def g(gen='poscar.gen'):
atoms = read(gen)
ir = IRFF(atoms=atoms, libfile='ffield.json', nn=True, autograd=True)
ir.calculate(atoms)
print(ir.grad)
ir_ = IRFF_TF(atoms=atoms, libfile='ffield.json', nn=True)
ir_.calculate(atoms)
print(ir_.grad.numpy())
from ase.optimize import BFGS, FIRE
from irff.md import opt
from irff.irff import IRFF
from ase.io.trajectory import Trajectory
import matplotlib.pyplot as plt
images = Trajectory('pre_opt.traj', mode='r')
ir = IRFF(atoms=images[0], libfile='ffield.json', rcut=None, nn=True)
GPa = 1.60217662 * 1.0e2
v_, p = [], []
v0 = images[0].get_volume()
for atoms_ in images:
ir.calculate(atoms=atoms_, CalStress=True)
stress = ir.results['stress']
nonzero = 0
stre_ = 0.0
for _ in range(3):
if abs(stress[_]) > 0.0000001:
nonzero += 1
stre_ += -stress[_]
pressure = stre_ * GPa / nonzero
p.append(pressure)
v = atoms_.get_volume()
v_.append(v / v0)
print(' * V/V0', v_[-1], v, pressure)
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 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 da(traj='opt.traj', batch_size=50, ef=3, nn=True, frame=44):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
his = TrajectoryWriter('tmp.traj', mode='w')
his.write(atoms=atoms)
# his.write(atoms=images[frame+1])
his.close()
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn, autograd=False)
ir.calculate(atoms)
rn = MPNN(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],
EnergyFunction=ef,
pkl=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()
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]),
'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]),
'theta: %10.8f' % (theta_[ang][a_][0]),
# 'sbo3: %10.8f' %(sbo3_[ang][a_][0]),
'theta0: %10.8f' % (theta0_[ang][a_][0]))
fa.close()
epen = rn.get_value(rn.epen)
eang = rn.get_value(rn.eang)
print(' * penalty energy:', ir.Epen.numpy(), epen[mol][0])
print(' * angel energy:', ir.Eang.numpy(), eang[mol][0])
def db(traj='opt.traj', batch_size=50, ef=3, nn=True, frame=40):
ffield = 'ffield.json' if nn else 'ffield'
images = Trajectory(traj)
atoms = images[frame]
e, ei = [], []
ir = IRFF(atoms=atoms, libfile=ffield, nn=nn, autograd=False)
ir.calculate(atoms)
ei.append(ir.Ebond)
ebond = ir.ebond # .numpy()
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=ef,
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()
D_ = rn.get_value(rn.Deltap)
D = ir.Deltap.numpy()
ebd_ = np.zeros([ir.natom, ir.natom])
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])
ebd_[i][j] = ebd[nb][frame]
ebd_[j][i] = ebd[nb][frame]
# 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]),
'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('\n- bond energy:', ir.Ebond.numpy(), eb[frame], end='\n')
for i in range(ir.natom):
for j in range(ir.natom):
if abs(ebd_[i][j] - ebond[i][j]) > 0.00001:
print('- %s %2d %2d:' % (bd, i, j), 'rbd %10.7f' % (r[i][j]),
'bop %10.7f' % (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' % (bo[i][j]),
'ebond %10.7f %10.7f' % (ebd_[i][j], ebond[i][j]))
# print(rcbo)
print('\n- bond energy:', ir.Ebond.numpy(), eb[frame], end='\n')
def static_compress():
traj = TrajectoryWriter('pre_opt.traj', mode='w')
atoms = read('nm.gen')
cell = atoms.get_cell()
atoms_ = atoms.copy()
configs = []
GPa = 1.60217662 * 1.0e2
s = 1.0
i = 0
ir = IRFF(atoms=atoms_, libfile='ffield.json', nn=True)
v_, p = [], []
v0 = atoms.get_volume()
while s > 0.66:
print(' * lattice step:', i)
f = s**(1.0 / 3.0)
cell_ = cell.copy()
cell_ = cell * f
ir.CalStress = False
atoms_.set_cell(cell_)
atoms_ = opt(atoms=atoms_, fmax=0.02, step=120, optimizer=FIRE)
configs.append(atoms_)
traj.write(atoms=atoms_)
ir.CalStress = True
ir.calculate(atoms=atoms_)
stress = ir.results['stress']
nonzero = 0
stre_ = 0.0
for _ in range(3):
if abs(stress[_]) > 0.0000001:
nonzero += 1
stre_ += -stress[_]
pressure = stre_ * GPa / nonzero
p.append(pressure)
v = atoms_.get_volume()
v_.append(v / v0)
print(' * V/V0', v_[-1], v, pressure)
s = s * 0.98
i += 1
fig, ax = plt.subplots()
plt.ylabel(r'$Pressure$ ($GPa$)')
plt.xlabel(r'$V/V_0$')
plt.plot(v_,
p,
label=r'$IRFF-MPNN$',
color='blue',
marker='o',
markerfacecolor='none',
markeredgewidth=1,
ms=5,
alpha=0.8,
linewidth=1,
linestyle='-')
plt.legend(loc='best', edgecolor='yellowgreen')
plt.savefig('pv.pdf')
plt.close()