def step(self, step=None):
"""Does one simulation time step."""
info("\nMD STEP %d" % step, verbosity.debug)
# Fetch spring constants
self.nebbfgsm.kappa = self.spring["kappa"]
self.neblm.kappa = self.spring["kappa"]
self.ptime = self.ttime = 0
self.qtime = -time.time()
if self.mode == "lbfgs":
# L-BFGS Minimization
# Initialize direction to the steepest descent direction
if step == 0: # or np.sqrt(np.dot(self.bfgsm.d, self.bfgsm.d)) == 0.0: <-- this part for restarting at claimed minimum
info(" @GEOP: Initializing L-BFGS", verbosity.debug)
fx, nebgrad = self.nebbfgsm(self.beads.q)
# Set direction to direction of NEB forces
self.nebbfgsm.d = -nebgrad
self.nebbfgsm.xold = self.beads.q.copy()
# Initialize lists of previous positions and gradients
self.qlist = np.zeros(
(self.corrections, len(self.beads.q.flatten())))
self.glist = np.zeros(
(self.corrections, len(self.beads.q.flatten())))
else:
fx, nebgrad = self.nebbfgsm(self.beads.q)
# Intial gradient and gradient modulus
u0, du0 = (fx, nebgrad)
# Store old force
self.old_f[:] = -nebgrad
# Do one iteration of L-BFGS and return positions, gradient modulus,
# direction, list of positions, list of gradients
# self.beads.q, fx, self.nebbfgsm.d, self.qlist, self.glist = L_BFGS(self.beads.q,
L_BFGS(self.beads.q,
self.nebbfgsm.d,
self.nebbfgsm,
self.qlist,
self.glist,
fdf0=(u0, du0),
big_step=self.big_step,
tol=self.ls_options["tolerance"],
itmax=self.ls_options["iter"],
m=self.corrections,
scale=self.scale,
k=step)
info(" @GEOP: Updated position list", verbosity.debug)
info(" @GEOP: Updated gradient list", verbosity.debug)
# x = current position - previous position. Use to determine converged minimization
x = np.amax(
np.absolute(np.subtract(self.beads.q, self.nebbfgsm.xold)))
# Store old positions
self.nebbfgsm.xold[:] = self.beads.q
self.beads.q = self.nebbfgsm.dbeads.q
self.forces.transfer_forces(self.nebbfgsm.dforces)
info(" @GEOP: Updated bead positions", verbosity.debug)
# Routine for steepest descent and conjugate gradient
# TODO: CURRENTLY DOES NOT WORK. MUST BE ELIMINATED OR DEBUGGED
else:
if (self.mode == "sd" or step == 0):
# Steepest descent minimization
# gradf1 = force at current atom position
# dq1 = direction of steepest descent
# dq1_unit = unit vector of dq1
nebgrad = self.neblm(self.beads.q)[0]
gradf1 = dq1 = -nebgrad
# Move direction for steepest descent and 1st conjugate gradient step
dq1_unit = dq1 / np.sqrt(
np.dot(gradf1.flatten(), gradf1.flatten()))
info(" @GEOP: Determined SD direction", verbosity.debug)
else:
# Conjugate gradient, Polak-Ribiere
# gradf1: force at current atom position
# gradf0: force at previous atom position
# dq1 = direction to move
# dq0 = previous direction
# dq1_unit = unit vector of dq1
gradf0 = self.old_f
dq0 = self.old_d
nebgrad = self.neblm(self.beads.q)[0]
gradf1 = -nebgrad
beta = np.dot((gradf1.flatten() - gradf0.flatten()),
gradf1.flatten()) / (np.dot(
gradf0.flatten(), gradf0.flatten()))
dq1 = gradf1 + max(0.0, beta) * dq0
dq1_unit = dq1 / np.sqrt(np.dot(dq1.flatten(), dq1.flatten()))
info(" @GEOP: Determined CG direction", verbosity.debug)
# Store force and direction for next CG step
self.old_d[:] = dq1
self.old_f[:] = gradf1
if len(self.fixatoms) > 0:
for dqb in dq1_unit:
dqb[self.fixatoms * 3] = 0.0
dqb[self.fixatoms * 3 + 1] = 0.0
dqb[self.fixatoms * 3 + 2] = 0.0
self.neblm.set_dir(dstrip(self.beads.q), dq1_unit)
# Reuse initial value since we have energy and forces already
u0 = np.dot(-nebgrad.flatten(), dq1_unit.flatten())
u0 = np.sqrt(np.dot(u0, u0))
(x, fx) = min_brent_neb(self.neblm,
fdf0=u0,
x0=0.0,
tol=self.ls_options["tolerance"],
itmax=self.ls_options["iter"],
init_step=self.ls_options["step"])
# Automatically adapt the search step for the next iteration.
# Relaxes better with very small step --> multiply by factor of 0.1 or 0.01
self.ls_options["step"] = 0.1 * x * self.ls_options["adaptive"] + (
1 - self.ls_options["adaptive"]) * self.ls_options["step"]
self.beads.q += dq1_unit * x
info(" @GEOP: Updated bead positions", verbosity.debug)
self.qtime += time.time()
# Determine conditions for converged relaxation
if ((fx - u0) / self.beads.natoms <= self.tolerances["energy"])\
and ((np.amax(np.absolute(self.forces.f)) <= self.tolerances["force"])
or (np.sqrt(np.dot(self.forces.f.flatten() - self.old_f.flatten(),
self.forces.f.flatten() - self.old_f.flatten())) == 0.0))\
and (x <= self.tolerances["position"]):
softexit.trigger(
"Geometry optimization converged. Exiting simulation")
else:
info(
" @GEOP: Not converged, deltaEnergy = %.8f, tol = %.8f" %
((fx - u0 / self.beads.natoms), self.tolerances["energy"]),
verbosity.debug)
info(
" @GEOP: Not converged, force = %.8f, tol = %f" % (np.amax(
np.absolute(self.forces.f)), self.tolerances["force"]),
verbosity.debug)
info(
" @GEOP: Not converged, deltaForce = %.8f, tol = 0.00000000" %
(np.sqrt(
np.dot(self.forces.f.flatten() - self.old_f.flatten(),
self.forces.f.flatten() - self.old_f.flatten()))),
verbosity.debug)
info(
" @GEOP: Not converged, deltaX = %.8f, tol = %.8f" %
(x, self.tolerances["position"]), verbosity.debug)
def step(self, step=None):
"""Does one simulation time step."""
info("\nMD STEP %d" % step, verbosity.debug)
# Fetch spring constants
self.nebbfgsm.kappa = self.spring["kappa"]
self.neblm.kappa = self.spring["kappa"]
self.ptime = self.ttime = 0
self.qtime = -time.time()
if self.mode == "lbfgs":
# L-BFGS Minimization
# Initialize direction to the steepest descent direction
if step == 0: # or np.sqrt(np.dot(self.bfgsm.d, self.bfgsm.d)) == 0.0: <-- this part for restarting at claimed minimum
info(" @GEOP: Initializing L-BFGS", verbosity.debug)
fx, nebgrad = self.nebbfgsm(self.beads.q)
# Set direction to direction of NEB forces
self.nebbfgsm.d = -nebgrad
self.nebbfgsm.xold = self.beads.q.copy()
# Initialize lists of previous positions and gradients
self.qlist = np.zeros((self.corrections, len(self.beads.q.flatten())))
self.glist = np.zeros((self.corrections, len(self.beads.q.flatten())))
else:
fx, nebgrad = self.nebbfgsm(self.beads.q)
# Intial gradient and gradient modulus
u0, du0 = (fx, nebgrad)
# Store old force
self.old_f[:] = -nebgrad
# Do one iteration of L-BFGS and return positions, gradient modulus,
# direction, list of positions, list of gradients
# self.beads.q, fx, self.nebbfgsm.d, self.qlist, self.glist = L_BFGS(self.beads.q,
L_BFGS(self.beads.q, self.nebbfgsm.d, self.nebbfgsm, self.qlist, self.glist,
fdf0=(u0, du0), big_step=self.big_step, tol=self.ls_options["tolerance"],
itmax=self.ls_options["iter"],
m=self.corrections, scale=self.scale, k=step)
info(" @GEOP: Updated position list", verbosity.debug)
info(" @GEOP: Updated gradient list", verbosity.debug)
# x = current position - previous position. Use to determine converged minimization
x = np.amax(np.absolute(np.subtract(self.beads.q, self.nebbfgsm.xold)))
# Store old positions
self.nebbfgsm.xold[:] = self.beads.q
self.beads.q = self.nebbfgsm.dbeads.q
self.forces.transfer_forces(self.nebbfgsm.dforces)
info(" @GEOP: Updated bead positions", verbosity.debug)
# Routine for steepest descent and conjugate gradient
# TODO: CURRENTLY DOES NOT WORK. MUST BE ELIMINATED OR DEBUGGED
else:
if (self.mode == "sd" or step == 0):
# Steepest descent minimization
# gradf1 = force at current atom position
# dq1 = direction of steepest descent
# dq1_unit = unit vector of dq1
nebgrad = self.neblm(self.beads.q)[0]
gradf1 = dq1 = -nebgrad
# Move direction for steepest descent and 1st conjugate gradient step
dq1_unit = dq1 / np.sqrt(np.dot(gradf1.flatten(), gradf1.flatten()))
info(" @GEOP: Determined SD direction", verbosity.debug)
else:
# Conjugate gradient, Polak-Ribiere
# gradf1: force at current atom position
# gradf0: force at previous atom position
# dq1 = direction to move
# dq0 = previous direction
# dq1_unit = unit vector of dq1
gradf0 = self.old_f
dq0 = self.old_d
nebgrad = self.neblm(self.beads.q)[0]
gradf1 = -nebgrad
beta = np.dot((gradf1.flatten() - gradf0.flatten()), gradf1.flatten()) / (np.dot(gradf0.flatten(), gradf0.flatten()))
dq1 = gradf1 + max(0.0, beta) * dq0
dq1_unit = dq1 / np.sqrt(np.dot(dq1.flatten(), dq1.flatten()))
info(" @GEOP: Determined CG direction", verbosity.debug)
# Store force and direction for next CG step
self.old_d[:] = dq1
self.old_f[:] = gradf1
if len(self.fixatoms) > 0:
for dqb in dq1_unit:
dqb[self.fixatoms * 3] = 0.0
dqb[self.fixatoms * 3 + 1] = 0.0
dqb[self.fixatoms * 3 + 2] = 0.0
self.neblm.set_dir(dstrip(self.beads.q), dq1_unit)
# Reuse initial value since we have energy and forces already
u0 = np.dot(-nebgrad.flatten(), dq1_unit.flatten())
u0 = np.sqrt(np.dot(u0, u0))
(x, fx) = min_brent_neb(self.neblm, fdf0=u0, x0=0.0,
tol=self.ls_options["tolerance"],
itmax=self.ls_options["iter"], init_step=self.ls_options["step"])
# Automatically adapt the search step for the next iteration.
# Relaxes better with very small step --> multiply by factor of 0.1 or 0.01
self.ls_options["step"] = 0.1 * x * self.ls_options["adaptive"] + (1 - self.ls_options["adaptive"]) * self.ls_options["step"]
self.beads.q += dq1_unit * x
info(" @GEOP: Updated bead positions", verbosity.debug)
self.qtime += time.time()
# Determine conditions for converged relaxation
if ((fx - u0) / self.beads.natoms <= self.tolerances["energy"])\
and ((np.amax(np.absolute(self.forces.f)) <= self.tolerances["force"])
or (np.sqrt(np.dot(self.forces.f.flatten() - self.old_f.flatten(),
self.forces.f.flatten() - self.old_f.flatten())) == 0.0))\
and (x <= self.tolerances["position"]):
softexit.trigger("Geometry optimization converged. Exiting simulation")
else:
info(" @GEOP: Not converged, deltaEnergy = %.8f, tol = %.8f" % ((fx - u0 / self.beads.natoms), self.tolerances["energy"]), verbosity.debug)
info(" @GEOP: Not converged, force = %.8f, tol = %f" % (np.amax(np.absolute(self.forces.f)), self.tolerances["force"]), verbosity.debug)
info(" @GEOP: Not converged, deltaForce = %.8f, tol = 0.00000000" % (np.sqrt(np.dot(self.forces.f.flatten() - self.old_f.flatten(), self.forces.f.flatten() - self.old_f.flatten()))), verbosity.debug)
info(" @GEOP: Not converged, deltaX = %.8f, tol = %.8f" % (x, self.tolerances["position"]), verbosity.debug)
