def pltf(atomi, atomj, gen):
atoms = read(gen)
ao = AtomOP(atoms)
# pairs = [[1,2],[13,7],[5,26]]
pairs = [[0, 1]]
images = ao.stretch(pairs, nbin=50, wtraj=True)
ao.close()
# view(images)
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=True)
e_, r_ = [], []
b_, f_ = [], []
for atoms in images:
ir.calculate(atoms)
# positions = atoms.get_positions()
# r = np.sqrt(np.sum(np.square(positions[1]-positions[0])))
r_.append(ir.r[atomi][atomj])
e_.append(atoms.get_potential_energy())
b_.append(ir.H[0][atomi][atomj])
f_.append(ir.F[-1][atomi][atomj])
fig, ax = plt.subplots()
plt.plot(r_,
f_,
label=r'$f_{NN}$ vs $BO^t=0$',
color='blue',
linewidth=2,
linestyle='-')
# plt.plot(b_,f_,label=r'$f_{NN}$ vs $BO^{t=0}$', color='blue',
# linewidth=2, linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('Estretch.svg')
# plt.show()
plt.close()
def deb_energy(images, debframe=[], i=6, j=8, show=False):
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', nn=True)
ir.calculate_Delta(images[0])
Eb, Ea, e = [], [], []
Ehb, Eo, Ev, Eu, El = [], [], [], [], []
Etor, Ef, Ep, Et = [], [], [], []
for i_, atoms in enumerate(images):
ir.calculate(images[i_])
# print('%d Energies: ' %i_,'%12.4f ' %ir.E, 'Ebd: %8.4f' %ir.ebond[0][1],'Ebd: %8.4f' %ir.ebond[2][3] )
Eb.append(ir.Ebond)
Ea.append(ir.Eang)
Eo.append(ir.Eover)
Ev.append(ir.Evdw)
Eu.append(ir.Eunder)
El.append(ir.Elone)
Ep.append(ir.Epen)
Et.append(ir.Etcon)
Ef.append(ir.Efcon)
Etor.append(ir.Etor)
Ehb.append(ir.Ehb)
e.append(ir.E)
plot(e, Eb, Eu, Eo, El, Ea, Et, Ep, Etor, Ef, Ev, Ehb, show=show)
return e
def ple(traj='md.traj'):
images = Trajectory(traj)
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', rcut=None, nn=True)
e, e_, r_ = [], [], []
for atoms in images:
# for i in range(62):
# atoms = images[i]
e.append(atoms.get_potential_energy())
ir.calculate(atoms)
# r_.append(ir.r[atomi][atomj])
e_.append(ir.E)
fig, ax = plt.subplots()
plt.plot(e,
label=r'$DFT$ ($SIESTA$)',
color='red',
markeredgewidth=1,
ms=5,
alpha=0.8,
linewidth=2,
linestyle='-')
# plt.plot(e,label=r'$Potential$ $Energy$', color='red',
# marker='^',markerfacecolor='none',
# markeredgewidth=1,
# ms=5,alpha=0.8,
# linewidth=1, linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('popeng.svg', transparent=True)
plt.close()
def bde(ffield='ffield.json', nn='T', gen='poscar.gen', traj='C2H4.traj'):
images = Trajectory(traj)
atoms = images[0]
nn_ = True if nn == 'T' else False
ir = IRFF_NP(atoms=atoms, libfile=ffield, rcut=None, nn=nn_)
natom = ir.natom
Empnn, Esiesta = [], []
eb, eb_ = [], []
for atoms in images:
ir.calculate(atoms)
Empnn.append(ir.E)
Esiesta.append(atoms.get_potential_energy())
# eb.append(ir.ebond[i][j])
fig, ax = plt.subplots()
plt.plot(Empnn[0:14],
label=r'$E_{MPNN}$',
color='blue',
linewidth=2,
linestyle='-.')
plt.plot(Esiesta[0:14],
label=r'$E_{SIESTA}$',
color='r',
linewidth=2,
linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('Ecompare.pdf')
plt.show()
plt.close()
def plev():
atoms = Atoms('H2',
positions=[(0, 0, 0), (r, 0, 0)],
cell=[10.0, 10.0, 10.0],
pbc=[1, 1, 1])
ao = AtomDance(atoms)
pairs = [0, 1]
images = ao.stretch(pairs,
nbin=50,
st=0.6,
ed=5.0,
scale=1.25,
traj='evdw.traj')
ao.close()
# view(images)
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=True)
ev_, r_ = [], []
for atoms in images:
ir.calculate(atoms)
r_.append(ir.r[0][1])
ev_.append(ir.Evdw)
fig, ax = plt.subplots()
plt.plot(r_,
ev_,
label=r'$Evdw$ VS $r$',
color='blue',
linewidth=2,
linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('Evdw.eps')
plt.close()
def get_theta(atoms, figsize=(8, 6)):
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', nn=True)
ir.calculate(atoms)
for a, angle in enumerate(ir.angs):
i, j, k = angle
print(
'{:3d} {:3d} {:3d} {:3d} {:6.4f} {:6.4f} Dpi: {:6.4f} SBO: {:6.4f} pbo: {:6.4f} SBO3: {:6.4f}'
.format(a, i, j, k, ir.thet0[a], ir.theta[a], ir.sbo[a], ir.SBO[a],
ir.pbo[a], ir.SBO3[a])) # self.thet0-self.theta
def deb_vdw(images, i=0, j=1, show=False):
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', nn=True)
ir.calculate_Delta(images[0])
Eb, Ea, e = [], [], []
Ehb, Eo, Ev, Eu, El = [], [], [], [], []
Etor, Ef, Ep, Et = [], [], [], []
for i_, atoms in enumerate(images):
ir.calculate(images[i_])
# print('%d Energies: ' %i_,'%12.4f ' %ir.E, 'Ebd: %8.4f' %ir.ebond[0][1],'Ebd: %8.4f' %ir.ebond[2][3] )
Eb.append(ir.Ebond)
Ea.append(ir.Eang)
Eo.append(ir.Eover)
Ev.append(ir.Evdw)
Eu.append(ir.Eunder)
El.append(ir.Elone)
Ep.append(ir.Epen)
Et.append(ir.Etcon)
Ef.append(ir.Efcon)
Etor.append(ir.Etor)
Ehb.append(ir.Ehb)
e.append(ir.E)
emin_ = np.min(Eb)
eb = np.array(Eb) - emin_ # )/emin_
vmin_ = np.min(Ev)
ev = np.array(EV) - vmin_ # )/emin_
plt.figure(figsize=figsize)
# plt.plot(bopsi,alpha=0.8,linewidth=2,linestyle=':',color='k',label=r'$BO_p^{\sigma}$')
# plt.plot(boppi,alpha=0.8,linewidth=2,linestyle='-.',color='k',label=r'$BO_p^{\pi}$')
# plt.plot(boppp,alpha=0.8,linewidth=2,linestyle='--',color='k',label=r'$BO_p^{\pi\pi}$')
# plt.plot(bo0,alpha=0.8,linewidth=2,linestyle='-',color='g',label=r'$BO^{t=0}$')
plt.plot(ev,
alpha=0.8,
linewidth=2,
linestyle='-',
color='y',
label=r'$E_{vdw}$')
plt.plot(eb,
alpha=0.8,
linewidth=2,
linestyle='-',
color='r',
label=r'$E_{bond}$')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('deb_bo.pdf')
if show: plt.show()
plt.close()
return eb, ev
def pleo(atomi,traj='md.traj'):
images = Trajectory(traj)
ir = IRFF_NP(atoms=images[0],
libfile='ffield.json',
rcut=None,
nn=True)
el_,eu_,eo_,r_ = [],[],[],[]
delta = []
for atoms in images:
ir.calculate(atoms)
# r_.append(ir.r[atomi][atomj])
eo_.append(ir.eover[atomi])
eu_.append(ir.eunder[atomi])
el_.append(ir.elone[atomi])
delta.append(ir.Delta[atomi])
print('Delta_e:',ir.Delta_e[atomi],'Delta_lp:',ir.Delta_lp[atomi],
'Delta_lpcorr:',ir.Delta_lpcorr[atomi])
fig, ax = plt.subplots()
plt.plot(delta,eo_,label=r'$E_{over}$ VS $Radius$', color='blue',
linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Eover.pdf')
plt.close()
fig, ax = plt.subplots()
plt.plot(delta,eo_,label=r'$E_{over}$', color='blue',
linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Eover.pdf')
plt.close()
fig, ax = plt.subplots()
plt.plot(delta,eu_,label=r'$E_{under}$', color='blue',
linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Eunder.pdf')
plt.close()
fig, ax = plt.subplots()
plt.plot(el_,label=r'$E_{lone}$', color='blue',
linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Elone.pdf')
plt.close()
def eover(i=0, j=1, ffield='ffield.json', nn='T', traj='md.traj'):
# atoms = read(traj)
# ao = AtomDance(atoms)
# images = ao.stretch([[i,j]],nbin=50,traj=False)
images = Trajectory(traj)
atoms = images[0]
nn_ = True if nn == 'T' else False
ir = IRFF_NP(atoms=atoms, libfile=ffield, rcut=None, nn=nn_)
ir.calculate_Delta(atoms)
natom = ir.natom
r_,eb,bosi,bop_si,bop,bop_pi,bop_pp,bo = [],[],[],[],[],[],[],[]
eba, eo, dlpi, dlpj, ev, boe = [], [], [], [], [], []
esi, epi, epp = [], [], []
Di, Dj = [], []
Dpi = []
for atoms in images:
positions = atoms.positions
v = positions[j] - positions[i]
r = np.sqrt(np.sum(np.square(v)))
ir.calculate(atoms)
r_.append(ir.r[i][j])
eb.append(ir.ebond[i][j])
eba.append(ir.ebond[i][j] + ir.eover[i] + ir.Evdw)
ev.append(ir.Evdw)
eo.append(ir.Eover)
# print(ir.so[j],ir.eover[j])
dlpi.append(ir.Delta_lpcorr[i])
dlpj.append(ir.Delta_lpcorr[j])
Di.append(ir.Delta[i])
Dj.append(ir.Delta[j])
Dpi.append(ir.Dpil[j])
fig, ax = plt.subplots()
ax.plot(eo, label=r'$E_{over}$', color='r', linewidth=2, linestyle='-')
# fig, ax = plt.subplots(2,1,2)
# plt.plot(r_,dlpj,label=r'$\Delta_{lp}$(%s%d)' %(ir.atom_name[j],j),
# color='b', linewidth=2, linestyle='-') # Dpil
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('Eover.pdf')
# plt.show()
plt.close()
def deb_eang(images, ang=[0, 1, 2], figsize=(8, 6), show=False, print_=False):
i, j, k = ang
ang_ = [k, j, i]
a = 0
found = False
eang, ecoa, epen = [], [], []
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', nn=True)
ir.calculate_Delta(images[0])
for na, angle in enumerate(ir.angs):
i_, j_, k_ = angle
if (i_ == i and j_ == j and k_ == k) or (i_ == k and j_ == j
and k_ == i):
a = na
found = True
if not found:
print('Error: no angle found for', ang, angle)
for i_, atoms in enumerate(images):
ir.calculate(atoms)
eang.append(ir.Eang)
ecoa.append(ir.Etcon)
epen.append(ir.Epen)
if print_:
# for a,angle in enumerate(ir.angs):
#i_,j_,k_ = angle
print(
'{:3d} {:6.4f} {:6.4f} Dpi: {:6.4f} pbo: {:6.4f} N: {:6.4f} SBO3: {:6.4f}'
.format(i_, ir.thet0[a], ir.theta[a], ir.sbo[a], ir.pbo[a],
ir.nlp[j], ir.SBO3[a])) # self.thet0-self.theta
plt.figure(figsize=figsize)
plt.plot(eang,
alpha=0.8,
linewidth=2,
linestyle='-',
color='r',
label=r'$E_{ang}$') # ($-E_{ang}/{:4.2f}$)'.format(ang_m))
# plt.plot(ecoa,alpha=0.8,linewidth=2,linestyle='-',color='indigo',label=r'$E_{coa}$') # ($E_{coa}/%4.2f$)' %emx)
# plt.plot(epen,alpha=0.8,linewidth=2,linestyle='-',color='b',label=r'$E_{pen}$') # ($E_{pen}/%4.2f$)' %eox)
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('deb_ang.pdf')
if show: plt.show()
plt.close()
def LearningResult_angel(ffield='ffield.json',
nn='T',
gen='poscar.gen',
traj='C1N1O2H30.traj'):
images = Trajectory(traj)
traj_ = TrajectoryWriter(traj[:-5] + '_.traj', mode='w')
atoms = images[0]
nn_ = True if nn == 'T' else False
ir = IRFF_NP(atoms=atoms, libfile=ffield, rcut=None, nn=nn_)
natom = ir.natom
Empnn, Esiesta = [], []
eb, eb_ = [], []
eang_ = []
theta0 = []
l = len(images)
for _ in range(l - 10, l):
atoms = images[_]
ir.calculate(atoms)
Empnn.append(ir.E)
Esiesta.append(atoms.get_potential_energy())
atoms_ = atoms.copy()
atoms_.set_initial_charges(charges=ir.q)
calc = SinglePointCalculator(atoms_, energy=ir.E)
atoms_.set_calculator(calc)
traj_.write(atoms=atoms_)
eang_.append(ir.Eang)
# print(ir.Eang)
# print(ir.SBO3)
for a, ang in enumerate(ir.angi):
th0 = ir.thet0[a] #*180.0/3.14159
th = ir.theta[a] # *180.0/3.14159
i, j, k = ir.angi[a][0], ir.angj[a][0], ir.angk[a][0]
print(
'%s-%s-%s' % (ir.atom_name[i], ir.atom_name[j], ir.atom_name[k]),
'thet0: %8.6f' % th0,
'thet: %f8.6' % th,
'sbo3 %8.6f:' % ir.SBO3[a],
'Dpi: %8.6f' % ir.Dpi[ir.angj[a][0]],
'pbo: %8.6f' % ir.PBO[ir.angj[a][0]],
'nlp: %8.6f' % ir.nlp[ir.angj[a][0]],
'Dang: %8.6f' % ir.Dang[ir.angj[a][0]],
# 'eang:',ir.eang[a],'expang:',ir.expang[a],
)
def LearningResultAngel(ffield='ffield.json',
nn='T',
gen='poscar.gen',
traj='C2H4-1.traj'):
images = Trajectory(traj)
traj_ = TrajectoryWriter(traj[:-5] + '_.traj', mode='w')
atoms = images[0]
nn_ = True if nn == 'T' else False
ir = IRFF_NP(atoms=atoms, libfile=ffield, rcut=None, nn=nn_)
natom = ir.natom
Empnn, Esiesta = [], []
eb, eb_ = [], []
# images_= []
# for _ in range(0,50):
eang_ = []
for _, atoms in enumerate(images):
# atoms = images[_]
ir.calculate(atoms)
Empnn.append(ir.E)
Esiesta.append(atoms.get_potential_energy())
atoms_ = atoms.copy()
atoms_.set_initial_charges(charges=ir.q)
calc = SinglePointCalculator(atoms_, energy=ir.E)
atoms_.set_calculator(calc)
traj_.write(atoms=atoms_)
# print(ir.Eang)
eang_.append(ir.Eang)
traj_.close()
fig, ax = plt.subplots()
plt.plot(eang_,
label=r'$E_{Angle}$',
color='blue',
linewidth=2,
linestyle='-.')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('Eang-%s.eps' % traj[:-4])
# plt.show()
plt.close()
def learning_result(ffield='ffield.json',
nn='T',
gen='poscar.gen',
traj='C2H4.traj'):
images = Trajectory(traj)
traj_ = TrajectoryWriter(traj[:-5] + '_.traj', mode='w')
atoms = images[0]
nn_ = True if nn == 'T' else False
ir = IRFF_NP(atoms=atoms, libfile=ffield, rcut=None, nn=nn_)
natom = ir.natom
Empnn, Esiesta = [], []
eb, eb_ = [], []
# images_= []
# for _ in range(10,11):
for atoms in images:
ir.calculate(atoms)
Empnn.append(ir.E)
Esiesta.append(atoms.get_potential_energy())
atoms_ = atoms.copy()
atoms_.set_initial_charges(charges=ir.q)
calc = SinglePointCalculator(atoms_, energy=ir.E)
atoms_.set_calculator(calc)
traj_.write(atoms=atoms_)
traj_.close()
fig, ax = plt.subplots()
plt.plot(Empnn,
label=r'$E_{MPNN}$',
color='blue',
linewidth=2,
linestyle='-.')
plt.plot(Esiesta,
label=r'$E_{SIESTA}$',
color='r',
linewidth=2,
linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('result-%s.pdf' % traj[:-4])
plt.show()
plt.close()
def pleb(atomi=0,atomj=1,traj='md.traj'):
images = Trajectory(traj)
ir = IRFF_NP(atoms=images[0],
libfile='ffield.json',
rcut=None,
nn=True)
e_,r_ = [],[]
for atoms in images:
ir.calculate(atoms)
r_.append(ir.r[atomi][atomj])
e_.append(ir.ebond[atomi][atomj])
fig, ax = plt.subplots()
plt.plot(r_,e_,label=r'$E_{Bond}$ VS $Radius$', color='blue',
linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Ebond.eps')
plt.close()
def get_mole(traj='nm-0.traj'):
images = Trajectory(traj)
tframe = len(images)
x_ = [i for i in range(tframe)]
e,e_ = [],[]
mol_ = traj.split('.')[0]
mol = mol_.split('-')[0]
ir = IRFF_NP(atoms=images[0],
libfile='ffield.json',
rcut=None,
nn=True,vdwnn=True)
for i,atoms in enumerate(images):
energy = atoms.get_potential_energy()
ir.calculate(atoms)
e_.append(ir.E)
e.append(energy)
emol = np.mean(e) - np.mean(e_)
print('- recommended molecular energy for %s is :' %mol,emol)
return emol
def ple(traj='md.traj', dE=0.15, d2E=0.05, Etole=0.1):
images = Trajectory(traj)
tframe = len(images)
x_ = [i for i in range(tframe)]
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', rcut=None, nn=True)
energies, e_ = [], []
dEs, d2Es = [], []
ind_, labels = [], []
dE_ = 0.0
d2E_ = 0.0
fig, ax = plt.subplots()
with open('SinglePointEnergies.log', 'r') as fs:
in_, edft = [], []
for line in fs.readlines():
l = line.split()
i_ = int(l[0])
e = float(l[2])
e_ = float(l[4])
dE = float(l[8])
d2E = float(l[10])
in_.append(i_)
edft.append(e_)
for i, atoms in enumerate(images):
energy = atoms.get_potential_energy()
ir.calculate(atoms)
# e_.append(ir.E)
if i > 0: ###########
if i < (tframe - 1):
deltEl = energy - energies[-1]
deltEr = images[i + 1].get_potential_energy() - energy
dE_ = abs(deltEl)
d2E_ = abs(deltEr - deltEl)
else:
deltEl = energy - energies[-1]
dE_ = abs(deltEl)
if i in in_: ###########
ind_.append(i)
labels.append(energy)
energies.append(energy)
dEs.append(dE_)
d2Es.append(d2E_)
print(' * differential:', dE_, d2E_)
i_, edft_, labels_ = [], [], []
for _, i in enumerate(ind_):
if abs(edft[_] - labels[_]) > Etole:
i_.append(i)
edft_.append(edft[_])
labels_.append(labels[_])
le = (len(energies) - 1) * 0.1
dle = le / 100.0
plt.xlim(-dle, le + dle)
emin = np.min(energies)
edft_ = np.array(edft_)
edft = np.array(edft)
labels_ = np.array(labels_)
labels = np.array(labels)
energies = np.array(energies)
in_ = np.array(in_)
i_ = np.array(i_)
x_ = np.array(x_)
plt.ylabel('Energy (unit: eV)')
plt.xlabel('Time (unit: fs)')
plt.scatter(i_ * 0.1,
labels_ - emin,
color='none',
edgecolors='red',
linewidths=2,
marker='*',
s=150,
label=r'$Labeled$ $Data$',
alpha=1.0)
err = edft - labels
plt.errorbar(in_ * 0.1,
edft - emin,
yerr=err,
fmt='s',
ecolor='red',
color='none',
ms=7,
markerfacecolor='none',
mec='red',
elinewidth=2,
capsize=2,
label=r'$True$ $Value(DFT)$')
plt.plot(x_ * 0.1,
energies - emin,
label=r'$IRFF$($MPNN$) $Predictions$',
color='blue',
marker='o',
markerfacecolor='none',
markeredgewidth=1,
ms=3,
alpha=0.8,
linewidth=1,
linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('popeng.eps', transparents=True)
plt.close()
class AtomOP(object):
def __init__(self, atoms=None, rtole=0.5):
self.rtole = rtole
if atoms is None:
gen_ = 'md.traj' if isfile('md.traj') else 'poscar.gen'
atoms = read(gen_, index=-1)
self.ir = IRFF_NP(atoms=atoms,
libfile='ffield.json',
rcut=None,
nn=True)
self.natom = self.ir.natom
self.atom_name = self.ir.atom_name
spec = self.ir.spec
label_dic = {}
for sp in self.atom_name:
if sp in label_dic:
label_dic[sp] += 1
else:
label_dic[sp] = 1
self.label = ''
for sp in spec:
self.label += sp + str(label_dic[sp])
def check(self, wcheck=2, i=0, atoms=None, rtole=None):
if atoms is None:
atoms = self.ir.atoms
if not rtole is None:
self.rtole = rtole
self.ir.calculate_Delta(atoms, updateP=True)
fc = open('check.log', 'w')
if i % wcheck == 0:
atoms = self.checkLoneAtoms(atoms, fc)
else:
atoms = self.checkLoneAtom(atoms, fc)
atoms = self.checkClosedAtom(atoms, fc)
fc.close()
return atoms
def checkLoneAtom(self, atoms, fc):
for i in range(self.natom):
if self.ir.Delta[i] <= self.ir.atol:
print('- find an lone atom', i, self.atom_name[i], file=fc)
sid = np.argsort(self.ir.r[i])
for j in sid:
if self.ir.r[i][j] > 0.0001:
print(' move lone atom to nearest neighbor: %d' % j,
file=fc)
vr = self.ir.vr[i][j]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
j] + u * 0.64 * self.ir.r_cuta[i][j]
break
self.ir.calculate_Delta(atoms)
return atoms
def checkLoneAtoms(self, atoms, fc):
for i in range(self.natom):
if self.ir.Delta[i] <= self.ir.atol:
print('- find an lone atom', i, self.atom_name[i], file=fc)
mid = np.argmin(self.ir.ND)
if mid == i:
continue
print('- find the most atractive atom:', mid, file=fc)
print('\n- neighors of atom %d %s:' % (i, self.atom_name[i]),
end='',
file=fc)
neighs = []
for j, bo in enumerate(self.ir.bo0[mid]):
if bo > self.ir.botol:
neighs.append(j)
print(j, self.atom_name[j], end='', file=fc)
print(' ', file=fc)
if len(neighs) == 0:
vr = self.ir.vr[mid][i]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 1:
j = neighs[0]
vr = self.ir.vr[mid][j]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 2:
i_, j_ = neighs
xj = atoms.positions[mid]
xi = 0.5 * (atoms.positions[i_] + atoms.positions[j_])
vr = xj - xi
u = vr / np.sqrt(np.sum(np.square(vr)))
vij = atoms.positions[j_] - atoms.positions[i_]
rij = np.sqrt(np.sum(np.square(vij)))
r_ = np.dot(vij, u)
if r_ != rij:
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 3:
i_, j_, k_ = neighs
vi = atoms.positions[i_] - atoms.positions[j_]
vj = atoms.positions[i_] - atoms.positions[k_]
# cross product
vr = np.cross(vi, vj)
c = (atoms.positions[i_] + atoms.positions[j_] +
atoms.positions[k_]) / 3
v = atoms.positions[mid] - c
u = vr / np.sqrt(np.sum(np.square(vr)))
# dot product
dot = np.dot(v, u)
if dot <= 0:
u = -u
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
self.ir.calculate_Delta(atoms)
return atoms
def checkClosedAtom(self, atoms, fc):
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, self.ir.r_cuta)
for i in range(self.natom - 1):
for j in range(i + 1, self.natom):
if self.ir.r[i][j] < self.rtole * self.ir.r_cuta[i][j]:
print('- atoms %d and %d too closed' % (i, j), file=fc)
moveDirection = self.ir.vr[j][i] / self.ir.r[i][j]
moveD = self.ir.r_cuta[i][j] * (self.rtole +
0.01) - self.ir.r[i][j]
moveV = moveD * moveDirection
ToBeMove = []
ToBeMove = getAtomsToMove(i, j, ToBeMove, neighbors)
print(' atoms to to be moved:', ToBeMove, file=fc)
for m in ToBeMove:
newPos = atoms.positions[m] + moveV
r = np.sqrt(
np.sum(np.square(newPos - atoms.positions[i])))
if r > self.ir.r[i][m]:
atoms.positions[m] = newPos
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r,
self.ir.r_cuta)
return atoms
def stretch(self, pairs, nbin=20, scale=1.2, wtraj=False):
atoms = self.ir.atoms
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, scale * self.ir.re)
images = []
if wtraj: his = TrajectoryWriter('stretch.traj', mode='w')
for pair in pairs:
i, j = pair
ToBeMove = []
ToBeMove = getAtomsToMove(i, j, ToBeMove, neighbors)
bin_ = (self.ir.r_cuta[i][j] -
self.ir.re[i][j] * self.rtole) / nbin
moveDirection = self.ir.vr[j][i] / self.ir.r[i][j]
for n in range(nbin):
atoms_ = atoms.copy()
moveV = atoms.positions[i] + moveDirection * (
self.ir.re[i][j] * self.rtole +
bin_ * n) - atoms.positions[j]
# print(self.ir.re[i][j]*self.rtole+bin_*n)
for m in ToBeMove:
# sPos = atoms.positions[i] + self.ir.re[i][m]*self.rtole*moveDirection
newPos = atoms.positions[m] + moveV
r = np.sqrt(np.sum(np.square(newPos - atoms.positions[i])))
atoms_.positions[m] = newPos
self.ir.calculate(atoms_)
i_ = np.where(
np.logical_and(
self.ir.r < self.ir.re[i][j] * self.rtole - bin_,
self.ir.r > 0.0001))
n = len(i_[0])
try:
assert n == 0, 'Atoms too closed!'
except:
print('Atoms too closed.')
break
calc = SinglePointCalculator(atoms_, energy=self.ir.E)
atoms_.set_calculator(calc)
images.append(atoms_)
if wtraj: his.write(atoms=atoms_)
if wtraj: his.close()
return images
def close(self):
self.ir = None
self.atom_name = None
def energies(rmin=0.4,ffield='ffield.json',nn='T',traj='md.traj',massages=2):
images= Trajectory(traj)
atoms = images[0]
ir = IRFF_NP(atoms=atoms,
libfile=ffield,
rcut=None,
nn=nn,massages=massages)
ir.calculate(atoms)
natom = ir.natom
r_,eb,bosi,bop_si,esi,bop,bop_pi,bop_pp = [],[],[],[],[],[],[],[]
e,eo,eu,el,ea,ep,et,ev,eh,ef,ec,etc,es = [],[],[],[],[],[],[],[],[],[],[],[],[]
for atoms in images:
ir.calculate(atoms)
eb.append(ir.Ebond)
e.append(ir.E)
eo.append(ir.Eover)
eu.append(ir.Eunder)
el.append(ir.Elone)
ea.append(ir.Eang)
ep.append(ir.Epen)
et.append(ir.Etor)
ef.append(ir.Efcon)
ev.append(ir.Evdw)
eh.append(ir.Ehb)
ec.append(ir.Ecoul)
etc.append(ir.Etcon)
es.append(ir.Eself)
plt.figure()
plt.subplot(4,3,1)
plt.plot(e,label=r'$E_{tot}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,2)
#plt.ylabel( r'$\sigma$ Bond-Energy (eV)')
# plt.xlabel(r'$Radius$ $(Angstrom)$')
plt.plot(eb,label=r'$E_{bond}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,3)
plt.plot(eo,label=r'$E_{over}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,4)
plt.plot(eu,label=r'$E_{under}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,5)
plt.plot(el,label=r'$E_{lone}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,6)
plt.plot(ea,label=r'$E_{angle}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,7)
plt.plot(ep,label=r'$E_{penalty}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,8)
plt.plot(et,label=r'$E_{tor}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,9)
plt.plot(ef,label=r'$E_{four}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,10)
plt.plot(ev,label=r'$E_{vdw}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,11)
plt.plot(eh,label=r'$E_{hbond}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(4,3,12)
plt.plot(ec,label=r'$E_{coulb}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Energies.eps')
plt.close()
def e(traj='md.traj', nn=True):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[0]
e, e_ = [], []
eb, el, eo, eu = [], [], [], []
ea, ep, etc = [], [], []
et, ef = [], []
ev, eh, ec = [], [], []
eb_, el_, eo_, eu_ = [], [], [], []
ea_, ep_, etc_ = [], [], []
et_, ef_ = [], []
ev_, eh_, ec_ = [], [], []
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn, autograd=False)
for i, atoms in enumerate(images):
ir.calculate(atoms)
e.append(ir.E)
eb.append(ir.Ebond.numpy())
el.append(ir.Elone.numpy())
eo.append(ir.Eover.numpy())
eu.append(ir.Eunder.numpy())
ea.append(ir.Eang.numpy())
ep.append(ir.Epen.numpy())
et.append(ir.Etor.numpy())
ef.append(ir.Efcon.numpy())
etc.append(ir.Etcon.numpy())
ev.append(ir.Evdw.numpy())
eh.append(ir.Ehb.numpy())
ec.append(ir.Ecoul.numpy())
ir_ = IRFF_NP(atoms=atoms, libfile=ffield, nn=nn)
for i, atoms in enumerate(images):
ir_.calculate(atoms)
e_.append(ir_.E)
eb_.append(ir_.Ebond)
el_.append(ir_.Elone)
eo_.append(ir_.Eover)
eu_.append(ir_.Eunder)
ea_.append(ir_.Eang)
ep_.append(ir_.Epen)
et_.append(ir_.Etor)
ef_.append(ir_.Efcon)
etc_.append(ir_.Etcon)
ev_.append(ir_.Evdw)
eh_.append(ir_.Ehb)
ec_.append(ir_.Ecoul)
e_irffnp = {
'Energy': e,
'Ebond': eb,
'Eunger': eu,
'Eover': eo,
'Eang': ea,
'Epen': ep,
'Elone': el,
'Etcon': etc,
'Etor': et,
'Efcon': ef,
'Evdw': ev,
'Ecoul': ec,
'Ehbond': eh
}
e_irff = {
'Energy': e_,
'Ebond': eb_,
'Eunger': eu_,
'Eover': eo_,
'Eang': ea_,
'Epen': ep_,
'Elone': el_,
'Etcon': etc_,
'Etor': et_,
'Efcon': ef_,
'Evdw': ev_,
'Ecoul': ec_,
'Ehbond': eh_
}
for key in e_irffnp:
plt.figure()
plt.ylabel('%s (eV)' % key)
plt.xlabel('Step')
plt.plot(e_irffnp[key],
alpha=0.01,
linestyle='-.',
marker='o',
markerfacecolor='none',
markeredgewidth=1,
markeredgecolor='b',
markersize=4,
color='blue',
label='IRFF_NP')
plt.plot(e_irff[key],
alpha=0.01,
linestyle=':',
marker='^',
markerfacecolor='none',
markeredgewidth=1,
markeredgecolor='red',
markersize=4,
color='red',
label='IRFF')
plt.legend(loc='best',
edgecolor='yellowgreen') # lower left upper right
plt.savefig('%s.eps' % key)
plt.close()
def p3d(traj='ps0.1.lammpstrj',
frame=0,
atomType=['C', 'H', 'O', 'N'],
color={
'C': 'g',
'H': 'khaki',
'O': 'r',
'N': 'b'
},
size={
'C': 80,
'H': 40,
'O': 76,
'N': 76
}):
# atoms = LammpsHistory(traj=traj,frame=frame,atomType=atomType)
atoms = read('poscar.gen')
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=True)
ir.calculate(atoms)
fig = plt.figure()
ax = fig.gca(projection='3d')
# set figure information
ax.set_title("Atomic Configuration")
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
e = []
for e_ in ir.ebond:
for em in e_:
e.append(em)
# print(e)
mine = min(e)
cmap = cm.ScalarMappable(col.Normalize(mine, 0.0), cm.rainbow)
for i in range(ir.natom):
print('- ploting bonds for atom {0} ... '.format(i), end='\r')
for j in range(ir.natom):
bd = ir.atom_name[i] + '-' + ir.atom_name[j]
r = np.sqrt(
np.sum(np.square(atoms.positions[j] - atoms.positions[i])))
if j > i:
if ir.r[i][j] < ir.rcut[bd]:
x = [atoms.positions[i][0], atoms.positions[j][0]]
y = [atoms.positions[i][1], atoms.positions[j][1]]
z = [atoms.positions[i][2], atoms.positions[j][2]]
ax.plot(x,
y,
z,
c=cmap.to_rgba(ir.ebond[i][j]),
linewidth=1)
print(' ', end='\n')
# for a in atoms:
# ax.scatter(a.x, a.y, a.z, c=color[a.symbol],s=size[a.symbol],label=a.symbol)
ca = np.linspace(mine, 0, 100)
cmap.set_array(ca)
plt.colorbar(cmap, label='Color Map(Unit: eV)')
# plt.show()
plt.savefig('bondEnergy3d.eps')
plt.close()
def ep(traj='opt.traj', efun=3, batch_size=50, nn=True):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
e = []
eb, el, eo, eu = [], [], [], []
ea, ep, etc = [], [], []
et, ef = [], []
ev, eh, ec = [], [], []
ir = IRFF_NP(atoms=images[0], libfile=ffield, nn=nn)
for i, atoms in enumerate(images):
ir.calculate(atoms)
e.append(ir.E)
eb.append(ir.Ebond)
el.append(ir.Elone)
eo.append(ir.Eover)
eu.append(ir.Eunder)
ea.append(ir.Eang)
ep.append(ir.Epen)
et.append(ir.Etor)
ef.append(ir.Efcon)
etc.append(ir.Etcon)
ev.append(ir.Evdw)
eh.append(ir.Ehb)
ec.append(ir.Ecoul)
rn = MPNN(libfile=ffield,
direcs={'tmp': traj},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
bo_layer=[9, 2],
EnergyFunction=efun,
pkl=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
mol = 'tmp'
e_ = rn.get_value(rn.E[mol])
eb_ = rn.get_value(rn.ebond[mol])
el_ = rn.get_value(rn.elone[mol])
eu_ = rn.get_value(rn.eunder[mol])
eo_ = rn.get_value(rn.eover[mol])
ea_ = rn.get_value(rn.eang[mol])
ep_ = rn.get_value(rn.epen[mol])
etc_ = rn.get_value(rn.tconj[mol])
et_ = rn.get_value(rn.etor[mol])
ef_ = rn.get_value(rn.efcon[mol])
ev_ = rn.get_value(rn.evdw[mol])
ec_ = rn.get_value(rn.ecoul[mol])
eh_ = rn.get_value(rn.ehb[mol])
e_reax = {
'Energy': e,
'Ebond': eb,
'Eunger': eu,
'Eover': eo,
'Eang': ea,
'Epen': ep,
'Elone': el,
'Etcon': etc,
'Etor': et,
'Efcon': ef,
'Evdw': ev,
'Ecoul': ec,
'Ehbond': eh
}
e_irff = {
'Energy': e_,
'Ebond': eb_,
'Eunger': eu_,
'Eover': eo_,
'Eang': ea_,
'Epen': ep_,
'Elone': el_,
'Etcon': etc_,
'Etor': et_,
'Efcon': ef_,
'Evdw': ev_,
'Ecoul': ec_,
'Ehbond': eh_
}
for key in e_reax:
plt.figure()
plt.ylabel('%s (eV)' % key)
plt.xlabel('Step')
plt.plot(e_reax[key],
alpha=0.01,
linestyle='-.',
marker='o',
markerfacecolor='none',
markeredgewidth=1,
markeredgecolor='b',
markersize=4,
color='blue',
label='ReaxFF')
plt.plot(e_irff[key],
alpha=0.01,
linestyle=':',
marker='^',
markerfacecolor='none',
markeredgewidth=1,
markeredgecolor='red',
markersize=4,
color='red',
label='IRFF')
plt.legend(loc='best',
edgecolor='yellowgreen') # lower left upper right
plt.savefig('%s.eps' % key)
plt.close()
def messagePassing(atoms,
color={
'C': 'dimgray',
'H': 'silver',
'O': 'crimson',
'N': 'dodgerblue'
},
size={
'C': 320,
'H': 90,
'O': 180,
'N': 320
},
bondColor='darkgoldenrod',
boxColor='steelblue',
bondWidth=1,
latticeWidth=2,
bocut=0.0001,
elev=45,
azim=45,
Axis=True,
Box=True,
t=0,
text='edge',
labelnode=False,
ball_scale=20,
ray_scale=100,
n_ray_steps=10,
show=False,
show_element=False,
figname='messagepassing.svg'):
''' avilable colors: ghostwhite whitesmoke olive '''
positions = atoms.get_positions()
sym = atoms.get_chemical_symbols()
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=True)
ir.calculate(atoms)
normalized_charges = ir.q.copy()
normalized_charges[np.isnan(normalized_charges)] = 0
max_charge = np.max(np.abs(normalized_charges))
normalized_charges /= max_charge
normalized_charges = (normalized_charges + 1) / 2
color_map = plt.get_cmap("bwr_r")
colors = color_map(normalized_charges)
ball_sizes = np.array([ir.p['rvdw_' + e]
for e in ir.atom_name]) * ball_scale
ray_full_sizes = ball_sizes + np.abs(ir.q) * ray_scale
ray_sizes = np.array([
np.linspace(ray_full_sizes[i],
ball_sizes[i],
n_ray_steps,
endpoint=False) for i in range(ir.natom)
]).T
# plot scatter points
# fig, ax = plt.subplots()
fig = plt.figure(figsize=(8.0, 6.0))
ax = fig.add_subplot(111, projection="3d")
bonds = []
bondColors = [] #color_map(0.5)
bWs = []
for i in range(ir.natom - 1):
for j in range(i + 1, ir.natom):
if ir.H[t][i][j] > bocut:
bonds.append((atoms.positions[i], atoms.positions[j]))
ax.text(0.5 * (atoms.positions[i][0] + atoms.positions[j][0]),
0.5 * (atoms.positions[i][1] + atoms.positions[j][1]),
0.5 * (atoms.positions[i][2] + atoms.positions[j][2]),
r'$%3.3f$' % ir.H[t][i][j],
ha="center",
va="center",
zorder=100,
fontsize=16,
color='k')
bondColors.append(
color_map(0.5 *
(normalized_charges[i] + normalized_charges[j])))
bWs.append(ir.H[t][i][j])
sticks = Line3DCollection(bonds,
color=bondColors,
linewidths=bondWidth * np.array(bWs),
alpha=0.8)
ax.add_collection(sticks)
if show_element:
for i, atom in enumerate(atoms):
ax.text(atom.x,
atom.y,
atom.z,
ir.atom_name[i] + str(i),
color="black",
ha="center",
va="center",
zorder=100,
fontsize=16)
ax.scatter(*atoms.positions.T, c=colors, s=ball_sizes**2, alpha=1.0)
ax.set_facecolor((0.05, 0.05, 0.05))
ax.get_figure().set_facecolor((0.05, 0.05, 0.05))
# Plots the rays
for i in range(n_ray_steps):
ax.scatter(*atoms.positions.T,
s=ray_sizes[i]**2,
c=colors,
linewidth=0,
alpha=0.05)
# ymin_ =min( ymin,yl+0.1*yr)
# ax.text(xmin,ymin_,zmin_,r'$BO^{t=%d}$' %t,fontsize=16)
if Box:
# plot lattice
cell = atoms.get_cell()
crystalVetexes = [np.zeros(3), cell[0], cell[1], cell[2]]
crystalVetexes.append(cell[0] + cell[1])
crystalVetexes.append(cell[0] + cell[2])
crystalVetexes.append(cell[1] + cell[2])
crystalVetexes.append(crystalVetexes[4] + cell[2])
edges = [[0, 1], [0, 2], [0, 3], [1, 4], [1, 5], [2, 6], [2, 4],
[3, 5], [3, 6], [4, 7], [5, 7], [6, 7]]
for e in edges:
i, j = e
x = [crystalVetexes[i][0], crystalVetexes[j][0]]
y = [crystalVetexes[i][1], crystalVetexes[j][1]]
z = [crystalVetexes[i][2], crystalVetexes[j][2]]
ax.plot(x, y, z, c=boxColor, linewidth=latticeWidth, alpha=0.9)
fig.tight_layout()
ax.azim = azim
ax.elev = elev
ax.axis("off") # Remove frame
plt.savefig(figname, transparent=True)
if show:
plt.show()
plt.close()
def deb_bo(images, i=0, j=1, figsize=(16, 10), print_=False, show=False):
r, bopsi, boppi, boppp, bo0, bo1, eb, esi = [], [], [], [], [], [], [], []
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', nn=True)
ir.calculate_Delta(images[0])
for i_, atoms in enumerate(images):
ir.calculate(atoms)
bopsi.append(ir.eterm1[i][j])
boppi.append(ir.eterm2[i][j])
boppp.append(ir.eterm3[i][j])
bo0.append(ir.bop[i][j])
bo1.append(ir.bo0[i][j])
eb.append(ir.ebond[i][j])
esi.append(ir.esi[i][j])
r.append(ir.r[i][j])
if print_:
print(
'{:3d} r: {:6.4f} bo: {:6.4f} Delta: {:6.4f} {:6.4f}'.format(
i_, ir.r[i][j], ir.bo0[i][j], ir.Delta[i],
ir.Delta[j])) # self.thet0-self.theta
emin_ = np.min(eb)
eb = (emin_ - np.array(eb)) / emin_
ems = np.min(esi)
emx = np.max(esi)
esi = (np.array(esi) - ems) / emx
plt.figure(figsize=figsize)
plt.plot(r,
bopsi,
alpha=0.8,
linewidth=2,
linestyle=':',
color='k',
label=r'$BO_p^{\sigma}$')
plt.plot(r,
boppi,
alpha=0.8,
linewidth=2,
linestyle='-.',
color='k',
label=r'$BO_p^{\pi}$')
plt.plot(r,
boppp,
alpha=0.8,
linewidth=2,
linestyle='--',
color='k',
label=r'$BO_p^{\pi\pi}$')
plt.plot(r,
bo0,
alpha=0.8,
linewidth=2,
linestyle='-',
color='g',
label=r'$BO^{t=0}$')
plt.plot(r,
bo1,
alpha=0.8,
linewidth=2,
linestyle='-',
color='y',
label=r'$BO^{t=1}$')
plt.plot(r,
eb,
alpha=0.8,
linewidth=2,
linestyle='-',
color='r',
label=r'$E_{bond}$ ($-E_{bond}/%4.2f$)' % -emin_)
plt.plot(r,
esi,
alpha=0.8,
linewidth=2,
linestyle='-',
color='indigo',
label=r'$E_{esi}$ ($E_{si}/%4.2f$)' % emx)
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('deb_bo.pdf')
if show: plt.show()
plt.close()
return r, eb
def deb_eover(images, i=0, j=1, figsize=(16, 10), show=False, print_=True):
bopsi, boppi, boppp, bo0, bo1, eb = [], [], [], [], [], []
eo, eu, el, esi, r = [], [], [], [], []
eo_, eu_, eb_ = [], [], []
ir = IRFF_NP(atoms=images[0], libfile='ffield.json', nn=True)
ir.calculate_Delta(images[0])
for i_, atoms in enumerate(images):
ir.calculate(atoms)
bo0.append(ir.bop[i][j])
bo1.append(ir.bo0[i][j])
eb.append(ir.ebond[i][j])
eo.append(ir.Eover)
eu.append(ir.Eunder)
r.append(ir.r[i][j])
if print_:
print('r: {:6.4f} bo: {:6.4f} eu: {:6.4f} ev: {:6.4f} eb: {:6.4f}'.
format(ir.r[i][j], ir.bo0[i][j], ir.Eunder, ir.Eover,
ir.ebond[i][j]))
ebx = np.max(np.abs(eb))
eb = np.array(eb) / ebx + 1.0
eox = np.max(np.abs(eo))
if eox < 0.0001:
eox = 1.0
eo = np.array(eo) / eox + 1.0
eux = np.max(np.abs(eu))
eu = np.array(eu) / eux + 1.0
plt.figure(figsize=figsize)
# plt.plot(r,bo0,alpha=0.8,linewidth=2,linestyle='-',color='g',label=r'$BO^{t=0}$')
# plt.plot(r,bo1,alpha=0.8,linewidth=2,linestyle='-',color='y',label=r'$BO^{t=1}$')
plt.plot(r,
eb,
alpha=0.8,
linewidth=2,
linestyle='-',
color='r',
label=r'$E_{bond}$')
plt.plot(r,
eo,
alpha=0.8,
linewidth=2,
linestyle='-',
color='indigo',
label=r'$E_{over}$ ($E_{over}/%4.2f$)' % eox)
plt.plot(r,
eu,
alpha=0.8,
linewidth=2,
linestyle='-',
color='b',
label=r'$E_{under}$ ($E_{under}/%4.2f$)' % eux)
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('deb_bo.pdf')
if show: plt.show()
plt.close()
def bde(i=2,j=3,rmin=0.4,ffield='ffield.json',nn='T',gen='poscar.gen'):
atoms = read(gen)
nn_=True if nn=='T' else False
ir = IRFF_NP(atoms=atoms,
libfile=ffield,
rcut=None,
nn=nn_,massages=1)
ir.calculate_Delta(atoms)
natom = ir.natom
positions = atoms.positions
v = positions[j] - positions[i]
r = np.sqrt(np.sum(np.square(v)))
u = v/r
# print(u)
r_,eb,bosi,bop_si,esi,bop,bop_pi,bop_pp,bo = [],[],[],[],[],[],[],[],[]
e,eba,eo = [],[],[]
esi,epi,epp = [],[],[]
for i_ in range(50):
r = rmin + i_*0.015
atoms.positions[j] = atoms.positions[i] + u*r
v = positions[j] - positions[i]
R = np.sqrt(np.sum(np.square(v)))
ir.calculate(atoms)
r_.append(r)
eb.append(ir.ebond[i][j])
eba.append(ir.ebond[i][j] + ir.eover[i] + ir.eover[j])
eo.append(ir.eover[i] + ir.eover[j])
bo.append(ir.bo0[i][j])
bosi.append(ir.bosi[i][j])
esi.append(-ir.sieng[i][j])
epi.append(-ir.pieng[i][j])
epp.append(-ir.ppeng[i][j])
bop.append(ir.bop[i][j])
bop_si.append(ir.bop_si[i][j])
bop_pi.append(ir.bop_pi[i][j])
bop_pp.append(ir.bop_pp[i][j])
e.append(ir.E)
plt.figure()
plt.subplot(2,2,1)
plt.plot(r_,e,label=r'$E_{tot}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(2,2,2)
#plt.ylabel( r'$\sigma$ Bond-Energy (eV)')
# plt.xlabel(r'$Radius$ $(Angstrom)$')
plt.plot(r_,esi,label=r'$E_{bond}^{\sigma}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(2,2,3)
plt.plot(r_,epi,label=r'$E_{bond}^{\pi}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(2,2,4)
plt.plot(r_,epp,label=r'$E_{bond}^{\pi\pi}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Ebond.eps')
plt.close()
def plf3d(atomi, atomj, traj, bo_=2.11):
images = Trajectory(traj)
atoms = images[0]
# positions = atoms.get_positions()
sym = atoms.get_chemical_symbols()
with open('ffield.json', 'r') as lf:
j = js.load(lf)
m = j['m']
ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=True)
Di_, Dj_, F_, BO_ = [], [], [], []
for _, atoms in enumerate(images):
ir.calculate(atoms)
Di_.append(ir.D[0][atomi] - ir.H[0][atomi][atomj])
Dj_.append(ir.D[0][atomj] - ir.H[0][atomi][atomj])
BO_.append(ir.H[0][atomi][atomj])
F_.append(ir.F[-1][atomi][atomj])
for _, d in enumerate(Di_):
print(Di_[_], Dj_[_], F_[_])
Di_ = Dj_ = np.arange(1.54, 1.62, 0.0001)
Di, Dj = np.meshgrid(Di_, Dj_)
i_, j_ = Di.shape
F = np.zeros([i_, i_])
# di_= np.expand_dims(ir.D[0],axis=0)-ir.H[0]
# dj_= np.expand_dims(ir.D[0],axis=1)-ir.H[0]
# bo_= ir.H[0]
wi = np.array(m['f1wi_C-C'])
bi = np.array(m['f1bi_C-C'])
w = np.array(m['f1w_C-C'])
b = np.array(m['f1b_C-C'])
wo = np.array(m['f1wo_C-C'])
bo = np.array(m['f1bo_C-C'])
for i in range(i_):
for j in range(i, i_):
di_ = Di[i][j]
dj_ = Dj[i][j]
Fi = f_nn([dj_, di_, bo_],
wi,
bi,
w,
b,
wo,
bo,
layer=ir.bo_layer[1])
F_ = 2.0 * Fi * Fi
# print(F_)
F[i][j] = F_
F[j][i] = F_
fig = plt.figure()
ax = Axes3D(fig)
# plt.xlabel("Delta'")
ax = plt.subplot(111, projection='3d')
# ax.plot_surface(Di,Dj,F, cmap=plt.get_cmap('rainbow'))
ax.contourf(Di, Dj, F, zdir='z', cmap=plt.get_cmap('rainbow'))
plt.savefig('F.svg')
plt.close()
class AtomDance(object):
def __init__(self,
atoms=None,
poscar=None,
nn=True,
rtole=0.5,
bondTole=1.25,
botol=0.0):
self.rtole = rtole
self.bondTole = bondTole
self.botol = botol
self.BondDistrubed = []
if atoms is None:
if poscar is None:
atoms = read('poscar.gen')
else:
atoms = read(poscar)
self.ir = IRFF_NP(atoms=atoms, libfile='ffield.json', rcut=None, nn=nn)
self.natom = self.ir.natom
self.atom_name = self.ir.atom_name
spec = self.ir.spec
self.mass = atoms.get_masses()
label_dic = {}
for sp in self.atom_name:
if sp in label_dic:
label_dic[sp] += 1
else:
label_dic[sp] = 1
self.label = ''
for sp in spec:
self.label += sp + str(label_dic[sp])
self.ir.calculate_Delta(atoms)
self.InitBonds = getBonds(self.natom,
self.ir.r,
self.bondTole * self.ir.re,
self.ir.bo0,
botol=self.botol)
self.neighbors = getNeighbor(self.natom,
self.ir.r,
self.bondTole * self.ir.re,
self.ir.bo0,
botol=self.botol)
def bond_momenta_bigest(self, atoms):
ratio = []
s = []
for bd in self.InitBonds:
i, j = bd
ratio_ = self.ir.r[i][j] / self.ir.re[i][j]
s_ = ratio_ - 1.0
s.append(s_)
ratio.append(abs(s_))
m_ = np.argmax(ratio)
i, j = self.InitBonds[m_]
s_ = s[m_]
if s_ >= 0.0:
sign = 1.0
else:
sign = -1.0
atoms = self.set_bond_momenta(i, j, atoms, sign=sign)
return atoms
def bond_momenta(self, atoms):
ratio = []
for bd in self.InitBonds:
i, j = bd
if bd not in self.BondDistrubed:
s_ = self.ir.r[i][j] / self.ir.re[i][j] - 1.0
if s_ >= 0.0:
sign = 1.0
else:
sign = -1.0
self.BondDistrubed.append(bd)
atoms = self.set_bond_momenta(i, j, atoms, sign=sign)
return atoms, True
return atoms, False
def set_bond_momenta(self, i, j, atoms, sign=1.0):
ha = int(0.5 * self.natom)
# x = atoms.get_positions()
v = np.zeros([self.natom, 3])
group_j = []
group_j, ring = getAtomsToMove(i, j, j, group_j, self.neighbors)
jg = len(group_j)
group_i = []
group_i, ring = getAtomsToMove(j, i, i, group_i, self.neighbors)
ig = len(group_i)
if ring:
group_i = [i]
group_j = [j]
vij = self.ir.vr[j][i] / self.ir.r[i][j]
massi = 0.0
massj = 0.0
for a in group_i:
massi += self.mass[a]
for a in group_j:
massj += self.mass[a]
vi = 1.0 / massi
vj = 1.0 / massj
for a in group_i:
v[a] = sign * vi * vij
for a in group_j:
v[a] = -sign * vj * vij
atoms.set_velocities(v)
return atoms
def check_bond(self, atoms=None, mdtraj=None, bondTole=1.3):
if atoms is None:
atoms = self.ir.atoms
if not bondTole is None:
self.bondTole = bondTole
self.ir.calculate_Delta(atoms, updateP=True)
bkbd = None
bB_ = 0
bondBroken = False
bondTole_ = self.bondTole - 0.015
bonds = getBonds(self.natom,
self.ir.r,
bondTole_ * self.ir.re,
self.ir.bo0,
botol=self.botol * 0.5)
if len(bonds) == len(self.InitBonds):
for bd in self.InitBonds:
bd_ = (bd[1], bd[0])
if (bd not in bonds) and (bd_ not in bonds):
bkbd = bd
bondBroken = True
break
else:
bondBroken = True
if bondBroken:
bB_ += 1
bondBroken = False
bondTole_ = self.bondTole
bonds = getBonds(self.natom,
self.ir.r,
bondTole_ * self.ir.re,
self.ir.bo0,
botol=self.botol)
if len(bonds) == len(self.InitBonds):
for bd in self.InitBonds:
bd_ = (bd[1], bd[0])
if (bd not in bonds) and (bd_ not in bonds):
bondBroken = True
break
else:
bondBroken = True
if bondBroken:
bB_ += 1
return bB_, bkbd
def check(self, wcheck=2, i=0, atoms=None, rtole=None):
if atoms is None:
atoms = self.ir.atoms
if not rtole is None:
self.rtole = rtole
self.ir.calculate_Delta(atoms, updateP=True)
fc = open('check.log', 'w')
if i % wcheck == 0:
atoms = self.checkLoneAtoms(atoms, fc)
else:
atoms = self.checkLoneAtom(atoms, fc)
atoms = self.checkClosedAtom(atoms, fc)
fc.close()
return atoms
def checkLoneAtom(self, atoms, fc):
for i in range(self.natom):
if self.ir.Delta[i] <= self.ir.atol:
print('- find an lone atom', i, self.atom_name[i], file=fc)
sid = np.argsort(self.ir.r[i])
for j in sid:
if self.ir.r[i][j] > 0.0001:
print(' move lone atom to nearest neighbor: %d' % j,
file=fc)
vr = self.ir.vr[i][j]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
j] + u * 0.64 * self.ir.r_cuta[i][j]
break
self.ir.calculate_Delta(atoms)
return atoms
def checkLoneAtoms(self, atoms, fc):
for i in range(self.natom):
if self.ir.Delta[i] <= self.ir.atol:
print('- find an lone atom', i, self.atom_name[i], file=fc)
mid = np.argmin(self.ir.ND)
if mid == i:
continue
print('- find the most atractive atom:', mid, file=fc)
print('\n- neighors of atom %d %s:' % (i, self.atom_name[i]),
end='',
file=fc)
neighs = []
for j, bo in enumerate(self.ir.bo0[mid]):
if bo > self.ir.botol:
neighs.append(j)
print(j, self.atom_name[j], end='', file=fc)
print(' ', file=fc)
if len(neighs) == 0:
vr = self.ir.vr[mid][i]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 1:
j = neighs[0]
vr = self.ir.vr[mid][j]
u = vr / np.sqrt(np.sum(np.square(vr)))
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 2:
i_, j_ = neighs
xj = atoms.positions[mid]
xi = 0.5 * (atoms.positions[i_] + atoms.positions[j_])
vr = xj - xi
u = vr / np.sqrt(np.sum(np.square(vr)))
vij = atoms.positions[j_] - atoms.positions[i_]
rij = np.sqrt(np.sum(np.square(vij)))
r_ = np.dot(vij, u)
if r_ != rij:
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
elif len(neighs) == 3:
i_, j_, k_ = neighs
vi = atoms.positions[i_] - atoms.positions[j_]
vj = atoms.positions[i_] - atoms.positions[k_]
# cross product
vr = np.cross(vi, vj)
c = (atoms.positions[i_] + atoms.positions[j_] +
atoms.positions[k_]) / 3
v = atoms.positions[mid] - c
u = vr / np.sqrt(np.sum(np.square(vr)))
# dot product
dot = np.dot(v, u)
if dot <= 0:
u = -u
atoms.positions[i] = atoms.positions[
mid] + u * 0.64 * self.ir.r_cuta[i][mid]
self.ir.calculate_Delta(atoms)
return atoms
def checkClosedAtom(self, atoms, fc):
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, self.ir.r_cuta,
self.ir.bo0)
for i in range(self.natom - 1):
for j in range(i + 1, self.natom):
if self.ir.r[i][j] < self.rtole * self.ir.r_cuta[i][j]:
print('- atoms %d and %d too closed' % (i, j), file=fc)
moveDirection = self.ir.vr[j][i] / self.ir.r[i][j]
moveD = self.ir.r_cuta[i][j] * (self.rtole +
0.01) - self.ir.r[i][j]
moveV = moveD * moveDirection
ToBeMove = []
ToBeMove, ring = getAtomsToMove(i, j, j, ToBeMove,
neighbors)
print(' atoms to to be moved:', ToBeMove, file=fc)
for m in ToBeMove:
newPos = atoms.positions[m] + moveV
r = np.sqrt(
np.sum(np.square(newPos - atoms.positions[i])))
if r > self.ir.r[i][m]:
atoms.positions[m] = newPos
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r,
self.ir.r_cuta, self.ir.bo0)
return atoms
def bend(self, ang=None, rang=20.0, nbin=10, scale=1.2, wtraj=False):
i, j, k = ang
axis = [i, k]
images = self.rotate(atms=[i, k],
axis=axis,
o=j,
rang=rang,
nbin=nbin,
wtraj=wtraj,
scale=scale)
return images
def bend_axis(self,
axis=None,
group=None,
rang=20,
nbin=30,
scale=1.2,
wtraj=False):
images = self.rotate(atms=group,
axis=axis,
o=axis[0],
rang=rang,
nbin=nbin,
wtraj=wtraj,
scale=scale)
return images
def swing_group(self,
ang=None,
group=None,
rang=20,
nbin=30,
scale=1.2,
wtraj=False):
i, j, k = ang
atoms = self.ir.atoms
self.ir.calculate_Delta(atoms)
vij = atoms.positions[i] - atoms.positions[j]
vjk = atoms.positions[k] - atoms.positions[j]
r = self.ir.r[j][k]
ujk = vjk / r
ui = vij / self.ir.r[i][j]
uk = np.cross(ui, ujk)
rk = np.sqrt(np.sum(uk * uk))
if rk < 0.0000001:
uk = np.array([1.0, 0.0, 0.0])
else:
uk = uk / rk
images = self.rotate(atms=group,
axis_vector=uk,
o=j,
rang=rang,
nbin=nbin,
wtraj=wtraj,
scale=scale)
return images
def rotate(self,
atms=None,
axis=None,
axis_vector=None,
o=None,
rang=20.0,
nbin=10,
wtraj=False,
scale=1.2):
da = 2.0 * rang / nbin
atoms = self.ir.atoms
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, scale * self.ir.re,
self.ir.bo0)
images = []
if wtraj: his = TrajectoryWriter('rotate.traj', mode='a')
if axis_vector is None:
i, j = axis
vaxis = atoms.positions[j] - atoms.positions[i]
uk = vaxis / self.ir.r[i][j]
else:
uk = axis_vector
a_ = -rang
while a_ < rang:
atoms_ = atoms.copy()
for atomk in atms:
vo = atoms.positions[atomk] - atoms.positions[o]
r_ = np.dot(vo, uk)
o_ = atoms.positions[o] + r_ * uk
vi = atoms.positions[atomk] - o_
r = np.sqrt(np.sum(np.square(vi)))
ui = vi / r
uj = np.cross(uk, ui)
a = a_ * 3.14159 / 180.0
p = r * np.cos(a) * ui + r * np.sin(a) * uj
atoms_.positions[atomk] = o_ + p
self.ir.calculate(atoms_)
calc = SinglePointCalculator(atoms_, energy=self.ir.E)
atoms_.set_calculator(calc)
images.append(atoms_)
if wtraj: his.write(atoms=atoms_)
a_ += da
return images
def swing(self, ang, st=60.0, ed=180.0, nbin=50, scale=1.2, wtraj=False):
da = (ed - st) / nbin
i, j, k = ang
atoms = self.ir.atoms
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, scale * self.ir.re,
self.ir.bo0)
images = []
if wtraj: his = TrajectoryWriter('swing.traj', mode='w')
vij = atoms.positions[i] - atoms.positions[j]
vjk = atoms.positions[k] - atoms.positions[j]
r = self.ir.r[j][k]
ujk = vjk / r
ui = vij / self.ir.r[i][j]
uk = np.cross(ui, ujk)
rk = np.sqrt(np.sum(uk * uk))
if rk < 0.0000001:
uk = np.array([1.0, 0.0, 0.0])
else:
uk = uk / rk
uj = np.cross(uk, ui)
a_ = st
while a_ < ed:
atoms_ = atoms.copy()
a = a_ * 3.14159 / 180.0
p = r * np.cos(a) * ui + r * np.sin(a) * uj
atoms_.positions[k] = atoms_.positions[j] + p
self.ir.calculate(atoms_)
calc = SinglePointCalculator(atoms_, energy=self.ir.E)
atoms_.set_calculator(calc)
images.append(atoms_)
if wtraj: his.write(atoms=atoms_)
a_ += da
if wtraj: his.close()
return images
def stretch(self,
pair,
atoms=None,
nbin=20,
st=0.7,
ed=1.3,
scale=1.25,
traj=None,
ToBeMoved=None):
if atoms is None:
atoms = self.ir.atoms
self.ir.calculate_Delta(atoms)
neighbors = getNeighbor(self.natom, self.ir.r, scale * self.ir.re,
self.ir.bo0)
images = []
if not traj is None: his = TrajectoryWriter(traj, mode='w')
#for pair in pairs:
i, j = pair
if ToBeMoved is None:
ToBeMove = []
ToBeMove, ring = getAtomsToMove(i, j, j, ToBeMove, neighbors)
else:
ToBeMove = ToBeMoved
bin_ = (self.ir.re[i][j] * ed - self.ir.re[i][j] * st) / nbin
moveDirection = self.ir.vr[j][i] / self.ir.r[i][j]
if ring:
return None
for n in range(nbin):
atoms_ = atoms.copy()
moveV = atoms.positions[i] + moveDirection * (
self.ir.re[i][j] * st + bin_ * n) - atoms.positions[j]
# print(self.ir.re[i][j]*self.rtole+bin_*n)
for m in ToBeMove:
# sPos = atoms.positions[i] + self.ir.re[i][m]*self.rtole*moveDirection
newPos = atoms.positions[m] + moveV
r = np.sqrt(np.sum(np.square(newPos - atoms.positions[i])))
atoms_.positions[m] = newPos
self.ir.calculate(atoms_)
i_ = np.where(
np.logical_and(
self.ir.r < self.ir.re[i][j] * self.rtole - bin_,
self.ir.r > 0.0001))
n = len(i_[0])
try:
assert n == 0, 'Atoms too closed!'
except:
print('Atoms too closed.')
break
calc = SinglePointCalculator(atoms_, energy=self.ir.E)
atoms_.set_calculator(calc)
images.append(atoms_)
if not traj is None: his.write(atoms=atoms_)
if not traj is None: his.close()
return images
def close(self):
self.ir = None
self.atom_name = None
def over(i=2,j=3,rmin=0.4,ffield='ffield.json',nn='T',gen='poscar.gen'):
atoms = read(gen)
nn_=True if nn=='T' else False
ir = IRFF_NP(atoms=atoms,
libfile=ffield,
rcut=None,
nn=nn_,massages=1)
ir.calculate_Delta(atoms)
natom = ir.natom
positions = atoms.positions
v = positions[j] - positions[i]
r = np.sqrt(np.sum(np.square(v)))
u = v/r
# print(u)
r_,eb,bosi,bop_si,bop,bop_pi,bop_pp,bo = [],[],[],[],[],[],[],[]
eba,eo,dlpi,dlpj,ev,boe = [],[],[],[],[],[]
esi,epi,epp = [],[],[]
for i_ in range(50):
r = rmin + i_*0.015
atoms.positions[j] = atoms.positions[i] + u*r
v = positions[j] - positions[i]
R = np.sqrt(np.sum(np.square(v)))
ir.calculate(atoms)
r_.append(r)
eb.append(ir.ebond[i][j])
eba.append(ir.ebond[i][j] + ir.eover[i] + ir.Evdw)
ev.append(ir.Evdw)
eo.append(ir.eover[i] + ir.eover[j])
bo.append(ir.bo0[i][j])
bosi.append(ir.bosi[i][j])
esi.append(-ir.sieng[i][j])
boe.append(ir.esi[i][j])
bop.append(ir.bop[i][j])
bop_si.append(ir.bop_si[i][j])
bop_pi.append(ir.bop_pi[i][j])
bop_pp.append(ir.bop_pp[i][j])
dlpi.append(ir.Delta_lpcorr[i])
dlpj.append(ir.Delta_lpcorr[j])
plt.figure()
plt.subplot(3,2,1)
#plt.ylabel( r'$\sigma$ Bond-Energy (eV)')
# plt.xlabel(r'$Radius$ $(Angstrom)$')
plt.plot(r_,eb,label=r'$E_{bond}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,2)
plt.plot(r_,eba,label=r'$E_{bond}$ + $E_{vdw}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,3)
plt.plot(r_,bo,label=r'$E_{over}$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,4)
plt.plot(r_,bosi,label=r'$BO_{\sigma}$ t=1', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,5)
plt.plot(r_,boe,label=r'$BO_{\sigma}^e$', color='r', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,6)
plt.plot(r_,dlpi,label=r'$\Delta^{i}_{lpcorr}$(%s)' %ir.atom_name[i],
color='r', linewidth=2, linestyle='-')
plt.plot(r_,dlpj,label=r'$\Delta^{j}_{lpcorr}$(%s)' %ir.atom_name[j],
color='b', linewidth=2, linestyle='-')
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Eover.eps')
plt.close()