def debug_eo(direcs={'ch4': '/home/feng/siesta/train/ch4'},
gulp_cmd='/home/feng/gulp/gulp-5.0/Src/gulp<inp-gulp >gulp.out'):
for key in direcs:
mol = key
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
rc_scale='none',
clip_op=False,
optword='all',
batch_size=1,
sort=False,
pkl=True,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0 - 4, method='AdamOptimizer')
atlab = rn.lk.atlab
rbd = rn.get_value(rn.rbd)
eover = rn.get_value(rn.EOV)
D_lp = rn.get_value(rn.Delta_lpcorr)
otrm1 = rn.get_value(rn.otrm1)
otrm2 = rn.get_value(rn.otrm2)
p = rn.get_value(rn.p)
so = rn.get_value(rn.so)
cell = rn.cell[mol]
atom_name = molecules[mol].atom_name
A = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[0],
cell=cell,
pbc=(1, 1, 1))
write_gulp_in(A, runword='gradient nosymmetry conv qite verb')
system(gulp_cmd)
fg = open('gulp.out', 'r')
for line in fg.readlines():
if line.find('- eover:') >= 0:
l = line.split()
i = int(l[2]) - 1
an = atom_name[i]
ank = [mol, i]
find = False
if ank in atlab[an]:
na = atlab[an].index(ank)
find = True
if find:
# if abs(ebond[bn][nb][0]-float(l[4]))>0.001:
print('- ReaxFF %d %s:' % (na, an),
'eover: %10.6f' % eover[an][na][0],
'D_lp: %10.6f' % D_lp[an][na][0],
'otrm1: %10.6f' % otrm1[an][na][0],
'otrm2: %10.6f' % otrm2[an][na][0],
'Val: %10.6f' % p['val_' + an],
'SO: %10.6f' % so[an][na][0],
'ovun2: %10.6f' % p['ovun2_' + an])
print('- GULP %d %s:' % (na, an),
'eover: %10.6f' % float(l[3]),
'D_lp: %10.6f' % float(l[6]),
'otrm1: %10.6f' % float(l[5]),
'otrm2: %10.6f' % float(l[7]),
'Val: %10.6f' % float(l[8]), 'SO: %10.6f' % float(l[4]),
'ovun2: %10.6f' % float(l[9]))
else:
print('- GULP %d %s:' % (na, an),
'eover: %10.6f' % float(l[3]),
'D_lp: %10.6f' % float(l[6]),
'otrm1: %10.6f' % float(l[5]),
'otrm2: %10.6f' % float(l[7]),
'Val: %10.6f' % float(l[8]), 'SO: %10.6f' % float(l[4]),
'not found ... ... ... ')
fg.close()
def debug_v(direcs={'ch4': '/home/feng/siesta/train/ch4'},
gulp_cmd='/home/feng/gulp/Src/gulp<inp-gulp >gulp.out'):
for key in direcs:
mol = key
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
optword='nocoul',
batch_size=1,
sort=False,
pkl=True,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0 - 4, method='AdamOptimizer')
vlab = rn.lk.vlab
rv = rn.get_value(rn.rv)
expvdw1 = rn.get_value(rn.expvdw1)
expvdw2 = rn.get_value(rn.expvdw2)
evdw = rn.get_value(rn.EVDW)
f13 = rn.get_value(rn.f_13)
cell = rn.cell[mol]
A = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[0],
cell=cell,
pbc=(1, 1, 1))
write_gulp_in(A, runword='gradient nosymmetry conv qite verb')
system(gulp_cmd)
atom_name = molecules[mol].atom_name
fg = open('gulp.out', 'r')
for line in fg.readlines():
if line.find('- evdw:') >= 0:
l = line.split()
i = int(l[2]) - 1
j = int(l[3]) - 1
vb = atom_name[i] + '-' + atom_name[j]
vbk = [mol, i, j]
vbkr = [mol, j, i]
if not vb in rn.bonds:
vb = atom_name[j] + '-' + atom_name[i]
find = False
if vbk in vlab[vb]:
# nb = hblab[hb].index(hbk)
# print('------------------------------------')
nbs = []
for nb, bb in enumerate(vlab[vb]):
if bb == vbk:
nbs.append(nb)
find = True
elif vbkr in vlab[vb]:
# nb = hblab[hb].index(hbk)
# print('------------------------------------')
nbs = []
for nb, bb in enumerate(vlab[vb]):
if bb == vbkr:
nbs.append(nb)
find = True
if find:
ib = 0
for nb in nbs:
if abs(rv[vb][nb][0] - float(l[4])) < 0.00001:
# if abs(evdw[vb][nb][0]-float(l[5]))>0.0001:
print('- ReaxFF %d %s:' % (ib, vb),
'rv: %10.6f' % rv[vb][nb][0],
'evdw: %10.6f' % evdw[vb][nb][0])
print('- GULP %d %s:' % (ib, vb),
'rv: %10.6f' % float(l[4]),
'evdw: %10.6f' % float(l[5]))
ib += 1
else:
print('- N.F GULP %s:' % vb, 'rv: %10.6f' % float(l[4]),
'evdw: %10.6f' % float(l[5]))
fg.close()
def debug_bo(direcs={'ch4': '/home/feng/siesta/train/ch4'},
gulp_cmd='/home/feng/gulp/gulp-5.0/Src/gulp<inp-gulp >gulp.out'):
for key in direcs:
mol = key
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
optword='nocoul',
batch_size=1,
sort=False,
pkl=True,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0 - 4, method='AdamOptimizer')
bdlab = rn.lk.bdlab
bosi = rn.get_value(rn.bosi)
bo = rn.get_value(rn.bo)
bo0 = rn.get_value(rn.bo0)
bop = rn.get_value(rn.bop)
rbd = rn.get_value(rn.rbd)
bop_si = rn.get_value(rn.bop_si)
bop_pi = rn.get_value(rn.bop_pi)
bop_pp = rn.get_value(rn.bop_pp)
f = rn.get_value(rn.F)
f11 = rn.get_value(rn.F_11)
f12 = rn.get_value(rn.F_12)
f45 = rn.get_value(rn.F_45)
f4 = rn.get_value(rn.f_4)
f5 = rn.get_value(rn.f_5)
ebond = rn.get_value(rn.ebond)
cell = rn.cell[mol]
A = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[0],
cell=cell,
pbc=(1, 1, 1))
write_gulp_in(A, runword='gradient nosymmetry conv qite verb')
system(gulp_cmd)
atom_name = molecules[mol].atom_name
fg = open('gulp.out', 'r')
for line in fg.readlines():
if line.find('- bosi:') >= 0:
l = line.split()
i = int(l[2]) - 1
j = int(l[3]) - 1
bn = atom_name[i] + '-' + atom_name[j]
if not bn in rn.bonds:
bn = atom_name[j] + '-' + atom_name[i]
bnk = [mol, i, j]
bnkr = [mol, j, i]
find = False
if bnk in bdlab[bn]:
nb = bdlab[bn].index(bnk)
find = True
elif bnkr in bdlab[bn]:
nb = bdlab[bn].index(bnkr)
find = True
if find:
# if abs(rbd[bn][nb][0]-float(l[4]))>0.0001:
print('- ReaxFF %s:' % bn, 'rbd:', rbd[bn][nb][0], 'bop_si:',
bop_si[bn][nb][0], 'bop_pi:', bop_pi[bn][nb][0],
'bop_pp:', bop_pp[bn][nb][0])
print('- GULP %s:' % bn, 'rbd:', l[4], 'bop_si:', l[5],
'bop_pi:', l[6], 'bop_pp:', l[7])
else:
print('- GULP %s:' % bn, 'rbd:', l[4], 'bop_si:', l[5],
'bop_pi:', l[6], 'bop_pp:', l[7])
fg.close()
e_,eb_,el_,eo_,eu_,ea_,ep_,etc_,et_,ef_,ev_,ehb_,ecl_,esl_= \
get_reax_energy(fo='gulp.out')
print('- ebond - IRFF %f GULP %f.' % (ebond[mol], eb_))
def plddd(direcs={'ethane': '/home/gfeng/siesta/train/ethane'},
spec='C',
nbin=500,
batch_size=2000):
for m in direcs:
mol = m
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
optword='all',
batch_size=batch_size,
clip_op=False,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
delta = []
deltap = []
D = rn.get_value(rn.D)
atlab = rn.lk.atlab
natom = molecules[mol].natom
d = np.zeros([natom, batch_size])
dp = np.zeros([natom, batch_size])
cell = rn.cell[mol]
Dp_ = {}
for sp in rn.spec:
if rn.nsp[sp] > 0:
Dp_[sp] = tf.gather_nd(rn.Deltap, rn.atomlist[sp])
Dp = rn.get_value(Dp_)
plt.figure() # temperature
plt.ylabel(r'$\Delta$ distribution')
plt.xlabel(r"The value of $\Delta$ and $\Delta'$")
hist, bin_ = np.histogram(Dp[spec],
range=(np.min(Dp[spec]), np.max(Dp[spec])),
bins=nbin,
density=True)
plt.plot(bin_[:-1],
hist,
alpha=0.5,
color='blue',
linestyle=':',
label=r"$\Delta^{'}(%s)$ " % spec)
histp, bin_ = np.histogram(D[spec],
range=(np.min(D[spec]), np.max(D[spec])),
bins=nbin,
density=True)
plt.plot(bin_[:-1],
histp,
alpha=0.5,
color='yellowgreen',
linestyle='-.',
label=r'$\Delta$(%s)' % spec)
plt.legend(loc='best')
plt.savefig('delta_%s.eps' % spec)
plt.close()
def plbd(direcs={'ethane': '/home/gfeng/siesta/train/ethane'},
batch_size=50,
nn=False,
ffield='ffield',
bonds=[9, 41]):
for m in direcs:
mol = m
rn = ReaxFF(libfile=ffield,
direcs=direcs,
dft='siesta',
optword='all',
nn=nn,
InitCheck=False,
batch_size=batch_size,
clip_op=False,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
rbd = rn.get_value(rn.rbd)
bop = rn.get_value(rn.bop)
bo = rn.get_value(rn.bo0)
bopow1 = rn.get_value(rn.bopow1)
eterm1 = rn.get_value(rn.eterm1)
bop_si = rn.get_value(rn.bop_si)
bosi = rn.get_value(rn.bosi)
sieng = rn.get_value(rn.sieng)
powb = rn.get_value(rn.powb)
expb = rn.get_value(rn.expb)
bop_pi = rn.get_value(rn.bop_pi)
bopi = rn.get_value(rn.bopi)
bop_pp = rn.get_value(rn.bop_pp)
bopp = rn.get_value(rn.bopp)
if not nn:
f11 = rn.get_value(rn.F_11)
f12 = rn.get_value(rn.F_12)
f45 = rn.get_value(rn.F_45)
f = rn.get_value(rn.F)
bdlab = rn.lk.bdlab
atom_name = molecules[mol].atom_name
rn.close()
bd = bonds
bdn = atom_name[bd[0]] + '-' + atom_name[bd[1]]
if not bdn in rn.bonds:
bdn = atom_name[bd[1]] + '-' + atom_name[bd[0]]
bn = [mol, bd[0], bd[1]]
if bn in bdlab[bdn]:
bid = bdlab[bdn].index(bn)
else:
bid = bdlab[bdn].index([mol, bd[1], bd[0]])
plt.figure()
plt.subplot(3, 2, 1)
plt.plot(rbd[bdn][bid],
alpha=0.5,
color='b',
linestyle='-',
label="radius@%d-%d" % (bd[0], bd[1]))
plt.legend(loc='best', edgecolor='yellowgreen')
plt.subplot(3, 2, 2)
l = len(f[bdn][bid])
plt.plot(f[bdn][bid],
alpha=0.5,
color='r',
linestyle='-',
label="F@%d-%d" % (bd[0], bd[1]))
if nn:
plt.legend(loc='best', edgecolor='yellowgreen')
else:
x_ = int(0.3 * l)
y_ = f[bdn][bid][x_]
yt_ = y_ + 0.3 if y_ < 0.5 else y_ - 0.3
plt.annotate('F',
xy=(x_, y_),
xycoords='data',
xytext=(x_, yt_),
arrowprops=dict(arrowstyle='->', facecolor='red'))
if not nn:
plt.plot(f11[bdn][bid],
alpha=0.5,
color='g',
linestyle='-.',
label="F1@%d-%d" % (bd[0], bd[1]))
x_ = int(0.6 * l)
y_ = f11[bdn][bid][x_]
yt_ = y_ + 0.3 if y_ < 0.5 else y_ - 0.3
plt.annotate('F1',
xy=(x_, y_),
xycoords='data',
xytext=(x_, yt_),
arrowprops=dict(arrowstyle='->', facecolor='red'))
plt.plot(f45[bdn][bid],
alpha=0.5,
color='b',
linestyle=':',
label="F4@%d-%d" % (bd[0], bd[1]))
x_ = int(0.9 * l)
y_ = f45[bdn][bid][x_]
yt_ = y_ + 0.3 if y_ < 0.5 else y_ - 0.3
plt.annotate('F4',
xy=(x_, y_),
xycoords='data',
xytext=(x_, yt_),
arrowprops=dict(arrowstyle='->', facecolor='red'))
plt.subplot(3, 2, 3)
plt.plot(bo[bdn][bid],
alpha=0.5,
color='b',
linestyle='-',
label="BO@%d-%d" % (bd[0], bd[1]))
plt.legend(loc='best', edgecolor='yellowgreen')
plt.subplot(3, 2, 4)
plt.plot(bop_si[bdn][bid],
alpha=0.5,
color='b',
linestyle=':',
label=r"$BO^{'}_{si}$@%d-%d" % (bd[0], bd[1]))
plt.plot(bosi[bdn][bid],
alpha=0.5,
color='r',
linestyle='-',
label=r'$BO_{si}$@%d-%d' % (bd[0], bd[1]))
plt.legend(loc='best', edgecolor='yellowgreen')
plt.subplot(3, 2, 5)
plt.plot(sieng[bdn][bid],
alpha=0.5,
color='b',
linestyle='-',
label=r"$ebond_{si}$@%d-%d" % (bd[0], bd[1]))
plt.legend(loc='best', edgecolor='yellowgreen')
plt.subplot(3, 2, 6)
plt.plot(powb[bdn][bid],
alpha=0.5,
color='b',
linestyle='-',
label=r"$pow_{si}$@%d-%d" % (bd[0], bd[1]))
plt.plot(expb[bdn][bid],
alpha=0.5,
color='r',
linestyle='-',
label=r"$exp_{si}$@%d-%d" % (bd[0], bd[1]))
plt.legend(loc='best', edgecolor='yellowgreen')
# plt.subplot(3,2,6)
# # plt.ylabel(r'$exp$ (eV)')
# plt.xlabel(r"Step")
# plt.plot(eterm1[bdn][bid],alpha=0.5,color='b',
# linestyle='-',label=r"$exp_{si}$@%d-%d" %(bd[0],bd[1]))
# plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('bondorder.eps', transparent=True)
plt.close()
def plot_delta(direcs=None, batch_size=1, dft='siesta'):
for m in direcs:
mol = m
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft=dft,
opt=[],
optword='all',
batch_size=batch_size,
rc_scale='none',
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10, method='AdamOptimizer')
D = rn.get_value(rn.D)
Dlp = rn.get_value(rn.Dlp)
Dp_ = {}
for sp in rn.spec:
if rn.nsp[sp] > 0:
Dp_[sp] = tf.gather_nd(rn.Deltap, rn.atomlist[sp])
Dp = rn.get_value(Dp_)
atlab = rn.lk.atlab
traj = Trajectory('delta.traj', 'w')
trajp = Trajectory('deltap.traj', 'w')
trajlp = Trajectory('deltalp.traj', 'w')
natom = molecules[mol].natom
d = np.zeros([natom, batch_size])
dp = np.zeros([natom, batch_size])
dlp = np.zeros([natom, batch_size])
cell = rn.cell[mol]
for sp in rn.spec:
if rn.nsp[sp] > 0:
for l, lab in enumerate(atlab[sp]):
if lab[0] == mol:
i = int(lab[1])
d[i] = D[sp][l]
dp[i] = Dp[sp][l]
dlp[i] = Dlp[sp][l]
for nf in range(batch_size):
A = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[nf],
charges=d[:, nf],
cell=cell,
pbc=(1, 1, 1))
traj.write(A)
Ap = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[nf],
charges=dp[:, nf],
cell=cell,
pbc=(1, 1, 1))
trajp.write(Ap)
Alp = Atoms(symbols=molecules[mol].atom_name,
positions=molecules[mol].x[nf],
charges=dlp[:, nf],
cell=cell,
pbc=(1, 1, 1))
trajlp.write(Alp)
traj.close()
trajp.close()
trajlp.close()
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 plov(direcs={'ethane':'/home/gfeng/siesta/train/ethane'},
atoms=[8,51],
batch_size=2000):
for m in direcs:
mol = m
rn = ReaxFF(libfile='ffield',direcs=direcs,dft='siesta',
optword='all',
batch_size=batch_size,
clip_op=False,
pkl=False,
InitCheck=False,
interactive=True)
molecules = rn.initialize()
rn.session(learning_rate=1.0e-10,method='AdamOptimizer')
atom_name = molecules[mol].atom_name
D = rn.get_value(rn.D)
Dp = rn.get_value(rn.Delta_lpcorr)
Dl = rn.get_value(rn.Delta_lp)
De = rn.get_value(rn.Delta_e)
NLP = rn.get_value(rn.nlp)
Eov = rn.get_value(rn.EOV)
atlab = rn.lk.atlab
natom = molecules[mol].natom
d = np.zeros([natom,batch_size])
dlc = np.zeros([natom,batch_size])
dl = np.zeros([natom,batch_size])
de = np.zeros([natom,batch_size])
nlp = np.zeros([natom,batch_size])
eov = np.zeros([natom,batch_size])
cell = rn.cell[mol]
p = rn.p_
for sp in rn.spec:
if rn.nsp[sp]>0:
for l,lab in enumerate(atlab[sp]):
if lab[0]==mol:
i = int(lab[1])
d[i] = D[sp][l]
dlc[i] = Dp[sp][l]
dl[i] = Dl[sp][l]
de[i] = 2.0*De[sp][l]
nlp[i] = NLP[sp][l]
eov[i] = Eov[sp][l]
plt.figure()
plt.subplot(3,2,1)
# plt.ylabel(r'$\Delta_{lpcorr}$')
# plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(d[atm],alpha=0.5,color=colors[(i)%len(colors)],
label=r"$\Delta$@%s:%d" %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,2)
# plt.ylabel(r'$\Delta_{lpcorr}$')
# plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(dlc[atm],alpha=0.5,color=colors[(i)%len(colors)],
label=r"$\Delta_{lpcorr}$@%s:%d" %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,3)
# plt.ylabel(r'$\Delta_{lp}$')
# plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(dl[atm],alpha=0.5,color=colors[(i)%len(colors)],
label=r"$\Delta_{lp}$@%s:%d" %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,4)
# plt.ylabel(r'$\Delta_e$')
plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(de[atm],alpha=0.5,color=colors[(i)%len(colors)],
label=r'$\Delta_e$@%s:%d' %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,5)
# plt.ylabel(r'$Eover$')
plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(nlp[atm],alpha=0.5,color=colors[(i)%len(colors)],
label=r"$NLP$@%s:%d" %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.subplot(3,2,6)
# plt.ylabel(r'$Eover$')
plt.xlabel(r"Step")
for i,atm in enumerate(atoms):
plt.plot(dlc[atm]+p['val_'+atom_name[atm]],alpha=0.5,color=colors[(i)%len(colors)],
label=r"$\Delta_{lpcorr}+val$@%s:%d" %(atom_name[atm],atm))
plt.legend(loc='best',edgecolor='yellowgreen')
plt.savefig('Delta_lpcorr.eps',transparent=True)
plt.close()
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 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')