def opt(s):
sm, info = minimize(e,
s,
maxit=50,
ftol=1.0e-2,
xtol=1.0e-2,
algo=1)
print("converged=", info["converged"], "in",
info["iterations"])
return sm
def test_uranyl ():
e = compose (uo2, uranyl)
e = Memoize (e, DirStore (salt="XXX YYY"))
# s = array ([1.79, 1.79, pi])
s = zeros (6) # + 0.1
print (uranyl (s))
# print (e(s))
# exit (0)
sm, info = minimize (e, s, ftol=1.0e-2, xtol=1.0e-2, algo=1)
print (uranyl (sm))
print ("e(0)=", e (s), "e(1)=", e(sm), "iterations=", info["iterations"])
def test_uranyl():
e = compose(uo2, uranyl)
e = Memoize(e, DirStore(salt="XXX YYY"))
# s = array ([1.79, 1.79, pi])
s = zeros(6) # + 0.1
print(uranyl(s))
# print (e(s))
# exit (0)
sm, info = minimize(e, s, ftol=1.0e-2, xtol=1.0e-2, algo=1)
print(uranyl(sm))
print("e(0)=", e(s), "e(1)=", e(sm), "iterations=", info["iterations"])
def initial_path (f, s, c):
"""
This is called with pure MM PES f(s) to get some initial
path. Somewhat ad-hoc.
"""
# The input geometry s may be reasonable, but it may not be an
# exact minimum. Reoptimize:
s, info = minimize (f, s, maxit=100, ftol=5.0e-4, xtol=5.0e-4, algo=1)
print ("converged=", info["converged"], "in", info["iterations"])
# The value of reaction coordinate in the initial geometry:
c0 = c(s)
print ("XXX: rc(0)=", c0)
it = 0
ss = []
qs = []
algo = 1
while c(s) > -c0:
it += 1
# write_xyz ("in-%03d.xyz" % it, trafo (s))
print ("XXX: rc(0)=", c(s))
s, info = cminimize (f, s, Array (c), maxit=200, ftol=5.0e-3, xtol=5.0e-3, algo=algo)
# Collect optimized geometries and the corresponding
# values of reaction coordinate:
ss.append (s)
qs.append (c(s))
print ("converged=", info["converged"], "in", info["iterations"])
print ("XXX: rc(1)=", c(s))
# write_xyz ("out-%03d.xyz" % it, trafo (s))
# Here c.fprime(s) is how much that reaction coordinate
# will change if you modify the coordinates. Hacky way to
# chage a geometry so that the RC is modified too:
s = s - 0.1 * c.fprime (s)
ss = asarray (ss)
qs = asarray (qs)
print ("qs=", qs)
p = Path (ss, qs)
print ("path=", p)
qs = linspace (c0, -c0, 21)
print ("qs=", qs)
ss = map (p, qs)
res = map (lambda s: cminimize (f, s, Array (c), maxit=200, ftol=1.0e-3, xtol=1.0e-3, algo=algo), ss)
infos = [inf for _, inf in res]
ss = [s for s, _ in res]
for info in infos:
print ("converged=", info["converged"], "in", info["iterations"])
for i, s in enumerate (ss):
write_xyz ("out-%03d.xyz" % i, trafo (s))
return qs, ss
def opt (e, s, name, **kwargs):
print ("XXX: " + name + "...")
print (name + ": e(0)=", e (s) / kcal, "kcal, |g|=", max (abs (e.fprime (s))) / kcal, "kcal/Unit")
s, info = minimize (e, s, **kwargs)
print (name + ": converged =", info["converged"], "in", info["iterations"], "iterations")
write_xyz (name + ".xyz", trafo (s))
# print info
if "trajectory" in info:
traj = info["trajectory"]
print (map(e, traj))
for i, si in enumerate (traj):
# write_xyz ("traj-%s-%03d.xyz" % (name, i), trafo (si))
pass
return s, info
def opt(e, s, name, **kwargs):
print("XXX: " + name + "...")
print(name + ": e(0)=",
e(s) / kcal, "kcal, |g|=",
max(abs(e.fprime(s))) / kcal, "kcal/Unit")
s, info = minimize(e, s, **kwargs)
print(name + ": converged =", info["converged"], "in",
info["iterations"], "iterations")
write_xyz(name + ".xyz", trafo(s))
# print info
if "trajectory" in info:
traj = info["trajectory"]
print(map(e, traj))
for i, si in enumerate(traj):
# write_xyz ("traj-%s-%03d.xyz" % (name, i), trafo (si))
pass
return s, info
def main(argv):
from ase.io import read, write
from pts.fopt import minimize
from sys import stdout, stderr
for path in argv[1:]:
atoms = read(path)
symbols = atoms.get_chemical_symbols()
x = atoms.get_positions()
f = Gupta(symbols)
xm, info = minimize(f, x)
# print >> stderr, "e=", f(xm), \
# "converged=", info["converged"], \
# "in", info["iterations"], "iterations"
if not info["converged"]:
print >> stderr, "Not converged:", path
atoms.set_positions(xm)
write(stdout, atoms, format="xyz")
def main(argv):
from ase.io import read, write
from pts.fopt import minimize
from sys import stdout, stderr
for path in argv[1:]:
atoms = read(path)
symbols = atoms.get_chemical_symbols()
x = atoms.get_positions()
f = Gupta(symbols)
xm, info = minimize(f, x)
# print >> stderr, "e=", f(xm), \
# "converged=", info["converged"], \
# "in", info["iterations"], "iterations"
if not info["converged"]:
print >> stderr, "Not converged:", path
atoms.set_positions(xm)
write(stdout, atoms, format="xyz")
def initial_path(f, s, c):
"""
This is called with pure MM PES f(s) to get some initial
path. Somewhat ad-hoc.
"""
# The input geometry s may be reasonable, but it may not be an
# exact minimum. Reoptimize:
s, info = minimize(f, s, maxit=100, ftol=5.0e-4, xtol=5.0e-4, algo=1)
print("converged=", info["converged"], "in", info["iterations"])
# The value of reaction coordinate in the initial geometry:
c0 = c(s)
print("XXX: rc(0)=", c0)
it = 0
ss = []
qs = []
algo = 1
while c(s) > -c0:
it += 1
# write_xyz ("in-%03d.xyz" % it, trafo (s))
print("XXX: rc(0)=", c(s))
s, info = cminimize(f,
s,
Array(c),
maxit=200,
ftol=5.0e-3,
xtol=5.0e-3,
algo=algo)
# Collect optimized geometries and the corresponding
# values of reaction coordinate:
ss.append(s)
qs.append(c(s))
print("converged=", info["converged"], "in", info["iterations"])
print("XXX: rc(1)=", c(s))
# write_xyz ("out-%03d.xyz" % it, trafo (s))
# Here c.fprime(s) is how much that reaction coordinate
# will change if you modify the coordinates. Hacky way to
# chage a geometry so that the RC is modified too:
s = s - 0.1 * c.fprime(s)
ss = asarray(ss)
qs = asarray(qs)
print("qs=", qs)
p = Path(ss, qs)
print("path=", p)
qs = linspace(c0, -c0, 21)
print("qs=", qs)
ss = map(p, qs)
res = map(
lambda s: cminimize(
f, s, Array(c), maxit=200, ftol=1.0e-3, xtol=1.0e-3, algo=algo),
ss)
infos = [inf for _, inf in res]
ss = [s for s, _ in res]
for info in infos:
print("converged=", info["converged"], "in", info["iterations"])
for i, s in enumerate(ss):
write_xyz("out-%03d.xyz" % i, trafo(s))
return qs, ss
(1, 2, None)], base=1)
#
# Initial values of internal coordinates:
#
s = zmt.pinv (x)
assert max (abs (s - zmt.pinv (zmt (s)))) < 1.0e-10
clean ()
with QFunc (atoms, calc) as f:
f = Memoize (f, DirStore (salt="h2o, qm"))
e = compose (f, zmt)
print s, e (s)
s, info = minimize (e, s, algo=1, ftol=1.0e-2, xtol=1.0e-2)
print s, e (s), info["converged"]
#
# Internal coordinates:
#
# In: 0.97843364 0.97826543 1.87152024
# Out: 0.98477091 0.98477695 1.88111918
#
# Rigid water with optimized internal coordinates:
#
X = Rigid (zmt (s))
# Five optimized rigid waters, actual location and orientation to be
# specified ...
waters = [X] * 5
def opt (s):
sm, info = minimize (e, s, maxit=50, ftol=1.0e-2, xtol=1.0e-2, algo=1)
print ("converged=", info["converged"], "in", info["iterations"])
return sm
zmt = ZMat([(None, None, None), (1, None, None), (1, 2, None)], base=1)
#
# Initial values of internal coordinates:
#
s = zmt.pinv(x)
assert max(abs(s - zmt.pinv(zmt(s)))) < 1.0e-10
clean()
with QFunc(atoms, calc) as f:
f = Memoize(f, DirStore(salt="h2o, qm"))
e = compose(f, zmt)
print s, e(s)
s, info = minimize(e, s, algo=1, ftol=1.0e-2, xtol=1.0e-2)
print s, e(s), info["converged"]
#
# Internal coordinates:
#
# In: 0.97843364 0.97826543 1.87152024
# Out: 0.98477091 0.98477695 1.88111918
#
# Rigid water with optimized internal coordinates:
#
X = Rigid(zmt(s))
# Five optimized rigid waters, actual location and orientation to be
# specified ...
waters = [X] * 5