def dr(traj='poscar.gen', batch_size=1, nn=False):
ffield = 'ffield.json' if nn else 'ffield'
atoms = read(traj)
e, ei = [], []
ir = IRFF(atoms=atoms, libfile=ffield, nn=False, bo_layer=[8, 4])
ir.get_potential_energy(atoms)
def dh(traj='siesta.traj', batch_size=1, nn=True, frame=7):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
his = TrajectoryWriter('tmp.traj', mode='w')
his.write(atoms=atoms)
his.close()
from irff.irff import IRFF
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn, bo_layer=[9, 2])
ir.get_potential_energy(atoms)
from irff.reax import ReaxFF
rn = ReaxFF(libfile=ffield,
direcs={'tmp': 'tmp.traj'},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
pkl=False,
to_train=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
mol = 'tmp'
hblab = rn.lk.hblab
hbs = []
for hb in ir.hbs:
hbs.append(list(hb))
eh_ = rn.get_value(rn.EHB)
# eh = ir.ehb.numpy()
rhb_ = rn.get_value(rn.rhb)
# rhb = ir.rhb.numpy()
frhb_ = rn.get_value(rn.frhb)
# frhb = ir.frhb.numpy()
sin4_ = rn.get_value(rn.sin4)
# sin4 = ir.sin4.numpy()
# exphb1_= rn.get_value(rn.exphb1)
# exphb2_= rn.get_value(rn.exphb2)
# exphb1 = ir.exphb1.numpy()
# exphb2 = ir.exphb2.numpy()
# hbsum_ = rn.get_value(rn.hbsum)
# hbsum = ir.hbsum.numpy()
for hb in rn.hbs:
for a_ in range(rn.nhb[hb]):
a = hblab[hb][a_][1:]
i, j, k = a
if a in hbs:
ai = hbs.index(a)
# if eh_[hb][a_][0]<-0.000001:
print(
'- %2d%s-%2d%s-%2d%s:' %
(i, ir.atom_name[i], j, ir.atom_name[j], k,
ir.atom_name[k]), 'ehb: %10.8f' % (eh_[hb][a_][0]),
'rhb: %10.8f' % (rhb_[hb][a_][0]),
'frhb: %10.8f' % (frhb_[hb][a_][0]),
'sin4: %10.8f' % (sin4_[hb][a_][0]))
def da(traj='siesta.traj', batch_size=1, nn=False, frame=0):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
his = TrajectoryWriter('tmp.traj', mode='w')
his.write(atoms=atoms)
his.close()
ir = IRFF(atoms=atoms, libfile=ffield, nn=False, bo_layer=[8, 4])
ir.get_potential_energy(atoms)
rn = ReaxFF(libfile=ffield,
direcs={'tmp': 'tmp.traj'},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
pkl=False,
to_train=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
mol = 'tmp'
anglab = rn.lk.anglab
angs = []
for ang in ir.angs:
angs.append(list(ang))
ea_ = rn.get_value(rn.EANG)
ea = ir.eang.numpy()
f7_ = rn.get_value(rn.f_7)
f7 = ir.f_7.numpy()
f8_ = rn.get_value(rn.f_8)
f8 = ir.f_8.numpy()
expang_ = rn.get_value(rn.expang)
expang = ir.expang.numpy()
expaij_ = rn.get_value(rn.expaij)
expaij = ir.expaij.numpy()
expajk_ = rn.get_value(rn.expajk)
expajk = ir.expajk.numpy()
theta_ = rn.get_value(rn.theta)
theta = ir.theta.numpy()
theta0_ = rn.get_value(rn.theta0)
theta0 = ir.thet0.numpy()
sbo3_ = rn.get_value(rn.SBO3)
sbo3 = ir.SBO3.numpy()
fa = open('ang.txt', 'w')
for ang in rn.angs:
for a_ in range(rn.nang[ang]):
a = anglab[ang][a_][1:]
if a not in angs:
a.reverse()
i, j, k = a
if a in angs:
ai = angs.index(a)
print('- %2d%s-%2d%s-%2d%s:' %
(i, ir.atom_name[i], j, ir.atom_name[j], k,
ir.atom_name[k]),
'eang: %10.8f %10.8f' % (ea_[ang][a_][0], ea[ai]),
'f7: %10.8f %10.8f' % (f7_[ang][a_][0], f7[ai]),
'f8: %10.8f %10.8f' % (f8_[ang][a_][0], f8[ai]),
'expang: %10.8f %10.8f' %
(expang_[ang][a_][0], expang[ai]),
'expaij: %10.8f %10.8f' %
(expaij_[ang][a_][0], expaij[ai]),
'expajk: %10.8f %10.8f' %
(expajk_[ang][a_][0], expajk[ai]),
'theta: %10.8f %10.8f' %
(theta_[ang][a_][0], theta[ai]),
'sbo3: %10.8f %10.8f' % (sbo3_[ang][a_][0], sbo3[ai]),
'theta0: %10.8f %10.8f' %
(theta0_[ang][a_][0], theta0[ai]),
file=fa)
else:
print(
'- %2d%s-%2d%s-%2d%s:' %
(i, ir.atom_name[i], j, ir.atom_name[j], k,
ir.atom_name[k]), 'eang: %10.8f' % (ea_[ang][a_][0]),
'f7: %10.8f' % (f7_[ang][a_][0]),
'f8: %10.8f' % (f8_[ang][a_][0]),
'expang: %10.8f' % (expang_[ang][a_][0]),
'expang: %10.8f' % (expang_[ang][a_][0]),
'expaij: %10.8f' % (expaij_[ang][a_][0]),
'expajk: %10.8f' % (expajk_[ang][a_][0]), 'theta: %10.8f' %
(theta_[ang][a_][0]), 'sbo3: %10.8f' % (sbo3_[ang][a_][0]),
'theta0: %10.8f' % (theta0_[ang][a_][0]))
fa.close()
print('\n- angel energy:',
ir.Eang.numpy(),
rn.eang[mol].numpy()[0],
end='\n')
def dt(traj='siesta.traj', batch_size=1, nn=True, frame=0):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
his = TrajectoryWriter('tmp.traj', mode='w')
his.write(atoms=atoms)
his.close()
from irff.irff import IRFF
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn)
ir.get_potential_energy(atoms)
eb = ir.Ebond.numpy()
et = ir.etor.numpy()
ef = ir.efcon.numpy()
f10 = ir.f_10.numpy()
f11 = ir.f_11.numpy()
sijk = ir.s_ijk.numpy()
sjkl = ir.s_jkl.numpy()
f = ir.fijkl.numpy()
v1 = ir.v1.numpy()
v2 = ir.v2.numpy()
v3 = ir.v3.numpy()
expv2 = ir.expv2.numpy()
boij = ir.botij.numpy()
bojk = ir.botjk.numpy()
bokl = ir.botkl.numpy()
cosw = ir.cos_w.numpy()
cos2w = ir.cos2w.numpy()
w = ir.w.numpy()
Etor = ir.Etor.numpy()
del IRFF
from irff.reax import ReaxFF
rn = ReaxFF(libfile=ffield,
direcs={'tmp': 'tmp.traj'},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
bo_layer=[9, 2],
pkl=False,
to_train=False)
molecules = rn.initialize()
# rn.calculate(rn.p,rn.m)
rn.session(learning_rate=3.0 - 4, method='AdamOptimizer')
mol = 'tmp'
torlab = rn.lk.torlab
tors = []
for tor in ir.tors:
tors.append(list(tor))
print(tor)
eb_ = rn.get_value(rn.ebond[mol])
et_ = rn.get_value(rn.ETOR)
ef_ = rn.get_value(rn.Efcon)
f10_ = rn.get_value(rn.f_10)
f11_ = rn.get_value(rn.f_11)
sijk_ = rn.get_value(rn.s_ijk)
sjkl_ = rn.get_value(rn.s_jkl)
f_ = rn.get_value(rn.fijkl)
boij_ = rn.get_value(rn.BOtij)
bojk_ = rn.get_value(rn.BOtjk)
bokl_ = rn.get_value(rn.BOtkl)
v1_ = rn.get_value(rn.v1)
v2_ = rn.get_value(rn.v2)
v3_ = rn.get_value(rn.v3)
expv2_ = rn.get_value(rn.expv2)
cosw_ = rn.get_value(rn.cos_w)
cos2w_ = rn.get_value(rn.cos2w)
w_ = rn.get_value(rn.w)
for tor in rn.tors:
for a_ in range(rn.ntor[tor]):
a = torlab[tor][a_][1:]
if a not in tors:
a.reverse()
i, j, k, l = a
if a in tors:
ai = tors.index(a)
# if abs(et_[tor][a_][0]-et[ai])>0.0001:
print(
'- %2d%s-%2d%s-%2d%s-%2d%s:' %
(i, ir.atom_name[i], j, ir.atom_name[j], k,
ir.atom_name[k], l, ir.atom_name[l]),
'etor: %10.8f %10.8f' % (et_[tor][a_][0], et[ai]),
'sijk: %10.8f %10.8f' % (sijk_[tor][a_][0], sijk[ai]),
'sjkl: %10.8f %10.8f' % (sjkl_[tor][a_][0], sjkl[ai]),
'boij: %10.8f %10.8f' % (boij_[tor][a_][0], boij[ai]),
'bojk: %10.8f %10.8f' % (bojk_[tor][a_][0], bojk[ai]),
'bokl: %10.8f %10.8f' % (bokl_[tor][a_][0], bokl[ai]),
'fijkl: %10.8f %10.8f' % (f_[tor][a_][0], f[ai]),
'v1: %10.8f %10.8f' % (v1_[tor][a_][0], v1[ai]),
'v2: %10.8f %10.8f' % (v2_[tor][a_][0], v2[ai]),
'v3: %10.8f %10.8f' % (v3_[tor][a_][0], v3[ai]),
'expv2: %10.8f %10.8f' % (expv2_[tor][a_][0], expv2[ai]),
'ptor1: %10.8f %10.8f' %
(rn.p_['tor1_' + tor], ir.P['tor1'][ai]),
# 'cosw: %10.8f %10.8f' %(cosw_[tor][a_][0],cosw[ai]),
# 'cos2w: %10.8f %10.8f' %(cos2w_[tor][a_][0],cos2w[ai]),
# 'v1: %10.8f %10.8f' %(0.5*rn.p_['V1_'+tor]*(1.0+cosw_[tor][a_][0]),
# 0.5*ir.P['V1'][ai]*(1.0+cosw[ai])),
# 'w: %10.8f %10.8f' %(w_[tor][a_][0],w[ai]),
# 'efcon: %10.8f %10.8f' %(ef_[tor][a_][0],ef[ai]),
# 'f_10: %10.8f %10.8f' %(f10_[tor][a_][0],f10[ai]),
# 'f_11: %10.8f %10.8f' %(f11_[tor][a_][0],f11[ai]),
)
Etor_ = rn.get_value(rn.etor)
print('\n- torsion energy:', Etor, Etor_[mol][0], end='\n')
print('\n- Bond energy:', eb, eb_, end='\n')
del ReaxFF
def e(traj='siesta.traj', 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 = [], [], []
from irff.irff import IRFF
ir = IRFF(atoms=images[0], libfile=ffield, nn=nn)
for i, atoms in enumerate(images):
ir.get_potential_energy(atoms)
e.append(ir.E.numpy())
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())
from irff.mpnn import MPNN
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],
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 dl(traj='siesta.traj', batch_size=1, nn=False, frame=0):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
his = TrajectoryWriter('tmp.traj', mode='w')
his.write(atoms=atoms)
his.close()
ir = IRFF(atoms=atoms, libfile=ffield, nn=False, bo_layer=[8, 4])
ir.get_potential_energy(atoms)
el = ir.elone.numpy()
Dle = ir.Delta_e.numpy()
Dlp = ir.Delta_lp.numpy()
de = ir.DE.numpy()
nlp = ir.nlp.numpy()
elp = ir.explp.numpy()
eu = ir.eunder.numpy()
eu1 = ir.eu1.numpy()
eu2 = ir.eu2.numpy()
eu3 = ir.expeu3.numpy()
rn = ReaxFF(libfile=ffield,
direcs={'tmp': 'tmp.traj'},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
pkl=False,
to_train=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
mol = 'tmp'
el_ = rn.get_value(rn.EL)
Dle_ = rn.get_value(rn.Delta_e)
Dlp_ = rn.get_value(rn.Delta_lp)
de_ = rn.get_value(rn.DE)
nlp_ = rn.get_value(rn.nlp)
elp_ = rn.get_value(rn.explp)
eu_ = rn.get_value(rn.EUN)
eu1_ = rn.get_value(rn.eu1)
eu2_ = rn.get_value(rn.eu2)
eu3_ = rn.get_value(rn.expeu3)
alab = rn.lk.atlab
bosi = ir.bosi.numpy()
for i in range(ir.natom):
a = ir.atom_name[i]
al = [mol, i]
na = alab[a].index(al)
print(
'- %d %s:' % (i, ir.atom_name[i]),
'elone: %10.8f %10.8f' % (el_[a][na], el[i]),
# 'Delta_e: %10.8f %10.8f' %(Dle_[a][na],Dle[i]),
# 'Delta_lp: %10.8f %10.8f' %(Dlp_[a][na],Dlp[i]),
# 'nlp: %10.8f %10.8f' %(nlp_[a][na],nlp[i]),
'eunder: %10.8f %10.8f' % (eu_[a][na], eu[i]),
# 'eu1: %10.8f %10.8f' %(eu1_[a][na],eu1[i]),
'eu2: %10.8f %10.8f' % (eu2_[a][na], eu2[i]),
'eu3: %10.8f %10.8f' % (eu3_[a][na], eu3[i]))
print('\n- under energy:',
ir.Eunder.numpy(),
rn.eunder[mol].numpy()[0],
end='\n')
def db(gen='C3H7O1-0.traj', batch_size=50, nn=True, frame=7):
ffield = 'ffield.json' if nn else 'ffield'
# atoms = read(gen,index=1)
images = Trajectory(gen)
atoms = images[frame]
# his = TrajectoryWriter('tmp.traj',mode='w')
# calc = SinglePointCalculator(atoms,energy=0.0,free_energy=0.0)
# atoms.set_calculator(calc)
# his.write(atoms=atoms)
# his.close()
from irff.irff import IRFF
e, ei = [], []
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn)
ir.get_potential_energy(atoms)
ei.append(ir.Ebond)
ebond = ir.ebond.numpy()
from irff.mpnn import MPNN
rn = MPNN(libfile=ffield,
direcs={'tmp': gen},
dft='siesta',
opt=[],
optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
bo_layer=[9, 2],
pkl=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
mol = 'tmp'
er = rn.get_value(rn.ebond[mol])
blab = rn.lk.bdlab
bosi = ir.bosi.numpy()
bopsi = ir.bop_si.numpy()
boppi = ir.bop_pi.numpy()
boppp = ir.bop_pp.numpy()
bopow3 = ir.bopow3.numpy()
eterm3 = ir.eterm3.numpy()
bop = ir.bop.numpy()
bo = ir.bo0.numpy()
eterm1 = ir.eterm1.numpy()
bopow1 = ir.bopow1.numpy()
bodiv1 = ir.bodiv1.numpy()
r = ir.r.numpy()
# F = ir.F.numpy()
# Fi = ir.Fi.numpy()
# Fj = ir.Fj.numpy()
D_ = rn.get_value(rn.Deltap)
D = ir.Deltap.numpy()
for bd in rn.bonds:
for nb in range(rn.nbd[bd]):
ebd = rn.get_value(rn.EBD[bd])
mol_, i, j = blab[bd][nb]
bosi_ = rn.get_value(rn.bosi[bd])
bopsi_ = rn.get_value(rn.bop_si[bd])
boppi_ = rn.get_value(rn.bop_pi[bd])
boppp_ = rn.get_value(rn.bop_pp[bd])
bopow3_ = rn.get_value(rn.bopow3[bd])
eterm3_ = rn.get_value(rn.eterm3[bd])
bop_ = rn.get_value(rn.bop[bd])
bo_ = rn.get_value(rn.bo0[bd])
eterm1_ = rn.get_value(rn.eterm1[bd])
bopow1_ = rn.get_value(rn.bopow1[bd])
bodiv1_ = rn.get_value(rn.bodiv1[bd])
rbd = rn.get_value(rn.rbd[bd])
# if abs(bo_[nb][0]-bo[i][j])>0.00001:
print(
'- %s %2d %2d:' % (bd, i, j),
'rbd %10.7f %10.7f' % (rbd[nb][frame], r[i][j]),
'bop %10.7f %10.7f' % (bop_[nb][frame], bop[i][j]),
'Di %10.7f %10.7f' % (D_[i][frame], D[i]),
'Dj %10.7f %10.7f' % (D_[j][frame], D[j]),
'bo %10.7f %10.7f' % (bo_[nb][frame], bo[i][j]),
# 'F %10.7f %10.7f' %(F_[nb][0],F[i][j]),
# 'Fi %10.7f %10.7f' %(Fi_[nb][0],Fi[i][j]),
# 'Fj %10.7f %10.7f' %(Fj_[nb][0],Fj[i][j]),
'ebond %10.7f %10.7f' % (ebd[nb][frame], ebond[i][j]))
rcbo = rn.get_value(rn.rc_bo)
eb = rn.get_value(rn.ebond[mol])
# print(rcbo)
print('\n- bond energy:', ir.Ebond.numpy(), eb[frame], end='\n')
def e(traj='eos_dft.traj', batch_size=100):
images = Trajectory(traj)
e, ei, ei_, diff = [], [], [], []
ir_mpnn = IRFF(atoms=images[0], libfile='ffield.json', nn=True)
ir_mpnn.get_potential_energy(images[0])
ir_reax = IRFF(atoms=images[0], libfile='ffield', nn=False)
ir_reax.get_potential_energy(images[0])
v = []
for i, atoms in enumerate(images):
e.append(atoms.get_potential_energy())
ei.append(ir_mpnn.get_potential_energy(atoms))
ei_.append(ir_reax.get_potential_energy(atoms))
diff.append(abs(e[-1] - ei[-1]))
print(' * energy: ', e[-1], ei[-1], ei_[-1], diff[-1])
v.append(atoms.get_volume())
# stress = atoms.get_stress()
# print(stress)
print(' * mean difference: ', np.mean(diff))
e_min = min(e)
e_max = max(e)
e = np.array(e) - e_min
e_min = min(ei)
ei = np.array(ei) - e_min
e_min = min(ei_)
ei_ = np.array(ei_) - e_min
plt.figure()
plt.ylabel(r'$Energy$ ($eV$)')
plt.xlabel(r'$Volume$ ($\AA^3$)')
# plt.xlim(0,i)
# plt.ylim(0,np.max(hist)+0.01)
plt.plot(v,
ei,
alpha=0.9,
linestyle='-',
marker='o',
markerfacecolor='k',
markersize=5,
color='k',
label='IRFF(MPNN)')
# plt.plot(v,ei,alpha=0.9,
# linestyle='-',marker='^',markerfacecolor='none',
# markeredgewidth=1,markeredgecolor='blue',markersize=5,
# color='blue',label='IRFF')
err = np.abs(ei - e)
err_ = np.abs(ei_ - e)
plt.errorbar(v,
e,
yerr=err,
fmt='s',
ecolor='r',
color='r',
ms=6,
markerfacecolor='none',
mec='r',
elinewidth=2,
capsize=2,
label='DFT (SIESTA)')
plt.plot(v,
ei_,
alpha=0.9,
linestyle='-',
marker='^',
markerfacecolor='b',
markersize=5,
color='b',
label='ReaxFF(trained)')
plt.fill_between(v, e - err, e + err, color='darkorange', alpha=0.2)
plt.fill_between(
v,
e - err_,
e + err_,
color='palegreen', # palegreen
alpha=0.2)
plt.text(0.0, e_max, '%.3f' % e_min, fontdict={'size': 10.5, 'color': 'k'})
plt.legend(loc='best', edgecolor='yellowgreen') # lower left upper right
plt.savefig('Energy.svg', transparent=True)
plt.close()
def e(traj='pre_dft.traj',batch_size=100,nn=True):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
e,ei,ei_ = [],[],[]
ir = IRFF(atoms=images[0],
libfile=ffield,
nn=True)
ir.get_potential_energy(images[0])
ir_reax = IRFF(atoms=images[0],
libfile='ffield',
nn=False)
ir_reax.get_potential_energy(images[0])
v_,pdft,pml,preax,diff = [],[],[],[],[]
v0 = images[0].get_volume()
for i,atoms in enumerate(images):
v=atoms.get_volume()
e.append(atoms.get_potential_energy())
stress_ = atoms.get_stress()
if v/v0 < 0.86:
pdft.append(calculate_pressure(stress_))
ir.calculate(atoms=atoms,CalStress=True)
ei.append(ir.E)
stress = ir.results['stress']
pml.append(calculate_pressure(stress))
ir_reax.calculate(atoms=atoms,CalStress=True)
ei_.append(ir.E)
stress = ir_reax.results['stress']
preax.append(calculate_pressure(stress))
v_.append(v/v0)
diff.append(abs(pml[-1]-pdft[-1]))
print(' * V/V0',v_[-1],v,pml[-1],pdft[-1],' diff: ',diff[-1])
print(' * mean error:',np.mean(diff))
e_min = min(e)
e_max = max(e)
e = np.array(e) - e_min
e_min = min(ei)
ei = np.array(ei) - e_min
plt.figure()
plt.ylabel(r'$Pressure$ ($GPa$)')
plt.xlabel(r'$V/V_0$')
# plt.xlim(0,i)
# plt.ylim(0,np.max(hist)+0.01)
ax = plt.gca()
pml = np.array(pml)
pdft = np.array(pdft)
plt.plot(v_,pml,alpha=0.9,
linestyle='-',marker='o',markerfacecolor='none',markersize=7,
color='b',label='IRFF(MPNN)')
plt.plot(v_,preax,alpha=0.9,
linestyle='-',marker='^',markerfacecolor='none',markersize=7,
color='g',label='ReaxFF(trained)')
plt.plot(v_,pdft,alpha=0.9,
linestyle='-',marker='s',markerfacecolor='none',markersize=7,
color='r',label='DFT(SIESTA)')
pdiff = np.abs(pdft - pml)
plt.fill_between(v_, pdft - pdiff, pdft + pdiff, color='darkorange',
alpha=0.2)
pdiff = np.abs(pdft - preax)
plt.fill_between(v_, pdft - pdiff, pdft + pdiff, color='palegreen',
alpha=0.2)
plt.legend(loc='best',edgecolor='yellowgreen') # lower left upper right
plt.savefig('compare-pv.svg',transparent=True)
plt.close()
def e(traj='siesta.traj',batch_size=100,nn=False):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
e,ei = [],[]
ir = IRFF(atoms=images[0],
libfile='ffield',
nn=False,
bo_layer=[8,4])
ir.get_potential_energy(images[0])
for i,atoms in enumerate(images):
e.append(atoms.get_potential_energy())
ei.append(ir.get_potential_energy(atoms))
e_max = max(e)
e = np.array(e) - e_max
rn = ReaxFF(libfile=ffield,
direcs={'tmp':'siesta.traj'},
dft='siesta',
opt=[],optword='nocoul',
batch_size=batch_size,
atomic=True,
clip_op=False,
InitCheck=False,
nn=nn,
pkl=False,
to_train=False)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10,method='AdamOptimizer')
mol = 'tmp'
er = rn.get_value(rn.E[mol])
dfte = rn.get_value(rn.dft_energy[mol])
zpe = rn.get_value(rn.zpe[mol])
er = er - e_max
ei = np.array(ei) - e_max
plt.figure()
plt.ylabel('Energy (eV)')
plt.xlabel('Step')
plt.xlim(0,i)
# plt.ylim(0,np.max(hist)+0.01)
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
# ax.spines['left'].set_position(('data',0))
# ax.spines['bottom'].set_position(('data', 0))
plt.plot(e,alpha=0.01,
linestyle='-',marker='s',markerfacecolor='none',
markeredgewidth=1,markeredgecolor='r',markersize=4,
color='red',label='DFT (SIESTA)')
plt.plot(er,alpha=0.01,
linestyle='-',marker='o',markerfacecolor='none',
markeredgewidth=1,markeredgecolor='b',markersize=4,
color='blue',label='ReaxFF')
plt.plot(ei,alpha=0.01,
linestyle='-',marker='^',markerfacecolor='none',
markeredgewidth=1,markeredgecolor='g',markersize=4,
color='blue',label='IRFF')
plt.text( 0.0, 0.5, '%.3f' %e_max, fontdict={'size':10.5, 'color': 'k'})
plt.legend(loc='upper left',edgecolor='yellowgreen') # lower left upper right
plt.savefig('Energy.eps',transparent=True)
plt.close()
def BDE(traj='c2h6-CC.traj', iatom=0, jatom=3):
images = Trajectory(traj)
e, ei, ei_, ebud, d_irff, eg, erdx, d_reax = [], [], [], [], [], [], [], []
ir_mpnn = IRFF(atoms=images[0], libfile='ffield.json', nn=True)
# ir_mat = IRFF(atoms=images[0],libfile='ffield.reax.mattsson',nn=False)
ir_reax = IRFF(atoms=images[0], libfile='ffield', nn=False)
r, v = [], []
for i, atoms in enumerate(images):
if i % 2 != 0:
continue
emp = ir_mpnn.get_potential_energy(atoms)
r_ = ir_mpnn.r[iatom][jatom].detach().numpy()
# if r_>1.7:
# continue
e.append(atoms.get_potential_energy())
ei.append(emp)
ei_.append(ir_reax.get_potential_energy(atoms))
eg.append(get_gulp_energy(atoms, lib='reax_mat'))
ebud.append(get_gulp_energy(atoms, lib='reax_bud'))
# erdx.append(get_gulp_energy(atoms,lib='reax_rdx'))
d_irff.append(abs(e[-1] - ei[-1]))
d_reax.append(abs(e[-1] - ei_[-1]))
v.append(atoms.get_volume())
r.append(r_)
print(' * energy: ', e[-1], ei[-1], ei_[-1], d_irff[-1], ' radius: ',
r[-1])
# stress = atoms.get_stress()
# print(stress)
print(' * mean difference: ', np.mean(d_irff), np.mean(d_reax))
e_min = min(e)
e_max = max(e)
e = np.array(e) - e_min
ei = np.array(ei) - min(ei)
ei_ = np.array(ei_) - min(ei_)
ebud = np.array(ebud) - min(ebud)
eg = np.array(eg) - min(eg)
# erdx = np.array(erdx) - min(erdx)
plt.figure()
plt.ylabel(r'$Energy$ ($eV$)')
plt.xlabel(r'$Radius$ ($\AA$)')
# plt.xlim(0,i)
# plt.ylim(0,np.max(hist)+0.01)
plt.plot(r,
ei,
alpha=0.9,
linestyle='-',
marker='o',
markerfacecolor='none',
markeredgecolor='k',
markersize=5,
color='k',
label='IRFF(MPNN)')
plt.plot(r,
e,
alpha=0.9,
linestyle='-',
marker='s',
markerfacecolor='none',
markeredgecolor='r',
markersize=5,
color='r',
label='DFT(SIESTA)')
plt.plot(r,
ei_,
alpha=0.9,
linestyle='-',
marker='^',
markerfacecolor='none',
markeredgecolor='b',
markersize=5,
color='b',
label='ReaxFF(trained)')
plt.plot(r,
ebud,
alpha=0.9,
linestyle='-',
marker='+',
markerfacecolor='none',
markeredgecolor='g',
markersize=5,
color='g',
label='ReaxFF(Thompson)')
plt.plot(r,
eg,
alpha=0.9,
linestyle='-',
marker='v',
markerfacecolor='none',
markeredgecolor='mediumslateblue',
markersize=5,
color='mediumslateblue',
label='ReaxFF(Mattsson)')
# plt.plot(r,eg,alpha=0.9,
# linestyle='-',marker='d',markerfacecolor='none',markeredgecolor='steelblue',
# markersize=5,
# color='steelblue',label='ReaxFF(RDX)')
err = np.abs(ei - e)
err_ = np.abs(ei_ - e)
plt.fill_between(r, e - err, e + err, color='darkorange', alpha=0.2)
plt.fill_between(
r,
e - err_,
e + err_,
color='palegreen', # palegreen
alpha=0.2)
plt.text(0.0,
e_max,
'%.3f ' % e_min,
fontdict={
'size': 10.5,
'color': 'k'
})
plt.legend(loc='best', edgecolor='yellowgreen') # lower left upper right
plt.savefig('Energy.svg', transparent=True)
plt.close()
