def train_reax(writelib=1000,
print_step=10,
step=500,
opt=None,
cons=None,
lr=1.0e-4,
convergence=0.97,
lossConvergence=100.0,
batch=50):
rn = ReaxFF(libfile='ffield.json',
direcs=direcs,
dft='siesta',
optword='nocoul',
opt=None,
cons=None,
batch_size=batch,
losFunc='n2',
lambda_bd=10000.0,
lambda_me=0.01,
weight={
'al4': 2.0,
'others': 2.0
},
convergence=convergence,
lossConvergence=lossConvergence
) # Loss Functon can be n2,abs,mse,huber
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
loss, accu, accMax, i, zpe = rn.run(learning_rate=lr,
step=step,
print_step=print_step,
writelib=writelib)
libstep = int(i - i % writelib)
if i == libstep:
libstep = libstep - writelib
if libstep <= 0:
ffd = 'ffield.json'
else:
ffd = 'ffield_' + str(libstep) + '.json'
if loss == 0.0 and accu == 0.0:
send_msg(
'- Warning: the loss is NaN, parameters from %s changed auomatically ...'
% ffd)
return 0.0, 1.0, 1.0, None, None, i
# with open(ffd,'r') as fj:
# j = js.load(fj)
# ic = Init_Check(nanv=nanv)
# j['p'] = ic.auto(j['p'])
# ic.close()
#with open('ffield.json','w') as fj:
# js.dump(j,fj,sort_keys=True,indent=2)
p = rn.p_
ME = rn.MolEnergy_
rn.close()
return loss, accu, accMax, p, ME, i
def z():
opt = ['atomic', 'ovun5', 'Desi', 'Depi', 'Depp', 'Devdw', 'Dehb'],
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
atomic=True,
optword='nocoul',
opt=['atomic'],
nn=False,
cons=None,
pkl=True,
batch_size=batch,
losFunc='n2',
bo_penalty=10000.0)
# tc.session(learning_rate=1.0e-4,method='AdamOptimizer')
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
rn.run(learning_rate=100, step=1000, print_step=10, writelib=1000)
rn.close()
def r():
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
atomic=True,
InitCheck=False,
optword='nocoul',
VariablesToOpt=vto,
pkl=True,
batch_size=batch,
losFunc='n2',
bo_penalty=10.0)
# tc.session(learning_rate=1.0e-4,method='AdamOptimizer')
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
rn.run(
learning_rate=1.0e-4,
step=10000,
method='AdamOptimizer', # SGD
print_step=10,
writelib=1000)
rn.close()
def t():
opt = ['boc1', 'boc2', 'boc3', 'boc4', 'boc5', 'valboc']
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
atomic=True,
InitCheck=True,
optword='nocoul',
VariablesToOpt=vto,
nn=False,
bo_layer=[9, 2],
pkl=True,
batch_size=batch,
losFunc='n2',
bo_penalty=10.0)
# tc.session(learning_rate=1.0e-4,method='AdamOptimizer')
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
rn.sa(total_step=1000,
step=100000,
astep=3000,
print_step=10,
writelib=1000)
rn.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()