
+                            parallel_backend="loky"):
     """
     Computes and saves self-energy matrices for all combinations of k-points and energy values
     for left and right leads.
@@ -618,6 +621,10 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
         Number of parallel jobs to use. Default is -1 (use all available CPUs).
     batch_size : int, optional
         Number of (k, e) tasks per parallel batch. Default is 200.
+    parallel_backend : str, optional
+        Joblib backend used for self-energy calculation. Default is "loky"
+        to avoid pickling LeadProperty / TorchScript model objects when PyTorch
+        models are used.
 
     Returns
     -------
@@ -630,6 +637,10 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
                         "Self energy files will be saved in lead_L's results_path.")
         self_energy_save_path = os.path.join(lead_L.results_path, "self_energy")
 
+    self_energy_save_path = os.path.abspath(self_energy_save_path)
+    os.makedirs(self_energy_save_path, exist_ok=True)
+    temp_dir = tempfile.mkdtemp(prefix="tmp_self_energy_", dir=self_energy_save_path)
+
     # Calculate safe number of workers based on available memory
     # Use first k-point for memory estimation
     sample_kpoint = kpoints_grid[0] if len(kpoints_grid) > 0 else None
@@ -640,16 +651,17 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
         log.info(f"Adjusted n_jobs from {n_jobs} to {safe_n_jobs} due to memory constraints")
 
     total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
+    log.info(f"Using joblib backend='{parallel_backend}' for self-energy calculation.")
     if len(total_tasks) <= batch_size:
-        Parallel(n_jobs=safe_n_jobs, backend="loky")(
-            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, self_energy_save_path)
+        Parallel(n_jobs=safe_n_jobs, backend=parallel_backend)(
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, temp_dir)
             for k, e in total_tasks
         )
     else:
         for i in range(0, len(total_tasks), batch_size):
             batch = total_tasks[i:i+batch_size]
-            Parallel(n_jobs=safe_n_jobs, backend="loky")(
-                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, self_energy_save_path)
+            Parallel(n_jobs=safe_n_jobs, backend=parallel_backend)(
+                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, temp_dir)
                 for k, e in batch
             )
 
@@ -657,14 +669,22 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
     save_path_L = os.path.join(self_energy_save_path, "self_energy_leadL.h5")
     save_path_R = os.path.join(self_energy_save_path, "self_energy_leadR.h5")
 
-    merge_hdf5_files(self_energy_save_path, save_path_L, pattern="tmp_leadL_*.h5")
-    merge_hdf5_files(self_energy_save_path, save_path_R, pattern="tmp_leadR_*.h5")
+    try:
+        merge_hdf5_files(temp_dir, save_path_L, pattern="tmp_leadL_*.h5")
+        merge_hdf5_files(temp_dir, save_path_R, pattern="tmp_leadR_*.h5")
+    except Exception as exc:
+        raise RuntimeError(
+            f"Failed to merge self-energy temporary files from '{temp_dir}'. "
+            "Temporary files were preserved for debugging."
+        ) from exc
+
+    shutil.rmtree(temp_dir)
 
 
 
 
 
-def self_energy_worker(k, e, eta, lead_L, lead_R, self_energy_save_path):
+def self_energy_worker(k, e, eta, lead_L, lead_R, temp_dir):
     """
     Calculates the self-energy for left and right leads at a given k-point and energy,
     and saves the results to HDF5 files.
@@ -681,8 +701,8 @@ def self_energy_worker(k, e, eta, lead_L, lead_R, self_energy_save_path):
         The left lead object, which must implement a `self_energy_cal` method.
     lead_R : object
         The right lead object, which must implement a `self_energy_cal` method.
-    self_energy_save_path : str
-        Directory path where the self-energy HDF5 files will be saved.
+    temp_dir : str
+        Directory path where the temporary self-energy HDF5 shard files will be saved.
 
     Returns
     -------
@@ -690,8 +710,8 @@ def self_energy_worker(k, e, eta, lead_L, lead_R, self_energy_save_path):
         The function saves the calculated self-energies to files and does not return anything.
     """
 
-    save_tmp_L = os.path.join(self_energy_save_path, f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
-    save_tmp_R = os.path.join(self_energy_save_path, f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+    save_tmp_L = os.path.join(temp_dir, f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+    save_tmp_R = os.path.join(temp_dir, f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
 
     seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
     seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index cfcedbd..eb34526 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -109,6 +109,26 @@ def __init__(self,
             self.kpoints,self.wk = kmesh_sampling_negf(self.stru_options["kmesh"], 
                                                        self.stru_options["gamma_center"],
                                                      self.stru_options["time_reversal_symmetry"])
+
+        if 'override_overlap' in kwargs:
+            assert isinstance(kwargs['override_overlap'], str)
+            self.override_overlap = kwargs['override_overlap']
+            log.info(msg="Using external calculated overlap overriding!")
+        else:
+            self.override_overlap = None
+
+        if 'parallel_options' in kwargs:
+            assert isinstance(kwargs['parallel_options'], dict)
+            self.parallel_options = kwargs['parallel_options']
+            log.info(msg="Read parallel options from input!")
+        else:
+            self.parallel_options = {
+                "n_job": -1,
+                "batch_size": 200,
+                "backend": "loky"
+            }
+            log.info(msg="No parallel options Input, using default settings!")
+
         log.info(msg="------ k-point for NEGF -----")
         log.info(msg="Gamma Center: {0}".format(self.stru_options["gamma_center"]))
         log.info(msg="Time Reversal: {0}".format(self.stru_options["time_reversal_symmetry"]))
@@ -168,7 +188,7 @@ def __init__(self,
         e_fermi = {}; chemiPot = {}
         # calculate Fermi level
         if  self.e_fermi is None:        
-            elec_cal = ElecStruCal(model=model,device=self.torch_device)
+            elec_cal = ElecStruCal(model=model, device=self.torch_device, override_overlap=self.override_overlap)
             nel_atom_lead = self.get_nel_atom_lead(
                                 struct_leads, 
                                 charge={lead_tag: self.stru_options[lead_tag].get("charge", 0) for lead_tag in ["lead_L", "lead_R"]}
@@ -522,7 +542,8 @@ def prepare_self_energy(self, scf_require: bool) -> None:
         # self energy calculation
         log.info(msg="------Self-energy calculation------")
         if  self.self_energy_save_path is None:
-            self.self_energy_save_path = os.path.join(self.results_path, "self_energy") 
+            self.self_energy_save_path = os.path.join(self.results_path, "self_energy")
+        self.self_energy_save_path = os.path.abspath(self.self_energy_save_path)
         os.makedirs(self.self_energy_save_path, exist_ok=True)
 
         if self.use_saved_se:
@@ -539,11 +560,17 @@ def prepare_self_energy(self, scf_require: bool) -> None:
                 #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
                 #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
                 compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                        self.kpoints, self.density.integrate_range, self.self_energy_save_path)
+                                        self.kpoints, self.density.integrate_range, self.self_energy_save_path,
+                                        n_jobs=self.parallel_options["n_jobs"],
+                                        batch_size=self.parallel_options["batch_size"],
+                                        parallel_backend=self.parallel_options["backend"])
             elif not self.scf:
                 # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
                 compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                        self.kpoints, self.uni_grid, self.self_energy_save_path)
+                                        self.kpoints, self.uni_grid, self.self_energy_save_path,
+                                        n_jobs=self.parallel_options["n_jobs"],
+                                        batch_size=self.parallel_options["batch_size"],
+                                        parallel_backend=self.parallel_options["backend"])
         log.info(msg="-----------------------------------\n")
 
 
diff --git a/dpnegf/tests/data/test_negf/test_negf_e3/7_0.vasp b/dpnegf/tests/data/test_negf/test_negf_e3/7_0.vasp
new file mode 100644
index 0000000..fc02ea7
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_e3/7_0.vasp
@@ -0,0 +1,904 @@
+C 
+ 1.0000000000000000
+    25.4802092204926112    0.0000000000000000    0.0000000000000000
+     0.0000000000000000   25.3428089336544424    0.0000000000000000
+     0.0000000000000000    0.0000000000000000  136.3199999999999932
+ C  
+ 896
+Cartesian
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+ 13.3509476106316995  9.9951264176189216 135.6037500000000193
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+ 13.3509476106317013 15.3476825160355173 135.6037500000000193
+ 11.0285605568142184 10.5251961076609266 135.6037500000000193
+ 15.2133574262325588 13.8624602489004474 135.6037500000000193
diff --git a/dpnegf/tests/data/test_negf/test_negf_e3/negf_7_0.json b/dpnegf/tests/data/test_negf/test_negf_e3/negf_7_0.json
new file mode 100644
index 0000000..76a4ddb
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_e3/negf_7_0.json
@@ -0,0 +1,72 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": true,
+        "ele_T": 300,
+        "unit": "eV",
+        "scf_options": {
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-06,
+            "rel_err": 0.0001,
+            "max_iter": 100
+        },
+        "stru_options": {
+            "gamma_center": true,
+            "time_reversal_symmetry": true,
+            "nel_atom": {
+                "C": 4.0
+            },
+            "kmesh": [1, 1, 1],
+            "eig_solver": "numpy",
+            "pbc": [false, false, false],
+            "device": {
+                "id": "224-672",
+                "sort": true
+            },
+            "lead_L": {
+                "id": "0-224",
+                "voltage": 0.0,
+                "kmesh_lead_Ef": [1, 1, 20],
+                "useBloch": false
+            },
+            "lead_R": {
+                "id": "672-896",
+                "voltage": 0.0,
+                "kmesh_lead_Ef": [1, 1, 20],
+                "useBloch": false
+            }
+        },
+        "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-5
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.08,
+        "emin": -0.2,
+        "emax": 0.2,
+        "e_fermi": -13.638587951660156,
+        "density_options": {
+            "method": "Fiori",
+            "integrate_way": "direct"
+        },
+        "eta_lead": 1e-5,
+        "eta_device": 0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": false,
+        "out_current_nscf": true,
+        "parallel_options": {
+            "n_jobs": 1,
+            "batch_size": 200,
+            "backend": "threading"
+        }
+    },
+    "AtomicData_options" :{
+        "r_max": 4.0
+    },
+    "structure": "./7_0.vasp"
+}
\ No newline at end of file
diff --git a/dpnegf/tests/test_negf_e3.py b/dpnegf/tests/test_negf_e3.py
new file mode 100644
index 0000000..e443f6b
--- /dev/null
+++ b/dpnegf/tests/test_negf_e3.py
@@ -0,0 +1,38 @@
+from dpnegf.entrypoints.run import run
+import pytest
+import torch
+import numpy as np
+import os
+
+
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
+
+def test_negf_e3(root_directory):
+    INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_e3/negf_7_0.json"
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_e3/out_negf_e3"
+    checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_e3/nnenv.best.pth'
+    structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_e3/7_0.vasp"
+
+    run(INPUT=INPUT_file,
+        init_model=checkfile,
+        structure=structure,
+        output=output,
+        log_level=5,
+        log_path=output+"/output.log")
+
+    
+    negf_out_path = output+"/results/negf.out.pth"
+    assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
+    negf_results = torch.load(negf_out_path,weights_only=False)
+    trans = negf_results['T_avg']
+    assert(abs(trans[int(len(trans)/2)]-0.2751458)<1e-5)  #compare with calculated transmission at efermi
+
+
+    if os.path.exists(output+"/results"):
+        os.system("rm -r "+output+"/results")
+
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 6bb307c..0691d05 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1035,6 +1035,8 @@ def negf():
     doc_out_lcurrent = ""
     doc_density_options = ""
     doc_out_potential = ""
+    doc_override_overlap = ""
+    doc_parallel_options = ""
 
     return [
         Argument("scf", bool, optional=True, default=False, doc=doc_scf),
@@ -1064,7 +1066,19 @@ def negf():
         Argument("out_current", bool, optional=True, default=False, doc=doc_out_current),
         Argument("out_current_nscf", bool, optional=True, default=False, doc=doc_out_current_nscf),
         Argument("out_ldos", bool, optional=True, default=False, doc=doc_out_ldos),
-        Argument("out_lcurrent", bool, optional=True, default=False, doc=doc_out_lcurrent)
+        Argument("out_lcurrent", bool, optional=True, default=False, doc=doc_out_lcurrent),
+        Argument("override_overlap", str, optional=True, doc=doc_override_overlap),
+        Argument("parallel_options", dict, optional=True, sub_fields=parallel_options(), doc=doc_parallel_options)
+    ]
+
+def parallel_options():
+    doc_n_jobs = ""
+    doc_batch_size = ""
+    doc_backend = ""
+    return [
+        Argument("n_jobs", int, optional=True, default=-1, doc=doc_n_jobs),
+        Argument("batch_size", int, optional=True, default=200, doc=doc_batch_size),
+        Argument("backend", str, optional=True, default="loky", doc=doc_backend)
     ]
 
 def stru_options():
diff --git a/dpnegf/utils/elec_struc_cal.py b/dpnegf/utils/elec_struc_cal.py
index edda731..dc3f4e5 100644
--- a/dpnegf/utils/elec_struc_cal.py
+++ b/dpnegf/utils/elec_struc_cal.py
@@ -23,7 +23,8 @@ class ElecStruCal(object):
     def __init__ (
             self, 
             model: torch.nn.Module,
-            device: Union[str, torch.device]=None
+            device: Union[str, torch.device]=None,
+            **kwargs
             ):
         '''It initializes ElecStruCal object with a neural network model, optional results path, GUI
         usage flag, and device information, and sets up eigenvalues  based on model properties.
@@ -68,12 +69,18 @@ def __init__ (
             )
         r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
         self.cutoffs = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
+
+        if 'override_overlap' in kwargs and isinstance(kwargs['override_overlap'], str):
+            self.override_overlap = kwargs['override_overlap']
+        else:
+            self.override_overlap = None
+
     def get_data(self,
                  data: Union[AtomicData, ase.Atoms, str],
                  pbc:Union[bool,list]=None,
                  device: Union[str, torch.device]=None,
                  AtomicData_options:dict=None,
-                 override_overlap:Optional[str]=None):
+                 override_overlap:Union[str,bool,None]=None):
         '''The function `get_data` takes input data in the form of a string, ase.Atoms object, or AtomicData
         object, processes it accordingly, and returns the AtomicData class.
         
@@ -88,7 +95,8 @@ def get_data(self,
         device : Union[str, torch.device]
             The `device` parameter in the `get_data` function is used to specify the device on which the data
         should be processed. If no device is provided, it defaults to `self.device`.
-        override_overlap : the path for overlap.h5 to use and override overlap matrix from model.
+        override_overlap : the path for overlap.h5 to use and override overlap matrix from model. If None, will try
+            to use self.override_overlap; If False, will not try anything.
         
         Returns
         -------
@@ -139,6 +147,7 @@ def get_data(self,
         else:
             raise ValueError('data should be either a string, ase.Atoms, or AtomicData')
 
+        override_overlap = None if override_overlap == False else override_overlap if override_overlap else self.override_overlap
         if isinstance(override_overlap, str):
             assert os.path.exists(override_overlap), "Overlap file not found."
             overlap_blocks = h5py.File(override_overlap, "r")
@@ -175,7 +184,7 @@ def get_eigs(self,
                  klist: np.ndarray,
                  pbc:Union[bool,list]=None,
                  AtomicData_options:dict=None,
-                 override_overlap:Optional[str]=None,
+                 override_overlap:Union[str,bool,None]=None,
                  eig_solver:Optional[str]=None):
         '''This function calculates eigenvalues for Hk at specified k-points.
         
@@ -189,14 +198,16 @@ def get_eigs(self,
         AtomicData_options : dict
             The `AtomicData_options` parameter is a dictionary that contains options for configuring the
         `AtomicData` object.
-        override_overlap : the path for overlap.h5 to use and override overlap matrix from model.
+        override_overlap : the path for overlap.h5 to use and override overlap matrix from model. If None, will try
+            to use self.override_overlap; If False, will not try anything.
         
         Returns
         -------
             The function `get_eigs` returns the loaded data and the energy eigenvalues as a numpy array.
         
         '''
-            
+
+        override_overlap = None if override_overlap == False else override_overlap if override_overlap else self.override_overlap
         data  = self.get_data(data=data, pbc=pbc, device=self.device,AtomicData_options=AtomicData_options, override_overlap=override_overlap)
         # set the kpoint of the AtomicData
         data[AtomicDataDict.KPOINT_KEY] = \