diff --git a/TightBinding.jl b/TightBinding.jl
index 6ccdbef..092b0cb 100644
--- a/TightBinding.jl
+++ b/TightBinding.jl
@@ -453,8 +453,8 @@ function hamiltonian!(tbm::TBModel, k,
                 idx += 1
                 It[idx] = In[i]
                 Jt[idx] = Im[j]
-                Ht[idx] = H_nm[i,j]*exp_i_kR[m]
-                Mt[idx] = M_nm[i,j]*exp_i_kR[m]
+                Ht[idx] = H_nm[i,j] * exp_i_kR[m]
+                Mt[idx] = M_nm[i,j] * exp_i_kR[m]
             end
         end
         # now compute the on-site terms (we could move these to be done in-place)
@@ -748,7 +748,6 @@ site_energy(nn::Array{Int}, atm::ASEAtoms, tbm::TBModel) =
 
 
 
-
 # site_forces always returns a complete gradient, i.e. dEs = d x Natm
 # When idx is an array, then the return-value is the gradient of \sum_{i ∈ idx} E_i
 
@@ -764,10 +763,18 @@ function site_forces(idx::Array{Int,1}, atm::ASEAtoms, tbm::TBModel)
 
     # precompute neighbourlist
     nlist = NeighbourList(cutoff(tbm), atm)
+	Nneig = 1
+    for (n, neigs, r, R) in Sites(nlist)
+        if length(neigs) > Nneig
+            Nneig = length(neigs)
+        end
+    end
+
     X = positions(atm)
+	# assemble the site forces for k-points
     for iK = 1:size(K,2)
         sfrc +=  weight[iK] *
-            real(site_forces_k(idx, X, tbm, nlist, K[:,iK]))
+            real(site_forces_k(idx, X, tbm, nlist, Nneig, K[:,iK]))
     end
 
     return sfrc
@@ -780,7 +787,103 @@ site_forces(n::Int, atm::ASEAtoms, tbm::TBModel) = site_forces([n;], atm, tbm)
 
 
 
+# With a given k-point, compute the site force by loop through eigenpairs (index s)
+# note that in the old version, we loop through through atoms
+#     E_l 	= ∑_s f(ɛ_s)⋅[ψ_s]_l^2
+#     E_l 	= ∑_s f(ɛ_s)⋅[ψ_s]_l⋅[M⋅ψ_s]_l
+# E_{l,n}	= ∑_s (	f'(ɛ_s)⋅ɛ_{s,n}⋅[ψ_s]_l⋅[M⋅ψ_s]_l + 2.0⋅f(ɛ_s)⋅[ψ_s]_{l,n}⋅[M⋅ψ_s]_l 
+#					+ f(ɛ_s)⋅[ψ_s]⋅[M_{,n}⋅ψ_s]_l )
+# We loop through eigenpair s to compute the first two parts and through atom n for the third part
+#
 function site_forces_k(idx::Array{Int,1}, X::Matrix{Float64},
+                       tbm::TBModel, nlist, Nneig, k::Vector{Float64};
+                       beta = ones(size(X,2)))
+
+    # obtain the precomputed arrays
+    epsn = get_k_array(tbm, :epsn, k)
+    C = get_k_array(tbm, :C, k)::Matrix{Complex{Float64}}
+	# some constant parameters
+    Nelc = length(epsn)
+	Natm = size(X,2)
+    Norb = tbm.norbitals
+
+	# overlap matrix is needed in this calculation
+	# use the following parameters as those in update_eig!
+    nnz_est = length(nlist) * Norb^2 + Natm * Norb^2
+    It = zeros(Int32, nnz_est)
+    Jt = zeros(Int32, nnz_est)
+    Ht = zeros(Complex{Float64}, nnz_est)
+    Mt = zeros(Complex{Float64}, nnz_est)
+    ~, M = hamiltonian!(tbm, k, It, Jt, Ht, Mt, nlist, X)
+	MC = M * C::Matrix{Complex{Float64}}
+
+    # allocate output
+    const dEs = zeros(Complex{Float64}, 3, Natm)
+    # pre-allocate dM
+    const dM_nm = zeros(3, Norb, Norb)
+    const Msn = zeros(Complex{Float64}, Nelc)
+	# const eps_s_n = zeros(Float64, 3, Natm)
+	# const psi_s_n = zeros(Float64, 3, Natm, Nelc)
+
+	# precompute electron distribution function
+	f = tbm.smearing(epsn, tbm.eF) .* epsn
+    df = tbm.smearing(epsn, tbm.eF) + epsn .* (@D tbm.smearing(epsn, tbm.eF))
+
+	# loop through all eigenstates to compute the hessian
+	for s = 1 : Nelc
+		# compute ϵ_{s,n} and ψ_{s,n}
+		eps_s_n, psi_s_n = d_eigenstate_k(s, tbm, X, nlist, Nneig, k)
+
+		# loop for the first part
+ 		for d = 1:3
+ 			for n = 1:Natm
+				Msn = M * psi_s_n[d, n, :][:]
+	            for id in idx
+	                # in this iteration of the loop we compute the contributions
+    	            # that come from the site i. hence multiply everything with beta[i]
+        	        Ii = indexblock(id, tbm)
+	 				dEs[d, n] -= beta[id] * df[s] * eps_s_n[d, n] * sum( C[Ii, s] .* MC[Ii, s] )
+ 					dEs[d, n] -= beta[id] * f[s] * sum( MC[Ii, s] .* psi_s_n[d, n, Ii][:] )
+ 					dEs[d, n] -= beta[id] * f[s] * sum( C[Ii, s] .* Msn[Ii] )
+				end 	# loop of id
+ 			end		# loop of d
+ 		end		# loop of n
+	end 	# loop of s
+
+    # loop through all atoms, to compute the last part
+    for (n, neigs, r, R) in Sites(nlist)
+		# consider only the rows related to site idx
+		if n in idx
+	        # compute the block of indices for the orbitals belonging to n
+    	    In = indexblock(n, tbm)
+        	exp_i_kR = exp(im * (k' * (R - (X[:, neigs] .- X[:, n]))))
+
+	        for i_n = 1:length(neigs)
+    	      	m = neigs[i_n]
+        	    Im = indexblock(m, tbm)
+				eikr = exp_i_kR[i_n]
+
+            	# compute and store ∂M_mn/∂y_n
+	            grad!(tbm.overlap, r[i_n], R[:,i_n], dM_nm)
+				# sum over all eigenpairs
+		    	for s = 1:Nelc
+    	    	    for d = 1:3
+            	  		dEs[d, n] -= beta[n] * f[s] * sum( C[In, s] .* ( - slice(dM_nm, d, :, :) * C[Im, s] ) ) * eikr
+              			dEs[d, m] -= beta[n] * f[s] * sum( C[In, s] .* ( slice(dM_nm, d, :, :) * C[Im, s] ) ) * eikr
+		            end		# loop for d
+				end 	# loop for s
+			end		# loop for neighbour i_n
+		end 	# end of if
+	end		# loop for atom n 
+
+	return dEs
+	# note that this is in fact the site force, -dEs
+end
+
+
+
+# an old version for site force
+function site_forces_k_old(idx::Array{Int,1}, X::Matrix{Float64},
                        tbm::TBModel, nlist, k::Vector{Float64};
                        beta = ones(size(X,2)))
     # obtain the precomputed arrays
@@ -927,10 +1030,8 @@ function site_forces_k(idx::Array{Int,1}, X::Matrix{Float64},
                     dEs[a,m] += beta[id] * f[s] * sum( C[Ii, s] .* MC_s_m[Ii,a] )
                 end
             end
-
         end  # loop for s, eigenpairs
     end  # loop for n, atomic sites
-
     return -dEs # , [1:Natm;]
 end
 
@@ -938,7 +1039,6 @@ end
 
 
 
-
 ###################### Hessian and Higher-oerder derivatives ##########################
 
 
@@ -976,7 +1076,7 @@ function d_eigenstate_k(s::Int, tbm::TBModel, X::Matrix{Float64}, nlist, Nneig::
 
 	# allocate memory
 	psi_s_n = zeros(Complex{Float64}, 3*Natm, Nelc)
-	eps_s_n = zeros(Float64, 3*Natm)
+	eps_s_n = zeros(Complex{Float64}, 3*Natm)
 	g_s_n = zeros(Complex{Float64}, 3*Natm, Nelc)
 	f_s_n = zeros(Complex{Float64}, 3*Natm, Nelc)
 	const dH_nn = zeros(3, Norb, Norb, Nneig)
@@ -985,7 +1085,6 @@ function d_eigenstate_k(s::Int, tbm::TBModel, X::Matrix{Float64}, nlist, Nneig::
 
 	# Step 1. loop through all atoms to compute g_s_n and f_s_n for all n
     for (n, neigs, r, R) in Sites(nlist)
-
         In = indexblock(n, tbm)
         exp_i_kR = exp(im * (k' * (R - (X[:, neigs] .- X[:, n]))))
 
@@ -995,6 +1094,7 @@ function d_eigenstate_k(s::Int, tbm::TBModel, X::Matrix{Float64}, nlist, Nneig::
         for i_n = 1:length(neigs)
 			m = neigs[i_n]
 	        Im = indexblock(m, tbm)
+			eikr = exp_i_kR[i_n]
 
             # compute and store ∂H_nm/∂y_m (hopping terms) and ∂M_nm/∂y_m
             grad!(tbm.hop, r[i_n], R[:,i_n], dH_nm)
@@ -1006,11 +1106,10 @@ function d_eigenstate_k(s::Int, tbm::TBModel, X::Matrix{Float64}, nlist, Nneig::
 				g_s_n[md, In] += ( slice(dH_nn, d, :, :, i_n) * C[In, s] )'
 				g_s_n[nd, In] -= ( slice(dH_nn, d, :, :, i_n) * C[In, s] )'
 
-                g_s_n[md, In] += ( slice(dH_nm, d, :, :) * C[Im, s] )' # * eikr
-       	        g_s_n[nd, In] -= ( slice(dH_nm, d, :, :) * C[Im, s] )' # * eikr
-
-                f_s_n[md, In] += ( slice(dM_nm, d, :, :) * C[Im, s] )' # * eikr
-   	            f_s_n[nd, In] -= ( slice(dM_nm, d, :, :) * C[Im, s] )' # * eikr
+                g_s_n[md, In] += ( slice(dH_nm, d, :, :) * C[Im, s] )' * eikr
+       	        g_s_n[nd, In] -= ( slice(dH_nm, d, :, :) * C[Im, s] )' * eikr
+                f_s_n[md, In] += ( slice(dM_nm, d, :, :) * C[Im, s] )' * eikr
+   	            f_s_n[nd, In] -= ( slice(dM_nm, d, :, :) * C[Im, s] )' * eikr
 			end		# loop for dimension
 
 		end		# loop for neighbours
@@ -1053,7 +1152,6 @@ end
 
 
 
-
 # hessian always returns a complete hessian, i.e. hessian = ( d × Natm )^2
 function hessian(atm::ASEAtoms, tbm::TBModel)
 
@@ -1103,6 +1201,7 @@ potential_energy_d2(atm::ASEAtoms, tbm::TBModel) = hessian(atm, tbm)
 #       ɛ_{s,mn} ∈ R^{ Nelc ×  3 × Natm × 3 × Natm }
 # note that the output of  ɛ_{s,mn}  is stored for usage of computing d3E
 # TODO: have not added e^ikr into the hamiltonian yet
+# TODO: we do not need the whole hessian matrix, but only those related to the centred atom
 
 function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64})
 
@@ -1186,6 +1285,7 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
     	    for i_n = 1:length(neigs)
 				m = neigs[i_n]
 		        Im = indexblock(m, tbm)
+				eikr = exp_i_kR[i_n]
 
         	    # compute and store ∂H, ∂^2H and ∂M, ∂^2M
             	# evaluate!(tbm.overlap, r[i_n], R[:, i_n], M_nm)
@@ -1206,7 +1306,7 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
 								 + C[In, s]' * (
 								 eps_s_n[d2, l] * slice(dM_nm, d1, :, :)
                                               ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 							eps_s_mn[s, d1, l, d2, n] += (
 								 C[In, s]' * ( - slice(dH_nm, d2, :, :)
 								 + epsn[s] * slice(dM_nm, d2, :, :)
@@ -1214,7 +1314,7 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
 								 + C[In, s]' * (
 								 eps_s_n[d1, l] * slice(dM_nm, d2, :, :)
                                               ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 							eps_s_mn[s, d1, m, d2, l] += (
 								 C[In, s]' * ( slice(dH_nm, d1, :, :)
 								 - epsn[s] * slice(dM_nm, d1, :, :)
@@ -1222,7 +1322,7 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
 								 - C[In, s]' * (
 								 eps_s_n[d2, l] * slice(dM_nm, d1, :, :)
                                               ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 							eps_s_mn[s, d1, l, d2, m] += (
 								 C[In, s]' * ( slice(dH_nm, d2, :, :)
 								 - epsn[s] * slice(dM_nm, d2, :, :)
@@ -1230,7 +1330,7 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
 								 - C[In, s]' * (
 								 eps_s_n[d1, l] * slice(dM_nm, d2, :, :)
                                               ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 						end	# loop for atom l
 
 						# contributions from hopping terms
@@ -1238,19 +1338,19 @@ function hessian_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Flo
 						eps_s_mn[s, d1, n, d2, n] += (
 								 C[In, s]' * ( slice(d2H_nm, d1, d2, :, :)
 								 - epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 						eps_s_mn[s, d1, m, d2, m] += (
 								 C[In, s]' * ( slice(d2H_nm, d1, d2, :, :)
 								 - epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 						eps_s_mn[s, d1, m, d2, n] += (
 								 C[In, s]' * ( - slice(d2H_nm, d1, d2, :, :)
 								 + epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 						eps_s_mn[s, d1, n, d2, m] += (
 								 C[In, s]' * ( - slice(d2H_nm, d1, d2, :, :)
 								 + epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * C[Im,s]
-								 )[1]
+								 )[1] * eikr
 
 						# contributions from onsite terms
 						m1 = 3*(i_n-1) + d1
@@ -1347,7 +1447,6 @@ function d3E(atm::ASEAtoms, tbm::TBModel)
 end
 
 
-
 potential_energy_d3(atm::ASEAtoms, tbm::TBModel) = d3E(atm, tbm)
 
 
@@ -1473,6 +1572,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
     	    for i_n = 1:length(neigs)
 				m = neigs[i_n]
 		        Im = indexblock(m, tbm)
+				eikr = exp_i_kR[i_n]
 
         	    # compute and store ∂H, ∂^2H and ∂M, ∂^2M
             	evaluate_fd!(tbm.hop, R[:,i_n], dH_nm)
@@ -1496,7 +1596,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											+ 2.0 * eps_s_n[d3, q] * C[In, s]' * slice(dM_nm, d1, :, :) * psi_s_n[d2, p, Im][:]
 											+ 2.0 * psi_s_n[d2, p, In][:]' * ( - slice(dH_nm, d1, :, :)
 								 			+ epsn[s] * slice(dM_nm, d1, :, :) ) * psi_s_n[d3, q, Im][:]
-											)[1]
+											)[1] * eikr
 									# 2. mpq
 									D3E[d1, m, d2, p, d3, q] +=  feps3[s] * (
 											- eps_s_mn[s, d2, p, d3, q] * C[In, s]' * slice(dM_nm, d1, :, :) * C[Im, s]
@@ -1504,7 +1604,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											- 2.0 * eps_s_n[d3, q] * C[In, s]' * slice(dM_nm, d1, :, :) * psi_s_n[d2, p, Im][:]
 											+ 2.0 * psi_s_n[d2, p, In][:]' * ( slice(dH_nm, d1, :, :)
 								 			- epsn[s] * slice(dM_nm, d1, :, :) ) * psi_s_n[d3, q, Im][:]
-											)[1]
+											)[1] * eikr
 									# 3. pnq
 									D3E[d1, p, d2, n, d3, q] +=  feps3[s] * (
 											eps_s_mn[s, d1, p, d3, q] * C[In, s]' * slice(dM_nm, d2, :, :) * C[Im, s]
@@ -1512,7 +1612,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											+ 2.0 * eps_s_n[d3, q] * C[In, s]' * slice(dM_nm, d2, :, :) * psi_s_n[d1, p, Im][:]
 											+ 2.0 * psi_s_n[d1, p, In][:]' * ( - slice(dH_nm, d2, :, :)
 								 			+ epsn[s] * slice(dM_nm, d2, :, :) ) * psi_s_n[d3, q, Im][:]
-											)[1]
+											)[1] * eikr
 									# 4. pmq
 									D3E[d1, p, d2, m, d3, q] +=  feps3[s] * (
 											- eps_s_mn[s, d1, p, d3, q] * C[In, s]' * slice(dM_nm, d2, :, :) * C[Im, s]
@@ -1520,7 +1620,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											- 2.0 * eps_s_n[d3, q] * C[In, s]' * slice(dM_nm, d2, :, :) * psi_s_n[d1, p, Im][:]
 											+ 2.0 * psi_s_n[d1, p, In][:]' * ( slice(dH_nm, d2, :, :)
 								 			- epsn[s] * slice(dM_nm, d2, :, :) ) * psi_s_n[d3, q, Im][:]
-											)[1]
+											)[1] * eikr
 									# 5. pqn
 									D3E[d1, p, d2, q, d3, n] +=  feps3[s] * (
 											eps_s_mn[s, d1, p, d2, q] * C[In, s]' * slice(dM_nm, d3, :, :) * C[Im, s]
@@ -1528,7 +1628,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											+ 2.0 * eps_s_n[d2, q] * C[In, s]' * slice(dM_nm, d3, :, :) * psi_s_n[d1, p, Im][:]
 											+ 2.0 * psi_s_n[d1, p, In][:]' * ( - slice(dH_nm, d3, :, :)
 								 			+ epsn[s] * slice(dM_nm, d3, :, :) ) * psi_s_n[d2, q, Im][:]
-											)[1]
+											)[1] * eikr
 									# 6. pqm
 									D3E[d1, p, d2, q, d3, m] +=  feps3[s] * (
 											- eps_s_mn[s, d1, p, d2, q] * C[In, s]' * slice(dM_nm, d3, :, :) * C[Im, s]
@@ -1536,7 +1636,7 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 											- 2.0 * eps_s_n[d2, q] * C[In, s]' * slice(dM_nm, d3, :, :) * psi_s_n[d1, p, Im][:]
 											+ 2.0 * psi_s_n[d1, p, In][:]' * ( slice(dH_nm, d3, :, :)
 								 			- epsn[s] * slice(dM_nm, d3, :, :) ) * psi_s_n[d2, q, Im][:]
-											)[1]
+											)[1] * eikr
 								end 	# loop for atom p
 							end 	# loop for atom q
 
@@ -1549,73 +1649,73 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 										- eps_s_n[d3, l] * C[In, s]' * slice(d2M_nm, d1, d2, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d1, d2, :, :)
 										- epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * psi_s_n[d3, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 2. mml
 								D3E[d1, m, d2, m, d3, l] +=  feps3[s] * (
 										- eps_s_n[d3, l] * C[In, s]' * slice(d2M_nm, d1, d2, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d1, d2, :, :)
 										- epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * psi_s_n[d3, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 3. nml
 								D3E[d1, n, d2, m, d3, l] +=  feps3[s] * (
 										eps_s_n[d3, l] * C[In, s]' * slice(d2M_nm, d1, d2, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d1, d2, :, :)
 										+ epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * psi_s_n[d3, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 4. mnl
 								D3E[d1, m, d2, n, d3, l] +=  feps3[s] * (
 										eps_s_n[d3, l] * C[In, s]' * slice(d2M_nm, d1, d2, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d1, d2, :, :)
 										+ epsn[s] * slice(d2M_nm, d1, d2, :, :) ) * psi_s_n[d3, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 5. nln
 								D3E[d1, n, d2, l, d3, n] +=  feps3[s] * (
 										- eps_s_n[d2, l] * C[In, s]' * slice(d2M_nm, d1, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d1, d3, :, :)
 										- epsn[s] * slice(d2M_nm, d1, d3, :, :) ) * psi_s_n[d2, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 6. mlm
 								D3E[d1, m, d2, l, d3, m] +=  feps3[s] * (
 										- eps_s_n[d2, l] * C[In, s]' * slice(d2M_nm, d1, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d1, d3, :, :)
 										- epsn[s] * slice(d2M_nm, d1, d3, :, :) ) * psi_s_n[d2, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 7. nlm
 								D3E[d1, n, d2, l, d3, m] +=  feps3[s] * (
 										eps_s_n[d2, l] * C[In, s]' * slice(d2M_nm, d1, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d1, d3, :, :)
 										+ epsn[s] * slice(d2M_nm, d1, d3, :, :) ) * psi_s_n[d2, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 8. mln
 								D3E[d1, m, d2, l, d3, n] +=  feps3[s] * (
 										eps_s_n[d2, l] * C[In, s]' * slice(d2M_nm, d1, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d1, d3, :, :)
 										+ epsn[s] * slice(d2M_nm, d1, d3, :, :) ) * psi_s_n[d2, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 9. lnn
 								D3E[d1, l, d2, n, d3, n] +=  feps3[s] * (
 										- eps_s_n[d1, l] * C[In, s]' * slice(d2M_nm, d2, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d2, d3, :, :)
 										- epsn[s] * slice(d2M_nm, d2, d3, :, :) ) * psi_s_n[d1, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 10. lmm
 								D3E[d1, l, d2, m, d3, m] +=  feps3[s] * (
 										- eps_s_n[d1, l] * C[In, s]' * slice(d2M_nm, d2, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( slice(d2H_nm, d2, d3, :, :)
 										- epsn[s] * slice(d2M_nm, d2, d3, :, :) ) * psi_s_n[d1, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 11. lnm
 								D3E[d1, l, d2, n, d3, m] +=  feps3[s] * (
 										eps_s_n[d1, l] * C[In, s]' * slice(d2M_nm, d2, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d2, d3, :, :)
 										+ epsn[s] * slice(d2M_nm, d2, d3, :, :) ) * psi_s_n[d1, l, Im][:]
-										)[1]
+										)[1] * eikr
 								# 12. lmn
 								D3E[d1, l, d2, m, d3, n] +=  feps3[s] * (
 										eps_s_n[d1, l] * C[In, s]' * slice(d2M_nm, d2, d3, :, :) * C[Im, s]
 										+ 2.0 * C[In, s]' * ( - slice(d2H_nm, d2, d3, :, :)
 										+ epsn[s] * slice(d2M_nm, d2, d3, :, :) ) * psi_s_n[d1, l, Im][:]
-										)[1]
+										)[1] * eikr
 							end 	# loop for atom l
 
 							# contributions from hopping terms
@@ -1625,42 +1725,42 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 							D3E[d1, n, d2, n, d3, n] +=  feps3[s] * (
 								 	C[In, s]' * ( - slice(d3H_nm, d1, d2, d3, :, :)
 									+ epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 2. nnm
 							D3E[d1, n, d2, n, d3, m] +=  feps3[s] * (
 								 	C[In, s]' * ( slice(d3H_nm, d1, d2, d3, :, :)
 									- epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-								 	)[1]
+								 	)[1] * eikr
 							# 3. nmn
 							D3E[d1, n, d2, m, d3, n] +=  feps3[s] * (
 								 	C[In, s]' * ( slice(d3H_nm, d1, d2, d3, :, :)
 									- epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 4. nmm
 							D3E[d1, n, d2, m, d3, m] +=  feps3[s] * (
 								 	C[In, s]' * ( - slice(d3H_nm, d1, d2, d3, :, :)
 									+ epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 5. mmm
 							D3E[d1, m, d2, m, d3, m] +=  feps3[s] * (
 								 	C[In, s]' * ( slice(d3H_nm, d1, d2, d3, :, :)
 									- epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 6. mmn
 							D3E[d1, m, d2, m, d3, n] +=  feps3[s] * (
 								 	C[In, s]' * ( - slice(d3H_nm, d1, d2, d3, :, :)
 									+ epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 7. mnm
 							D3E[d1, m, d2, n, d3, m] +=  feps3[s] * (
 								 	C[In, s]' * ( - slice(d3H_nm, d1, d2, d3, :, :)
 									+ epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 							# 8. mnn
 							D3E[d1, m, d2, n, d3, n] +=  feps3[s] * (
 								 	C[In, s]' * ( slice(d3H_nm, d1, d2, d3, :, :)
 									- epsn[s] * slice(d3M_nm, d1, d2, d3, :, :)	) * C[Im, s]
-									)[1]
+									)[1] * eikr
 
 
 							# contributions from onsite terms
@@ -1679,18 +1779,18 @@ function d3E_k(X::Matrix{Float64}, tbm::TBModel, nlist, Nneig, k::Vector{Float64
 									D3E[d1, m, d2, p, d3, q] +=  feps3[s] * (
  										2.0 * psi_s_n[d2, p, In][:]' * ( dH_nn[m1, :][:] .* psi_s_n[d3, q, In][:] )
 										)[1]
-									# npq, mpq
+									# pnq, pmq
 									D3E[d1, p, d2, n, d3, q] +=  feps3[s] * (
  										2.0 * psi_s_n[d1, p, In][:]' * ( - dH_nn[m2, :][:] .* psi_s_n[d3, q, In][:] )
 										)[1]
 									D3E[d1, p, d2, m, d3, q] +=  feps3[s] * (
  										2.0 * psi_s_n[d1, p, In][:]' * ( dH_nn[m2, :][:] .* psi_s_n[d3, q, In][:] )
 										)[1]
-									# npq, mpq
+									# pqn, pqm
 									D3E[d1, p, d2, q, d3, n] +=  feps3[s] * (
  										2.0 * psi_s_n[d1, p, In][:]' * ( - dH_nn[m3, :][:] .* psi_s_n[d2, q, In][:] )
 										)[1]
-									D3E[d1, p, d2, p, d3, m] +=  feps3[s] * (
+									D3E[d1, p, d2, q, d3, m] +=  feps3[s] * (
  										2.0 * psi_s_n[d1, p, In][:]' * ( dH_nn[m3, :][:] .* psi_s_n[d2, q, In][:] )
 										)[1]
 								end 	# loop for atom q
diff --git a/notebooks/NRLTB-test.ipynb b/notebooks/NRLTB-test.ipynb
index fa7cbe7..6b9ac2a 100644
--- a/notebooks/NRLTB-test.ipynb
+++ b/notebooks/NRLTB-test.ipynb
@@ -4,9 +4,20 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.52917721092"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "push!(LOAD_PATH, \"..\")\n",
     "\n",
@@ -16,7 +27,8 @@
     "using PyCall\n",
     "using TightBinding\n",
     "\n",
-    "import NRLTB"
+    "import NRLTB\n",
+    "BOHR = 0.52917721092"
    ]
   },
   {
@@ -28,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -40,28 +52,33 @@
       "WARNING: replacing module NRLTB\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lattice constant of Si = 5"
+     ]
+    },
     {
      "data": {
       "image/png": [
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"
+       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"
       ],
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x5c6bc90>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0xd528210>)"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "1-element Array{Any,1}:\n",
-       " PyObject <matplotlib.lines.Line2D object at 0x5e1fdd0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".43\n",
+      " original curtoff radius = 12.5\n",
+      " reduced cutoff radius = 9.375"
+     ]
     }
    ],
    "source": [
@@ -78,10 +95,34 @@
     "    y3[k] = NRLTB.h_hop(x[k], 3, NRLTB.Si_sp)\n",
     "    y4[k] = NRLTB.h_hop(x[k], 4, NRLTB.Si_sp)\n",
     "end\n",
-    "plot(x,y1)\n",
-    "plot(x,y2)\n",
-    "plot(x,y3)\n",
-    "plot(x,y4)"
+    "plot(x,y1,\"r-\")\n",
+    "plot(x,y2,\"y-\")\n",
+    "plot(x,y3,\"k-\")\n",
+    "plot(x,y4,\"b-\")\n",
+    "\n",
+    "\n",
+    "# compare the hamiltonian matrix elements with different cutoff radius\n",
+    "Rc = NRLTB.Si_sp.Rc\n",
+    "NRLTB.Si_sp.Rc = Rc * 3/4\n",
+    "\n",
+    "z1 = zeros(length(x))\n",
+    "z2 = zeros(length(x))\n",
+    "z3 = zeros(length(x))\n",
+    "z4 = zeros(length(x))\n",
+    "for k = 1:length(x)\n",
+    "    z1[k] = NRLTB.h_hop(x[k], 1, NRLTB.Si_sp)\n",
+    "    z2[k] = NRLTB.h_hop(x[k], 2, NRLTB.Si_sp)\n",
+    "    z3[k] = NRLTB.h_hop(x[k], 3, NRLTB.Si_sp)\n",
+    "    z4[k] = NRLTB.h_hop(x[k], 4, NRLTB.Si_sp)\n",
+    "end\n",
+    "plot(x,z1,\"r--\")\n",
+    "plot(x,z2,\"y--\")\n",
+    "plot(x,z3,\"k--\")\n",
+    "plot(x,z4,\"b--\")\n",
+    "\n",
+    "\n",
+    "print(\"lattice constant of Si = \", 5.43, \"\\n\")\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.Si_sp.Rc)"
    ]
   },
   {
@@ -93,73 +134,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "256"
+      "number of atoms = 108\n",
+      "lattice constant = 4.05"
      ]
     }
    ],
    "source": [
     "# create the supercell\n",
-    "n = 4\n",
+    "reload(\"NRLTB\")\n",
     "\n",
+    "n = 3\n",
     "at = bulk(\"Al\"; cubic=true)\n",
     "at = repeat(at, (n, n, n))\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "\n",
     "X = positions(at)\n",
-    "print(length(at))"
+    "print(\"number of atoms = \", length(at), \"\\n\")\n",
+    "\n",
+    "CELL = get_cell(at)\n",
+    "bond = CELL[1]/n\n",
+    "print(\"lattice constant = \", bond)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module TightBinding\n",
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "4.05"
+       "1.2004059507766591"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "CELL = get_cell(at)\n",
+    "# set the fermi level\n",
+    "reload(\"TightBinding\")\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.Al_spd)\n",
+    "tbm.smearing.beta = 50\n",
     "\n",
-    "bond = CELL[1]/n"
+    "TightBinding.potential_energy(at, tbm)\n",
+    "tbm.fixed_eF = false\n",
+    "TightBinding.update_eF!(at, tbm)\n",
+    "EF = tbm.eF"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.1849193989995597"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.463119458437378\t ratio = 0.9\t energy = 1488.0131457983784\n",
+      "1.1849193989995597\t ratio = 1.0\t energy = 1196.2040410817326\n",
+      "1.0176371260567376\t ratio = 1.1\t energy = 1021.8496101330011\n",
+      "0.9425181153290498\t ratio = 1.2\t energy = 926.933791343571\n",
+      "0.9182682467587087\t ratio = 1.3\t energy = 879.8496297834662\n",
+      "0.9250570860339956\t ratio = 1.4\t energy = 858.3457102191409\n",
+      "0.9449491732562654\t ratio = 1.5\t energy = 850.4242213608374\n",
+      "0.9645449457377067\t ratio = 1.6\t energy = 849.0830306579605\n",
+      "0.978876663626219\t ratio = 1.7000000000000002\t energy = 850.4098263198781\n",
+      "0.9873987190863462\t ratio = 1.8\t energy = 852.0787488639514\n",
+      "0.9916095348768792\t ratio = 1.9\t energy = 852.9682635429407\n",
+      "0.9933274725890894\t ratio = 2.0\t energy = 853.0605650396055\n",
+      "0.9937523452796602\t ratio = 2.1\t energy = 852.7348604690209\n"
+     ]
     }
    ],
    "source": [
+    "# compute total energy with different cell size\n",
+    "\n",
+    "# NOTE: For Al, it is very necessary to adjust the fermi level for different systems\n",
+    "\n",
+    "m = 13\n",
+    "x = zeros(m)\n",
+    "y = zeros(m)\n",
+    "\n",
+    "for k = 1:m    \n",
+    "    ratio = 1.0 + (k-2) * 0.1\n",
+    "    set_positions!(at, X * ratio)\n",
+    "    set_cell!(at, CELL * ratio)\n",
+    "    \n",
+    "    TightBinding.update!(at, tbm)\n",
+    "    print(tbm.eF)\n",
+    "    \n",
+    "    x[k] = bond * ratio\n",
+    "    y[k] = TightBinding.potential_energy(at, tbm)\n",
+    "    println(\"\\t ratio = \", ratio, \"\\t energy = \", y[k])\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "###### perform the same calculations with smaller cutoff radius\n",
+    "# reduce the cutoff radius by 1/6\n",
+    "\n",
     "# set the fermi level\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "Rc = NRLTB.Al_spd.Rc\n",
+    "NRLTB.Al_spd.Rc = Rc * 2/3\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.Si_sp.Rc)\n",
     "\n",
     "set_pbc!(at, [true, true, true])\n",
     "tbm = NRLTB.NRLTBModel(elem = NRLTB.Al_spd)\n",
@@ -180,26 +299,31 @@
    "outputs": [],
    "source": [
     "# compute total energy with different cell size\n",
+    "reload(\"NRLTB\")\n",
     "\n",
-    "m = 12\n",
+    "# compare the hamiltonian matrix elements with different cutoff radius\n",
+    "Rc = NRLTB.Al_spd.Rc\n",
+    "NRLTB.Al_spd.Rc = Rc * 2/3\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.Al_spd.Rc)\n",
+    "\n",
+    "m = 13\n",
     "x = zeros(m)\n",
-    "y = zeros(m)\n",
+    "z = zeros(m)\n",
     "\n",
     "for k = 1:m    \n",
-    "    ratio = 1.0 + (k-7) * 0.1\n",
-    "    println(ratio)\n",
+    "    ratio = 1.0 + (k-5) * 0.02\n",
     "    set_positions!(at, X * ratio)\n",
     "    set_cell!(at, CELL * ratio)\n",
     "    \n",
     "    x[k] = bond * ratio\n",
-    "    y[k] = TightBinding.potential_energy(at, tbm)\n",
-    "    println(ratio, y[k],\"\\n\")\n",
+    "    z[k] = TightBinding.potential_energy(at, tbm)\n",
+    "    println(\"ratio = \", ratio, \"  energy = \", y[k],\"\\n\")\n",
     "end"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -207,10 +331,10 @@
     {
      "data": {
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"
+       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"
       ],
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x89a9b50>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x5dca590>)"
       ]
      },
      "metadata": {},
@@ -220,10 +344,10 @@
      "data": {
       "text/plain": [
        "1-element Array{Any,1}:\n",
-       " PyObject <matplotlib.lines.Line2D object at 0x8b55350>"
+       " PyObject <matplotlib.lines.Line2D object at 0x5df2550>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,15 +356,6 @@
     "plot(x,y,\".-\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -259,23 +374,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "512"
+      "number of atoms = 216\n",
+      "lattice constant = 3.57"
      ]
     }
    ],
    "source": [
     "# create the supercell\n",
-    "n = 4\n",
+    "n = 3\n",
     "\n",
     "at = bulk(\"C\"; cubic=true)\n",
     "at = repeat(at, (n, n, n))\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "\n",
     "X = positions(at)\n",
-    "print(length(at))"
+    "print(\"number of atoms = \", length(at), \"\\n\")\n",
+    "\n",
+    "CELL = get_cell(at)\n",
+    "bond = CELL[1]/n\n",
+    "print(\"lattice constant = \", bond)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -283,43 +405,277 @@
     {
      "data": {
       "text/plain": [
-       "3.57"
+       "0.6392069108341737"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# set the fermi level\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.C_sp)\n",
+    "tbm.smearing.beta = 200\n",
+    "\n",
+    "TightBinding.potential_energy(at, tbm)\n",
+    "tbm.fixed_eF = false\n",
+    "TightBinding.update_eF!(at, tbm)\n",
+    "EF = tbm.eF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ratio = 0.85\t  energy = 67.64576561664973\n",
+      "ratio = 0.88\t  energy = 47.40792872629426\n",
+      "ratio = 0.91\t  energy = 33.526033241046086\n",
+      "ratio = 0.94\t  energy = 24.464788626021107\n",
+      "ratio = 0.97\t  energy = 19.13674237370315\n",
+      "ratio = 1.0\t  energy = 16.760142336013725\n",
+      "ratio = 1.03\t  energy = 16.744222445645047\n",
+      "ratio = 1.06\t  energy = 18.622950260205457\n",
+      "ratio = 1.09\t  energy = 22.020684697823746\n",
+      "ratio = 1.12\t  energy = 26.631012590985513\n",
+      "ratio = 1.15\t  energy = 32.19858349419396\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute total energy with different cell size\n",
+    "\n",
+    "m = 11\n",
+    "x = zeros(m)\n",
+    "y = zeros(m)\n",
+    "\n",
+    "for k = 1:m    \n",
+    "    ratio = 1.0 + (k-6) * 0.03\n",
+    "    set_positions!(at, X * ratio)\n",
+    "    set_cell!(at, CELL * ratio)\n",
+    "    \n",
+    "    # update the fermi level, which is necessary when the temperature is now very low\n",
+    "    # TightBinding.update!(at, tbm)\n",
+    "    \n",
+    "    x[k] = bond * ratio\n",
+    "    y[k] = TightBinding.potential_energy(at, tbm)\n",
+    "    println(\"ratio = \", ratio, \"\\t  energy = \", y[k])\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " original curtoff radius = 10.5\n",
+      " reduced cutoff radius = 9.1875\n",
+      "0.6243873450148406\n"
+     ]
+    }
+   ],
+   "source": [
+    "###### perform the same calculations with smaller cutoff radius\n",
+    "# reduce the cutoff radius by 1/8\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "# reset the atoms\n",
+    "at = bulk(\"C\"; cubic=true)\n",
+    "at = repeat(at, (n, n, n))\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "X = positions(at)\n",
     "CELL = get_cell(at)\n",
+    "bond = CELL[1]/n\n",
+    "\n",
+    "\n",
+    "# set the fermi level\n",
+    "Rc = NRLTB.C_sp.Rc\n",
+    "NRLTB.C_sp.Rc = Rc * 7/8\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.C_sp.Rc, \"\\n\")\n",
     "\n",
-    "bond = CELL[1]/n"
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.C_sp)\n",
+    "tbm.smearing.beta = 200\n",
+    "\n",
+    "TightBinding.potential_energy(at, tbm)\n",
+    "tbm.fixed_eF = false\n",
+    "TightBinding.update_eF!(at, tbm)\n",
+    "println(tbm.eF)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ratio = 0.85  energy = 12.852221273639158\n",
+      "ratio = 0.88  energy = -2.9190828255963073\n",
+      "ratio = 0.91  energy = -10.764029273976375\n",
+      "ratio = 0.94  energy = -12.744241010621916\n",
+      "ratio = 0.97  energy = -10.38659719530827\n",
+      "ratio = 1.0  energy = -4.821136466768914\n",
+      "ratio = 1.03  energy = 3.0549308893912506\n",
+      "ratio = 1.06  energy = 12.470592322040694\n",
+      "ratio = 1.09  energy = 23.026476073167085\n",
+      "ratio = 1.12  energy = 34.3172631383202\n",
+      "ratio = 1.15  energy = 45.98831534578391\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute total energy with different cell size\n",
+    "# reload(\"NRLTB\")\n",
+    "\n",
+    "m = 11\n",
+    "x = zeros(m)\n",
+    "z = zeros(m)\n",
+    "\n",
+    "for k = 1:m    \n",
+    "    ratio = 1.0 + (k-6) * 0.03\n",
+    "    set_positions!(at, X * ratio)\n",
+    "    set_cell!(at, CELL * ratio)\n",
+    "    \n",
+    "    # update the fermi level, which is necessary when the temperature is now very low\n",
+    "    TightBinding.update!(at, tbm)\n",
+    "    \n",
+    "    x[k] = bond * ratio\n",
+    "    z[k] = TightBinding.potential_energy(at, tbm)\n",
+    "    println(\"ratio = \", ratio, \"  energy = \", z[k])\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
+       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"
+      ],
+      "text/plain": [
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7abff90>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
-       "0.1509781246022319"
+       "PyObject <matplotlib.legend.Legend object at 0x696a250>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# set the fermi level\n",
+    "plot(x,y,\"*-\")\n",
+    "plot(x,z,\".--\")\n",
+    "legend((\"NRL-Rcut\", \"reduced(7/8)-Rcut\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### bond tests of Silicon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of atoms = 216\n",
+      "lattice constant = 5.43"
+     ]
+    }
+   ],
+   "source": [
+    "# create the supercell\n",
+    "n = 3\n",
     "\n",
+    "at = bulk(\"Si\"; cubic=true)\n",
+    "at = repeat(at, (n, n, n))\n",
     "set_pbc!(at, [true, true, true])\n",
-    "tbm = NRLTB.NRLTBModel(elem = NRLTB.C_sp)\n",
+    "\n",
+    "X = positions(at)\n",
+    "print(\"number of atoms = \", length(at), \"\\n\")\n",
+    "\n",
+    "CELL = get_cell(at)\n",
+    "bond = CELL[1]/n\n",
+    "print(\"lattice constant = \", bond)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.31772919355624474"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set the fermi level\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.Si_sp)\n",
     "tbm.smearing.beta = 200\n",
     "\n",
     "TightBinding.potential_energy(at, tbm)\n",
@@ -330,33 +686,152 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ratio = 0.85\t  energy = 40.43664570814637\n",
+      "ratio = 0.88\t  energy = 29.961811886670233\n",
+      "ratio = 0.91\t  energy = 21.199998620707298\n",
+      "ratio = 0.94\t  energy = 14.922430501834578\n",
+      "ratio = 0.97\t  energy = 11.31317364929463\n",
+      "ratio = 1.0\t  energy = 10.08402165816547\n",
+      "ratio = 1.03\t  energy = 10.918425230340542\n",
+      "ratio = 1.06\t  energy = 13.491415799680345\n",
+      "ratio = 1.09\t  energy = 17.463803452994505\n",
+      "ratio = 1.12\t  energy = 22.488895688888956\n",
+      "ratio = 1.15\t  energy = 28.135955899335833\n"
+     ]
+    }
+   ],
    "source": [
     "# compute total energy with different cell size\n",
     "\n",
-    "m = 10\n",
+    "m = 11\n",
     "x = zeros(m)\n",
     "y = zeros(m)\n",
     "\n",
     "for k = 1:m    \n",
-    "    ratio = 1.0 + (k-6) * 0.05\n",
-    "    println(ratio)\n",
+    "    ratio = 1.0 + (k-6) * 0.03\n",
     "    set_positions!(at, X * ratio)\n",
     "    set_cell!(at, CELL * ratio)\n",
     "    \n",
+    "    # update the fermi level, which is necessary when the temperature is now very low\n",
+    "    TightBinding.update!(at, tbm)\n",
+    "    \n",
     "    x[k] = bond * ratio\n",
     "    y[k] = TightBinding.potential_energy(at, tbm)\n",
-    "    println(ratio, y[k],\"\\n\")\n",
+    "    println(\"ratio = \", ratio, \"\\t  energy = \", y[k])\n",
     "end"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " original curtoff radius = 12.5\n",
+      " reduced cutoff radius = 10.416666666666666\n",
+      "0.3928117606696973\n"
+     ]
+    }
+   ],
+   "source": [
+    "###### perform the same calculations with smaller cutoff radius\n",
+    "# reduce the cutoff radius by 1/6\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "# reset the atoms\n",
+    "at = bulk(\"Si\"; cubic=true)\n",
+    "at = repeat(at, (n, n, n))\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "X = positions(at)\n",
+    "CELL = get_cell(at)\n",
+    "bond = CELL[1]/n\n",
+    "\n",
+    "\n",
+    "# set the fermi level\n",
+    "Rc = NRLTB.Si_sp.Rc\n",
+    "NRLTB.Si_sp.Rc = Rc * 5/6\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.Si_sp.Rc, \"\\n\")\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.Si_sp)\n",
+    "tbm.smearing.beta = 200\n",
+    "\n",
+    "TightBinding.potential_energy(at, tbm)\n",
+    "tbm.fixed_eF = false\n",
+    "TightBinding.update_eF!(at, tbm)\n",
+    "println(tbm.eF)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ratio = 0.85  energy = 10.719124676090493\n",
+      "ratio = 0.88  energy = 6.809765706087567\n",
+      "ratio = 0.91  energy = 5.3798215370580165\n",
+      "ratio = 0.94  energy = 6.438673485871115\n",
+      "ratio = 0.97  energy = 9.919971680445066\n",
+      "ratio = 1.0  energy = 15.571063507975834\n",
+      "ratio = 1.03  energy = 22.999797778245775\n",
+      "ratio = 1.06  energy = 31.81644248188988\n",
+      "ratio = 1.09  energy = 41.45364151691794\n",
+      "ratio = 1.12  energy = 51.69557032426117\n",
+      "ratio = 1.15  energy = 62.13233360224732\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute total energy with different cell size\n",
+    "# reload(\"NRLTB\")\n",
+    "\n",
+    "m = 11\n",
+    "x = zeros(m)\n",
+    "z = zeros(m)\n",
+    "\n",
+    "for k = 1:m    \n",
+    "    ratio = 1.0 + (k-6) * 0.03\n",
+    "    set_positions!(at, X * ratio)\n",
+    "    set_cell!(at, CELL * ratio)\n",
+    "    \n",
+    "    # update the fermi level, which is necessary when the temperature is now very low\n",
+    "    TightBinding.update!(at, tbm)\n",
+    "    \n",
+    "    x[k] = bond * ratio\n",
+    "    z[k] = TightBinding.potential_energy(at, tbm)\n",
+    "    println(\"ratio = \", ratio, \"  energy = \", z[k])\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -364,10 +839,10 @@
     {
      "data": {
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"
+       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"
       ],
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x48bed50>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x5476550>)"
       ]
      },
      "metadata": {},
@@ -376,17 +851,18 @@
     {
      "data": {
       "text/plain": [
-       "1-element Array{Any,1}:\n",
-       " PyObject <matplotlib.lines.Line2D object at 0x48ebd50>"
+       "PyObject <matplotlib.legend.Legend object at 0xd518210>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot(x,y,\".-\")"
+    "plot(x,y,\"*-\")\n",
+    "plot(x,z,\".--\")\n",
+    "legend((\"NRL-Rcut\", \"reduced(5/6)-Rcut\"))"
    ]
   },
   {
@@ -495,7 +971,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.4.1"
+   "version": "0.4.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Optimisation.ipynb b/notebooks/Optimisation.ipynb
index 67d745c..3162fb2 100644
--- a/notebooks/Optimisation.ipynb
+++ b/notebooks/Optimisation.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -28,7 +28,7 @@
        "MatSciPy.PairCalculator(Potentials.SWCutoff(Potentials.LennardJonesPotential(2.8637824638055176,1.0),7.159456159513794,1.0))"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,14 +47,12 @@
     "calc = MatSciPy.PairCalculator( SWCutoff( LennardJonesPotential(r0, 1.0), rcut, 1.0 ) )\n",
     "# calc = TightBinding.ToyTB.ToyTBModel(r0=r0, rcut=rcut)\n",
     "# TestAtoms.test_potentialenergy(calc, at)\n",
-    "# plot3D(X0[1,:][:], X0[2,:][:], X0[3,:][:], \"b.\")\n",
-    "\n",
-    "\n"
+    "# plot3D(X0[1,:][:], X0[2,:][:], X0[3,:][:], \"b.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -68,22 +66,22 @@
       " nit   |   ΔE        |∇E|∞     |Δx|∞      α        #E \n",
       "-------|---------------------------------------------------------\n",
       "     1 | -5.41e+03  1.80e+00  0.00e+00  1.00e+00      1 \n",
-      "     2 | -6.76e+00  4.95e-01  1.17e-01  1.00e+00      2 \n",
-      "     3 | -6.26e-01  2.03e-01  1.34e-01  9.63e-01      3 \n",
-      "     4 | -1.68e-01  1.14e-01  1.51e-01  1.13e+00      4 \n",
-      "     5 | -5.80e-02  5.27e-02  2.86e-01  1.68e+00      5 \n",
-      "     6 | -8.30e-03  4.36e-02  5.80e-01  1.91e+00      6 \n",
-      "     7 | -3.11e-03  2.05e-02  9.91e-02  1.08e+00      7 \n",
-      "     8 | -7.20e-04  8.23e-03  5.27e-02  7.91e-01      8 \n",
-      "     9 | -1.79e-04  6.09e-03  4.34e-02  8.21e-01      9 \n",
-      "    10 | -2.21e-04  8.34e-04  2.80e-01  2.90e+00     10 \n",
-      "    11 | -4.44e-06  4.24e-04  2.03e-01  3.19e+00     11 \n",
-      "    12 | -4.89e-07  9.99e-05  1.18e-01  8.17e-01     13 \n",
-      "    13 | -5.18e-08  5.60e-05  9.51e-02  7.67e-01     14 \n",
-      "    14 | -5.85e-08  3.29e-05  2.72e-01  1.53e+00     15 \n",
-      "    15 | -4.31e-08  7.60e-06  4.05e-01  4.02e+00     16 \n",
+      "     2 | -6.76e+00  4.95e-01  1.14e-01  1.00e+00      2 \n",
+      "     3 | -6.26e-01  2.03e-01  2.41e-01  9.63e-01      3 \n",
+      "     4 | -1.68e-01  1.14e-01  1.80e-01  1.13e+00      4 \n",
+      "     5 | -5.80e-02  5.27e-02  1.66e-01  1.68e+00      5 \n",
+      "     6 | -8.30e-03  4.36e-02  1.24e-01  1.91e+00      6 \n",
+      "     7 | -3.11e-03  2.05e-02  9.77e-02  1.08e+00      7 \n",
+      "     8 | -7.20e-04  8.23e-03  4.34e-02  7.91e-01      8 \n",
+      "     9 | -1.79e-04  6.09e-03  9.91e-02  8.21e-01      9 \n",
+      "    10 | -2.21e-04  8.34e-04  4.71e-01  2.90e+00     10 \n",
+      "    11 | -4.44e-06  4.24e-04  1.82e-01  3.19e+00     11 \n",
+      "    12 | -4.89e-07  9.99e-05  1.33e-01  8.17e-01     13 \n",
+      "    13 | -5.18e-08  5.60e-05  6.90e-02  7.67e-01     14 \n",
+      "    14 | -5.85e-08  3.29e-05  1.59e-01  1.53e+00     15 \n",
+      "    15 | -4.31e-08  7.60e-06  5.01e-01  4.02e+00     16 \n",
       "-------|---------------------------------------------------------\n",
-      "  2.484906 seconds (1.56 M allocations: 639.786 MB, 3.67% gc time)\n"
+      "  "
      ]
     }
    ],
@@ -95,14 +93,12 @@
     "X0 = positions(at)\n",
     "srand(12345)\n",
     "set_positions!(at, X0+0.05 * rand(size(X0)))\n",
-    "@time AtOptim.minimise!(at, calc, 1e-5; precon=precon, disp=3, maxnit=100);\n",
-    "\n",
-    "\n"
+    "@time AtOptim.minimise!(at, calc, 1e-5; precon=precon, disp=3, maxnit=100);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -115,53 +111,120 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 2.0206252e-03 \n",
-      "  3 | 2.0217136e-04 \n",
-      "  4 | 2.0217788e-05 \n",
-      "  5 | 2.0215844e-06 \n",
-      "  6 | 2.0621377e-07 \n",
-      "  7 | 7.2864390e-07 \n",
-      "  8 | 6.9510733e-06 \n",
-      "  9 | 7.3021015e-05 \n",
-      " 10 | 7.2520810e-04 \n",
+      "  2 | 1.0090802e-03 \n",
+      "  3 | 1.0092990e-04 \n",
+      "  4 | 1.0093127e-05 \n",
+      "  5 | 1.0102944e-06 \n",
+      "  6 | 1.1723648e-07 \n",
+      "  7 | 2.0862872e-07 \n",
+      "  8 | 1.6218559e-06 \n",
+      "  9 | 1.8961699e-05 \n",
+      " 10 | 1.7189014e-04 \n",
       "-----------------------------\n",
-      "  1.606599 seconds (29.20 M allocations: 837.522 MB, 8.18% gc time)\n",
+      "  0.605043 seconds (1.49 M allocations: 69.621 MB, 2.62% gc time)\n",
       "-------|---------------------------------------------------------\n",
       " nit   |   ΔE        |∇E|∞     |Δx|∞      α        #E \n",
       "-------|---------------------------------------------------------\n",
-      "     1 | -5.09e+01  2.14e-02  0.00e+00  1.00e+00      1 \n",
-      "     2 | -6.68e-03  1.19e-02  1.14e-01  1.00e+00      2 \n",
-      "     3 | -1.85e-03  3.97e-03  1.70e-01  6.78e-01      3 \n",
-      "     4 | -1.81e-04  1.78e-03  4.51e-02  5.93e-01      4 \n",
-      "     5 | -8.50e-05  1.49e-03  1.42e-01  6.41e-01      5 \n",
-      "     6 | -1.31e-04  1.02e-03  8.49e-01  1.43e+00      6 \n",
-      "     7 | -1.03e-04  1.05e-03  2.44e-01  1.09e+00      7 \n",
-      "     8 | -1.33e-05  1.07e-03  3.96e-04  1.09e-01      8 \n",
-      "     9 | -1.37e-06  1.07e-03  4.04e-05  1.09e-02      9 \n",
-      "    10 | -1.38e-07  1.07e-03  4.05e-06  1.09e-03     10 \n",
-      "    11 | -1.38e-08  1.07e-03  4.05e-07  1.09e-04     11 \n",
-      "    12 | -1.38e-09  1.07e-03  4.05e-08  1.09e-05     12 \n",
-      "    13 | -1.38e-10  1.07e-03  4.05e-09  1.09e-06     13 \n",
-      "    14 | -1.38e-11  1.07e-03  4.05e-10  1.09e-07     14 \n",
-      "  "
-     ]
-    },
-    {
-     "ename": "LoadError",
-     "evalue": "LoadError: ls_armijo! : alpha < alpha_min \nwhile loading In[6], in expression starting on line 11",
-     "output_type": "error",
-     "traceback": [
-      "LoadError: ls_armijo! : alpha < alpha_min \nwhile loading In[6], in expression starting on line 11",
-      "",
-      " in ls_armijo! at /Users/ortner/gits/Atoms.jl/AtOptim.jl:185",
-      " in minimise! at /Users/ortner/gits/Atoms.jl/AtOptim.jl:114"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  15 | -1.40e-12  1.07e-03  4.05e-11  1.09e-08     15 \n"
+      "     1 | -2.44e+01  1.17e-02  0.00e+00  1.00e+00      1 \n",
+      "     2 | -3.91e-03  3.46e-03  3.74e-01  4.11e-01      3 \n",
+      "     3 | -3.81e-04  1.83e-03  3.00e-01  3.81e-01      4 \n",
+      "     4 | -1.31e-04  1.07e-03  1.47e-01  4.05e-01      5 \n",
+      "     5 | -9.31e-05  7.38e-04  2.80e-01  6.60e-01      6 \n",
+      "     6 | -6.08e-05  6.82e-04  5.11e-01  8.87e-01      7 \n",
+      "     7 | -3.22e-05  6.16e-04  4.60e-01  4.63e-01      8 \n",
+      "     8 | -1.88e-05  5.92e-04  3.28e-01  2.44e-01      9 \n",
+      "     9 | -1.95e-06  5.93e-04  1.64e-02  2.44e-02     10 \n",
+      "    10 | -1.97e-07  5.93e-04  2.11e-03  2.44e-03     11 \n",
+      "    11 | -1.97e-08  5.93e-04  1.21e-04  2.44e-04     12 \n",
+      "    12 | -1.97e-09  5.93e-04  4.48e-05  2.44e-05     13 \n",
+      "    13 | -1.97e-10  5.93e-04  1.69e-06  2.44e-06     14 \n",
+      "    14 | -1.97e-09  5.93e-04  4.41e-05  2.44e-05     15 \n",
+      "    15 | -1.97e-10  5.93e-04  1.59e-06  2.44e-06     16 \n",
+      "    16 | -1.97e-09  5.93e-04  6.09e-05  2.44e-05     17 \n",
+      "    17 | -1.97e-10  5.93e-04  1.19e-06  2.44e-06     18 \n",
+      "    18 | -1.97e-11  5.93e-04  1.54e-07  2.44e-07     19 \n",
+      "    19 | -1.97e-10  5.93e-04  1.80e-06  2.44e-06     20 \n",
+      "    20 | -1.97e-11  5.93e-04  8.09e-08  2.44e-07     21 \n",
+      "    21 | -1.97e-10  5.93e-04  1.55e-06  2.44e-06     22 \n",
+      "    22 | -1.97e-11  5.93e-04  3.38e-08  2.44e-07     23 \n",
+      "    23 | -1.97e-10  5.93e-04  9.72e-07  2.44e-06     24 \n",
+      "    24 | -1.97e-11  5.93e-04  5.37e-08  2.44e-07     25 \n",
+      "    25 | -1.97e-10  5.93e-04  3.40e-06  2.44e-06     26 \n",
+      "    26 | -1.97e-09  5.93e-04  1.02e-05  2.44e-05     27 \n",
+      "    27 | -1.97e-10  5.93e-04  8.08e-07  2.44e-06     28 \n",
+      "    28 | -1.97e-09  5.93e-04  2.03e-05  2.44e-05     29 \n",
+      "    29 | -1.97e-10  5.93e-04  4.69e-06  2.44e-06     30 \n",
+      "    30 | -1.97e-09  5.93e-04  9.23e-06  2.44e-05     31 \n",
+      "    31 | -1.97e-10  5.93e-04  1.71e-06  2.44e-06     32 \n",
+      "    32 | -1.97e-09  5.93e-04  2.25e-05  2.44e-05     33 \n",
+      "    33 | -1.97e-10  5.93e-04  2.78e-06  2.44e-06     34 \n",
+      "    34 | -1.97e-11  5.93e-04  2.06e-07  2.44e-07     35 \n",
+      "    35 | -1.97e-10  5.93e-04  2.26e-06  2.44e-06     36 \n",
+      "    36 | -1.97e-11  5.93e-04  3.13e-07  2.44e-07     37 \n",
+      "    37 | -1.97e-10  5.93e-04  3.99e-06  2.44e-06     38 \n",
+      "    38 | -1.97e-09  5.93e-04  2.26e-05  2.44e-05     39 \n",
+      "    39 | -1.97e-10  5.93e-04  1.09e-06  2.44e-06     40 \n",
+      "    40 | -1.97e-09  5.93e-04  9.57e-06  2.44e-05     41 \n",
+      "    41 | -1.97e-10  5.93e-04  1.41e-06  2.44e-06     42 \n",
+      "    42 | -1.97e-11  5.93e-04  1.78e-07  2.44e-07     43 \n",
+      "    43 | -1.97e-10  5.93e-04  1.64e-06  2.44e-06     44 \n",
+      "    44 | -1.97e-09  5.93e-04  1.81e-05  2.44e-05     45 \n",
+      "    45 | -1.97e-10  5.93e-04  1.94e-06  2.44e-06     46 \n",
+      "    46 | -1.97e-09  5.93e-04  1.54e-05  2.44e-05     47 \n",
+      "    47 | -1.97e-10  5.93e-04  1.46e-06  2.44e-06     48 \n",
+      "    48 | -1.97e-09  5.93e-04  4.12e-05  2.44e-05     49 \n",
+      "    49 | -1.97e-10  5.93e-04  2.75e-06  2.44e-06     50 \n",
+      "    50 | -1.97e-09  5.93e-04  1.21e-05  2.44e-05     51 \n",
+      "    51 | -1.97e-10  5.93e-04  2.62e-06  2.44e-06     52 \n",
+      "    52 | -1.97e-09  5.93e-04  1.03e-05  2.44e-05     53 \n",
+      "    53 | -1.97e-10  5.93e-04  1.62e-06  2.44e-06     54 \n",
+      "    54 | -1.97e-09  5.93e-04  3.94e-05  2.44e-05     55 \n",
+      "    55 | -1.97e-10  5.93e-04  1.70e-06  2.44e-06     56 \n",
+      "    56 | -1.97e-09  5.93e-04  3.74e-05  2.44e-05     57 \n",
+      "    57 | -1.97e-10  5.93e-04  6.46e-06  2.44e-06     58 \n",
+      "    58 | -1.97e-09  5.93e-04  1.40e-05  2.44e-05     59 \n",
+      "    59 | -1.97e-10  5.93e-04  2.71e-06  2.44e-06     60 \n",
+      "    60 | -1.97e-09  5.93e-04  1.62e-05  2.44e-05     61 \n",
+      "    61 | -1.97e-10  5.93e-04  7.80e-07  2.44e-06     62 \n",
+      "    62 | -1.97e-09  5.93e-04  2.69e-05  2.44e-05     63 \n",
+      "    63 | -1.97e-10  5.93e-04  2.43e-06  2.44e-06     64 \n",
+      "    64 | -1.97e-09  5.93e-04  1.50e-05  2.44e-05     65 \n",
+      "    65 | -1.97e-10  5.93e-04  3.21e-06  2.44e-06     66 \n",
+      "    66 | -1.97e-09  5.93e-04  1.39e-05  2.44e-05     67 \n",
+      "    67 | -1.97e-10  5.93e-04  2.53e-06  2.44e-06     68 \n",
+      "    68 | -1.97e-09  5.93e-04  1.82e-05  2.44e-05     69 \n",
+      "    69 | -1.97e-10  5.93e-04  1.66e-06  2.44e-06     70 \n",
+      "    70 | -1.97e-09  5.93e-04  2.99e-05  2.44e-05     71 \n",
+      "    71 | -1.97e-10  5.93e-04  2.58e-06  2.44e-06     72 \n",
+      "    72 | -1.97e-09  5.93e-04  3.85e-05  2.44e-05     73 \n",
+      "    73 | -1.97e-10  5.93e-04  1.98e-06  2.44e-06     74 \n",
+      "    74 | -1.97e-11  5.93e-04  2.35e-07  2.44e-07     75 \n",
+      "    75 | -1.97e-10  5.93e-04  1.21e-06  2.44e-06     76 \n",
+      "    76 | -1.97e-09  5.93e-04  3.24e-05  2.44e-05     77 \n",
+      "    77 | -1.97e-10  5.93e-04  4.70e-06  2.44e-06     78 \n",
+      "    78 | -1.97e-09  5.93e-04  2.64e-05  2.44e-05     79 \n",
+      "    79 | -1.97e-10  5.93e-04  1.95e-06  2.44e-06     80 \n",
+      "    80 | -1.97e-09  5.93e-04  2.47e-05  2.44e-05     81 \n",
+      "    81 | -1.97e-10  5.93e-04  1.19e-06  2.44e-06     82 \n",
+      "    82 | -1.97e-09  5.93e-04  1.37e-05  2.44e-05     83 \n",
+      "    83 | -1.97e-10  5.93e-04  2.35e-06  2.44e-06     84 \n",
+      "    84 | -1.97e-09  5.93e-04  1.14e-05  2.44e-05     85 \n",
+      "    85 | -1.97e-10  5.93e-04  1.08e-06  2.44e-06     86 \n",
+      "    86 | -1.97e-09  5.93e-04  3.18e-05  2.44e-05     87 \n",
+      "    87 | -1.97e-10  5.93e-04  8.82e-07  2.44e-06     88 \n",
+      "    88 | -1.97e-09  5.93e-04  3.98e-05  2.44e-05     89 \n",
+      "    89 | -1.97e-10  5.93e-04  5.19e-06  2.44e-06     90 \n",
+      "    90 | -1.97e-09  5.93e-04  6.14e-06  2.44e-05     91 \n",
+      "    91 | -1.97e-10  5.93e-04  1.44e-06  2.44e-06     92 \n",
+      "    92 | -1.97e-09  5.93e-04  3.16e-05  2.44e-05     93 \n",
+      "    93 | -1.97e-10  5.93e-04  1.30e-06  2.44e-06     94 \n",
+      "    94 | -1.97e-11  5.93e-04  1.58e-07  2.44e-07     95 \n",
+      "    95 | -1.97e-10  5.93e-04  2.87e-06  2.44e-06     96 \n",
+      "    96 | -1.97e-09  5.93e-04  5.44e-05  2.44e-05     97 \n",
+      "    97 | -1.97e-10  5.93e-04  8.86e-07  2.44e-06     98 \n",
+      "    98 | -1.97e-09  5.93e-04  4.13e-05  2.44e-05     99 \n",
+      "    99 | -1.97e-10  5.93e-04  9.00e-07  2.44e-06    100 \n",
+      "   100 | -1.97e-11  5.93e-04  1.49e-07  2.44e-07    101 \n"
      ]
     }
    ],
@@ -172,12 +235,11 @@
     "srand(12345)\n",
     "set_positions!(at, X0+0.05 * rand(size(X0)))\n",
     "calc = TightBinding.ToyTB.ToyTBModel(r0=r0, rcut=rcut)\n",
-    "calc.nkpoints = (0,0,1)\n",
+    "calc.nkpoints = (0,0,2)\n",
     "TestAtoms.test_potentialenergy(calc, at)\n",
     "@time precon = AtPrecon.ExpPrecon(at, calc); precon.cstab = 0.1\n",
     "# precon = AtPrecon.IdPrecon()\n",
-    "AtOptim.minimise!(at, calc, 1e-4; precon=precon, disp=3, maxnit=100);\n",
-    "\n"
+    "AtOptim.minimise!(at, calc, 1e-4; precon=precon, disp=3, maxnit=100);"
    ]
   },
   {
@@ -382,7 +444,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 0.4.3",
+   "display_name": "Julia 0.4.0-rc4",
    "language": "julia",
    "name": "julia-0.4"
   },
@@ -390,7 +452,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.4.3"
+   "version": "0.4.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/TB-BandStructure.ipynb b/notebooks/TB-BandStructure.ipynb
index 090fed5..641f9e2 100644
--- a/notebooks/TB-BandStructure.ipynb
+++ b/notebooks/TB-BandStructure.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -94,7 +94,7 @@
        "0.750234150411399"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -110,7 +110,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module TightBinding\n",
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " original curtoff radius = 10.5\n",
+      " reduced cutoff radius = 8.75"
+     ]
+    }
+   ],
+   "source": [
+    "### computation with a reduced cutoff radius\n",
+    "reload(\"TightBinding\")\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "Rc = NRLTB.C_sp.Rc\n",
+    "NRLTB.C_sp.Rc = Rc * 5/6\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.C_sp.Rc)\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.C_sp)\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "tbm.nkpoints = (2,2,2)\n",
+    "\n",
+    "K, E_rR = TightBinding.band_structure_all(at, tbm);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -118,10 +158,10 @@
     {
      "data": {
       "image/png": [
-       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"
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"
       ],
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0xcbd4bd0>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x10e40810>)"
       ]
      },
      "metadata": {},
@@ -131,10 +171,10 @@
      "data": {
       "text/plain": [
        "1-element Array{Any,1}:\n",
-       " PyObject <matplotlib.lines.Line2D object at 0x9337d10>"
+       " PyObject <matplotlib.lines.Line2D object at 0xa003450>"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,11 +184,18 @@
     "\n",
     "F = E[:]\n",
     "IsortF = sortperm(F[:])\n",
-    "plot(HAR * F[IsortF], \".\")\n",
+    "plot(HAR * F[IsortF], \"b*\")\n",
+    "hold\n",
+    "\n",
+    "FrR = E_rR[:]\n",
+    "IsortFrR = sortperm(FrR[:])\n",
+    "plot(HAR * FrR[IsortFrR], \"r.\")\n",
     "\n",
     "x = 1:7000\n",
-    "y = x.^0 * tbm.eF * HAR\n",
-    "plot(x,y, \"k-\")"
+    "y = x.^0 * EF * HAR\n",
+    "plot(x,y, \"k-\")\n",
+    "\n",
+    "# axis([3400, 3600, 15, 30])"
    ]
   },
   {
@@ -433,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -454,7 +501,7 @@
        "  0.0    0.0   16.29"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -474,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -501,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -509,10 +556,10 @@
     {
      "data": {
       "text/plain": [
-       "0.44800343057900505"
+       "0.4509239975638428"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -528,7 +575,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: replacing module TightBinding\n",
+      "WARNING: replacing module NRLTB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " original curtoff radius = 12.5\n",
+      " reduced cutoff radius = 10.416666666666666"
+     ]
+    }
+   ],
+   "source": [
+    "### computation with a reduced cutoff radius\n",
+    "reload(\"TightBinding\")\n",
+    "reload(\"NRLTB\")\n",
+    "\n",
+    "Rc = NRLTB.Si_sp.Rc\n",
+    "NRLTB.Si_sp.Rc = Rc * 5/6\n",
+    "print(\" original curtoff radius = \", Rc, \"\\n reduced cutoff radius = \", NRLTB.Si_sp.Rc)\n",
+    "\n",
+    "tbm = NRLTB.NRLTBModel(elem = NRLTB.Si_sp)\n",
+    "set_pbc!(at, [true, true, true])\n",
+    "tbm.nkpoints = (2,2,2)\n",
+    "\n",
+    "K, E_rR = TightBinding.band_structure_all(at, tbm);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -536,10 +623,10 @@
     {
      "data": {
       "image/png": [
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"
+       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"
       ],
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x58f4190>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x46e1250>)"
       ]
      },
      "metadata": {},
@@ -549,10 +636,10 @@
      "data": {
       "text/plain": [
        "1-element Array{Any,1}:\n",
-       " PyObject <matplotlib.lines.Line2D object at 0xd160f50>"
+       " PyObject <matplotlib.lines.Line2D object at 0x1015d190>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,9 +650,14 @@
     "F = E[:]\n",
     "IsortF = sortperm(F[:])\n",
     "plot(HAR * F[IsortF], \".\")\n",
+    "hold\n",
+    "\n",
+    "FrR = E_rR[:]\n",
+    "IsortFrR = sortperm(FrR[:])\n",
+    "plot(HAR * FrR[IsortFrR], \"r.\")\n",
     "\n",
     "x = 1:7000\n",
-    "y = x.^0 * tbm.eF * HAR\n",
+    "y = x.^0 * EF * HAR\n",
     "plot(x,y, \"k-\")"
    ]
   },
@@ -851,7 +943,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.4.1"
+   "version": "0.4.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/TightBinding Tests.ipynb b/notebooks/TightBinding Tests.ipynb
index 7023ed6..5424f5a 100644
--- a/notebooks/TightBinding Tests.ipynb	
+++ b/notebooks/TightBinding Tests.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -224,16 +224,16 @@
       "  p | error \n",
       "----|------------------------\n",
       "  2 | 3.9513270e-04 \n",
-      "  3 | 3.9401830e-05 \n",
-      "  4 | 3.9391108e-06 \n",
-      "  5 | 3.9409857e-07 \n",
-      "  6 | 4.1020036e-08 \n",
-      "  7 | 2.5032824e-08 \n",
-      "  8 | 1.7763568e-07 \n",
-      "  9 | 2.2220586e-06 \n",
-      " 10 | 2.2646937e-05 \n",
+      "  3 | 3.9401826e-05 \n",
+      "  4 | 3.9390578e-06 \n",
+      "  5 | 3.9374294e-07 \n",
+      "  6 | 3.8355864e-08 \n",
+      "  7 | 2.5033061e-08 \n",
+      "  8 | 1.7924798e-07 \n",
+      "  9 | 2.2220583e-06 \n",
+      " 10 | 2.2646938e-05 \n",
       " 11 | 2.6157016e-04 \n",
-      " 12 | 2.3931984e-03 \n",
+      " 12 | 2.9358722e-03 \n",
       "-----------------------------\n"
      ]
     }
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -299,15 +299,15 @@
       "----|------------------------\n",
       "  2 | 2.9797294e-03 \n",
       "  3 | 2.9797806e-04 \n",
-      "  4 | 2.9797810e-05 \n",
-      "  5 | 2.9798046e-06 \n",
-      "  6 | 2.9807753e-07 \n",
-      "  7 | 3.0982161e-08 \n",
-      "  8 | 3.8411311e-08 \n",
-      "  9 | 8.1532003e-07 \n",
-      " 10 | 5.0653925e-06 \n",
-      " 11 | 5.6677931e-05 \n",
-      " 12 | 3.8463572e-04 \n",
+      "  4 | 2.9797814e-05 \n",
+      "  5 | 2.9797817e-06 \n",
+      "  6 | 2.9793876e-07 \n",
+      "  7 | 3.4069969e-08 \n",
+      "  8 | 6.3837824e-08 \n",
+      "  9 | 3.4694470e-07 \n",
+      " 10 | 6.1062266e-06 \n",
+      " 11 | 7.8062556e-05 \n",
+      " 12 | 5.3776428e-04 \n",
       "-----------------------------\n"
      ]
     }
@@ -325,9 +325,8 @@
     "tbm = NRLTB.NRLTBModel(elem = NRLTB.Al_spd)\n",
     "tbm.nkpoints = (0,0,0)\n",
     "\n",
-    "s=3\n",
+    "s=1\n",
     "\n",
-    "tbm.nkpoints = (0,0,0)\n",
     "K, weight = TightBinding.monkhorstpackgrid(at, tbm)\n",
     "X = positions(at)\n",
     "TightBinding.update!(at, tbm)\n",
@@ -369,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -392,17 +391,17 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 9.7554539e-04 \n",
-      "  3 | 9.6720799e-05 \n",
-      "  4 | 9.6638225e-06 \n",
-      "  5 | 9.6630632e-07 \n",
-      "  6 | 9.6901536e-08 \n",
-      "  7 | 1.2941155e-08 \n",
-      "  8 | 4.7184479e-08 \n",
-      "  9 | 2.9143354e-07 \n",
-      " 10 | 1.6653345e-06 \n",
-      " 11 | 2.6829532e-05 \n",
-      " 12 | 3.3306691e-04 \n",
+      "  2 | 2.4388635e-04 \n",
+      "  3 | 2.4180200e-05 \n",
+      "  4 | 2.4159564e-06 \n",
+      "  5 | 2.4158272e-07 \n",
+      "  6 | 2.4305288e-08 \n",
+      "  7 | 4.2067818e-09 \n",
+      "  8 | 1.0174231e-08 \n",
+      "  9 | 1.8135322e-07 \n",
+      " 10 | 1.0999275e-06 \n",
+      " 11 | 1.1680686e-05 \n",
+      " 12 | 6.3722390e-05 \n",
       "-----------------------------\n"
      ]
     }
@@ -445,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -468,17 +467,17 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 9.8529916e-03 \n",
-      "  3 | 8.4250365e-03 \n",
-      "  4 | 8.4250060e-03 \n",
-      "  5 | 8.4250057e-03 \n",
-      "  6 | 8.4250057e-03 \n",
-      "  7 | 8.4250059e-03 \n",
-      "  8 | 8.4250068e-03 \n",
-      "  9 | 8.4250621e-03 \n",
-      " 10 | 8.4253601e-03 \n",
-      " 11 | 8.4351862e-03 \n",
-      " 12 | 8.4546478e-03 \n",
+      "  2 | 8.9340052e-04 \n",
+      "  3 | 8.8183320e-05 \n",
+      "  4 | 8.8068202e-06 \n",
+      "  5 | 8.8056893e-07 \n",
+      "  6 | 8.8183577e-08 \n",
+      "  7 | 9.0303267e-09 \n",
+      "  8 | 2.3402119e-08 \n",
+      "  9 | 2.8103497e-07 \n",
+      " 10 | 1.3704218e-06 \n",
+      " 11 | 1.7746211e-05 \n",
+      " 12 | 1.1403567e-04 \n",
       "-----------------------------\n"
      ]
     }
@@ -497,9 +496,6 @@
     "tbm.nkpoints = (0,0,0)\n",
     "\n",
     "X = positions(at)\n",
-    "#        X[2,2] += 1.0e-15\n",
-    "#        X[3,2] -= 1.0e-15\n",
-    "#        set_positions!(at, X)\n",
     "Hess = TightBinding.hessian(at, tbm)\n",
     "f = reshape(Hess, 3*length(at), 3*length(at))\n",
     "Tens = TightBinding.d3E(at, tbm)\n",
@@ -525,6 +521,28 @@
     "println(\"-----------------------------\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 24.355606 seconds (161.55 M allocations: 9.630 GB, 11.06% gc time)\n",
+      " 24.813793 seconds (161.55 M allocations: 9.630 GB, 10.71% gc time)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test the time for d3E\n",
+    "@time TightBinding.d3E(at, tbm);\n",
+    "@time TightBinding.d3E(at, tbm);"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 51,
@@ -596,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -619,17 +637,17 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 1.5930766e-03 \n",
-      "  3 | 1.5576992e-04 \n",
-      "  4 | 1.5541830e-05 \n",
-      "  5 | 1.5538196e-06 \n",
-      "  6 | 1.5564084e-07 \n",
-      "  7 | 1.6554841e-08 \n",
-      "  8 | 1.6653345e-08 \n",
-      "  9 | 2.5295189e-07 \n",
-      " 10 | 1.6344273e-06 \n",
-      " 11 | 3.1332893e-05 \n",
-      " 12 | 3.3386867e-04 \n",
+      "  2 | 9.5899932e-05 \n",
+      "  3 | 9.5900283e-06 \n",
+      "  4 | 9.5900611e-07 \n",
+      "  5 | 9.5903936e-08 \n",
+      "  6 | 9.7450780e-09 \n",
+      "  7 | 4.1882209e-09 \n",
+      "  8 | 4.8154754e-08 \n",
+      "  9 | 2.9517938e-07 \n",
+      " 10 | 4.7203659e-06 \n",
+      " 11 | 3.4545560e-05 \n",
+      " 12 | 2.3161015e-04 \n",
       "-----------------------------\n"
      ]
     }
@@ -688,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -711,17 +729,17 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 1.3503005e+02 \n",
-      "  3 | 1.7916602e-04 \n",
-      "  4 | 1.7902184e-05 \n",
-      "  5 | 1.7900218e-06 \n",
-      "  6 | 1.7952114e-07 \n",
-      "  7 | 1.5319154e-08 \n",
-      "  8 | 7.3291674e-08 \n",
-      "  9 | 6.1528752e-07 \n",
-      " 10 | 1.3937964e-05 \n",
-      " 11 | 6.9539720e-05 \n",
-      " 12 | 8.7991192e-04 \n",
+      "  2 | 4.9673185e-04 \n",
+      "  3 | 1.3000268e+03 \n",
+      "  4 | 1.3001546e+04 \n",
+      "  5 | 1.3001533e+05 \n",
+      "  6 | 1.3001548e+06 \n",
+      "  7 | 1.3001546e+07 \n",
+      "  8 | 2.4740555e-07 \n",
+      "  9 | 1.3001546e+09 \n",
+      " 10 | 2.9972865e-05 \n",
+      " 11 | 1.3001546e+11 \n",
+      " 12 | 2.9975990e-03 \n",
       "-----------------------------\n"
      ]
     }
@@ -781,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -803,15 +821,15 @@
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 8.0597075e-04 \n",
-      "  3 | 8.0563563e-05 \n",
-      "  4 | 8.0563202e-06 \n",
-      "  5 | 8.0566677e-07 \n",
-      "  6 | 8.0383845e-08 \n",
-      "  7 | 8.8819055e-09 \n",
-      "  8 | 2.2204055e-07 \n",
-      "  9 | 2.2204443e-06 \n",
-      " 10 | 1.3322688e-05 \n",
+      "  2 | 1.9246636e-04 \n",
+      "  3 | 1.9247712e-05 \n",
+      "  4 | 1.9247743e-06 \n",
+      "  5 | 1.9250385e-07 \n",
+      "  6 | 1.9726388e-08 \n",
+      "  7 | 3.6830786e-09 \n",
+      "  8 | 2.3546291e-08 \n",
+      "  9 | 2.2070277e-07 \n",
+      " 10 | 3.3293272e-06 \n",
       "-----------------------------\n"
      ]
     }
@@ -829,7 +847,7 @@
     "\n",
     "set_pbc!(at, [true, true, true])\n",
     "tbm = NRLTB.NRLTBModel(elem = NRLTB.Al_spd)\n",
-    "tbm.nkpoints = (2,8,2)\n",
+    "tbm.nkpoints = (0,2,4)\n",
     "\n",
     "\n",
     "X = positions(at)\n",
@@ -863,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -872,22 +890,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4"
+      "6"
      ]
     }
    ],
    "source": [
-    "at = bulk(\"Al\")#; cubic=true)\n",
-    "at = repeat(at, (1, 2, 2))\n",
+    "at = bulk(\"Al\") #; cubic=true)\n",
+    "at = repeat(at, (1, 2, 3))\n",
     "X = positions(at)\n",
-    "# set_pbc!(at, [false, false, false])\n",
+    "set_pbc!(at, [false, false, false])\n",
     "# plot3D(X[1,:][:], X[2,:][:], X[3,:][:], \"b.\")\n",
     "print(length(at))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -906,30 +924,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "E - ∑ E_i = 5.329070518200751e-15\n",
+      "E - ∑ E_i = -1.0658141036401503e-14\n",
       "------------------------------\n",
       "Finite-difference test\n",
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 1.8566967e-04 \n",
-      "  3 | 1.8596655e-05 \n",
-      "  4 | 1.8600000e-06 \n",
-      "  5 | 1.8629885e-07 \n",
-      "  6 | 2.0875614e-08 \n",
-      "  7 | 3.4353978e-08 \n",
-      "  8 | 2.5728062e-07 \n",
-      "  9 | 3.7453059e-06 \n",
-      " 10 | 2.9216484e-05 \n",
-      " 11 | 2.1344333e-04 \n",
-      " 12 | 3.4997035e-03 \n",
+      "  2 | 1.8723765e-04 \n",
+      "  3 | 1.8753995e-05 \n",
+      "  4 | 1.8756845e-06 \n",
+      "  5 | 1.8734170e-07 \n",
+      "  6 | 1.6832353e-08 \n",
+      "  7 | 3.6902442e-08 \n",
+      "  8 | 3.1859084e-07 \n",
+      "  9 | 4.5285329e-06 \n",
+      " 10 | 3.5460745e-05 \n",
+      " 11 | 4.1085812e-04 \n",
+      " 12 | 5.0581097e-03 \n",
       "-----------------------------\n"
      ]
     }
    ],
    "source": [
     "# TEST NRL-TB site energy for Aluminum FCC\n",
-    "# WITH open BOUNDARY CONDITION ON THIRD DIMENSION\n",
+    "# WITH open BOUNDARY CONDITION \n",
     "\n",
     "using AtomsInterface\n",
     "reload(\"SparseTools\")\n",
@@ -971,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -990,30 +1008,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "E - ∑ E_i = 0.0\n",
+      "E - ∑ E_i = -1.7763568394002505e-15\n",
       "------------------------------\n",
       "Finite-difference test\n",
       "-----------------------------\n",
       "  p | error \n",
       "----|------------------------\n",
-      "  2 | 1.0063240e-03 \n",
-      "  3 | 1.0064698e-04 \n",
-      "  4 | 1.0064709e-05 \n",
-      "  5 | 1.0063949e-06 \n",
-      "  6 | 9.9476247e-08 \n",
-      "  7 | 2.2204517e-08 \n",
-      "  8 | 2.6645355e-07 \n",
-      "  9 | 1.7763569e-06 \n",
-      " 10 | 1.3322676e-05 \n",
-      " 11 | 3.1086245e-04 \n",
-      " 12 | 2.2204460e-03 \n",
+      "  2 | 1.0275612e-04 \n",
+      "  3 | 1.0275015e-05 \n",
+      "  4 | 1.0274886e-06 \n",
+      "  5 | 1.0271511e-07 \n",
+      "  6 | 9.9161716e-09 \n",
+      "  7 | 8.4521695e-09 \n",
+      "  8 | 8.2039690e-08 \n",
+      "  9 | 9.7064306e-07 \n",
+      " 10 | 9.0142620e-06 \n",
+      " 11 | 8.5313861e-05 \n",
+      " 12 | 7.9791681e-04 \n",
       "-----------------------------\n"
      ]
     }
    ],
    "source": [
     "# TEST NRL-TB site energy for Aluminum FCC\n",
-    "# WITH periodic BOUNDARY CONDITION ON THIRD DIMENSION\n",
+    "# WITH periodic BOUNDARY CONDITION ON FIRST DIMENSION\n",
     "\n",
     "using AtomsInterface\n",
     "reload(\"SparseTools\")\n",
@@ -1021,9 +1039,9 @@
     "reload(\"TightBinding\")\n",
     "reload(\"NRLTB\")  \n",
     "\n",
-    "set_pbc!(at, [false, true, true])\n",
+    "set_pbc!(at, [true, false, false])\n",
     "tbm = NRLTB.NRLTBModel(elem = NRLTB.Al_spd)\n",
-    "tbm.nkpoints = (0,0,0)\n",
+    "tbm.nkpoints = (0,0,4)\n",
     "\n",
     "X = positions(at)\n",
     "\n",