def grada(direcs,v='bo1_H-H',dft='siesta',batch=100):
''' variables like: bo1_C-H, boc5_C rosi_C-H boc1
'''
print('- grading ... ... ')
for m in direcs:
mol = m
rn = IRNN(libfile='ffield',direcs=direcs,
dft=dft,
batch_size=batch,
pkl=True)
rn.initialize()
rn.session(learning_rate=1.0e-4,method='AdamOptimizer')
fg = open('gradient.txt','w')
l = 'eang'
grad = rn.get_gradient(rn.__dict__[l][mol],rn.p[v])
print('- the gradient of %s_%s/%s is: ' %(l,mol,v),grad,file=fg)
fg.close()
# anglit = ['D_ang','thet','theta0','expang','f_7','f_8','EANG','EPEN','ETC']
anglit = ['sbo','f_8','EANG','EPEN','ETC']
angs = rn.angs
# angs = ['H-O-H']
for ang in angs:
if rn.nang[ang]>0:
for l in anglit:
grad = rn.get_gradient(rn.__dict__[l][ang],rn.p[v])
fg = open('gradient.txt','a')
print('- the gradient of %s/%s is: ' %(l+'_'+ang,v),
grad,file=fg)
fg.close()
rn.sess.close()
def gradt(v='bo2_C-N'):
''' variables like: bo1_C-H, boc5_C rosi_C-H boc1
'''
print('- grading ... ... ')
direcs = {
'nm': '/home/feng/cpmd_data/packmol/nm',
'nme': '/home/feng/cpmd_data/packmol/nme'
}
rn = IRNN(libfile='ffield',
direcs=direcs,
dft='cpmd',
batch_size=200,
pkl=True)
rn.initialize()
rn.session(learning_rate=1.0e-4, method='AdamOptimizer')
fg = open('gradient.txt', 'w')
grad = rn.get_gradient(rn.Loss, rn.p[v])
print('- the gradient of Loss/%s is: ' % v, grad, file=fg)
molit = ['etor', 'efcon']
for mol in rn.direcs:
for l in molit:
grad = rn.get_gradient(rn.__dict__[l][mol], rn.p[v])
print('- the gradient of %s/%s is: ' % (l + '_' + mol, v),
grad,
file=fg)
fg.close()
torlit = ['f_10', 'f_11', 'expv2', 'ETOR', 'Efcon']
i = 0
for tor in rn.tors:
if rn.ntor[tor] > 0:
# if i<=100:
if tor == 'C-O-O-N' or tor == 'C-O-O-H':
for l in torlit:
fg = open('gradient.txt', 'a')
grad = rn.get_gradient(rn.__dict__[l][tor], rn.p[v])
print('- the gradient of %s/%s is: ' % (l + '_' + tor, v),
grad,
file=fg)
fg.close()
i += 1
rn.sess.close()
def gradb(direcs,v='bo5',bd='C-O',dft='siesta',batch=100):
''' variables like: bo1_C-H, boc5_C rosi_C-H boc1
'''
v = v+'_'+bd
print('- grading ... ...')
for m in direcs:
mol = m
rn = IRNN(libfile='ffield',direcs=direcs,
dft=dft,
batch_size=batch,
pkl=True,
interactive=True)
rn.initialize()
rn.session(learning_rate=3.0e-4,method='AdamOptimizer')
fg = open('gradient.txt','w')
bdlit = ['bop_si','bop_pi','bop_pp',
'bosi','F',
'bopi',
'bopp',
'bo','bso',
'powb','expb','EBD']
# bdlit = ['bo','EBD']
gl = rn.get_gradient(rn.Loss,rn.p[v])
print('- the gradient of Loss/%s is:' %v,gl,file=fg)
bonds = rn.bonds
# bonds = [bd]
for b in bonds:
if rn.nbd[b]>0:
for l in bdlit:
grad = rn.get_gradient(rn.__dict__[l][b],rn.p[v])
print('- the gradient of %s/%s is: ' %(l+'_'+b,v),
grad,file=fg)
fg.close()
ml = ['ebond','elone','eover','eunder','eang',
'epen','tconj','etor','efcon','evdw','ehb']
# for mol in direcs:
for l in ml:
grad = rn.get_gradient(rn.__dict__[l][mol],rn.p[v])
fg = open('gradient.txt','a')
print('- the gradient of %s_%s/%s is: ' %(l,mol,v),grad,file=fg)
fg.close()
alit = ['Delta_lp','Delta_lpcorr','Dpi','Delta_e','nlp','slp','EL',
'EOV','so','otrm1','otrm2',
'EUN']
atoms = rn.spec
for l in alit:
# atoms = bd.split('-')
for a in atoms:
grad = rn.get_gradient(rn.__dict__[l][a],rn.p[v])
fg = open('gradient.txt','a')
print('- the gradient of %s_%s/%s is: ' %(l,a,v),grad,file=fg)
fg.close()
rn.sess.close()