def find_escape_paths(minimum,
potential,
graph,
ntries=1,
push=1.e-2,
push_minrms=1.e-2):
print "Single ended search for minimum", minimum._id, minimum.energy
search = DimerSearch(minimum.coords,
potential,
zeroEigenVecs=zeroEV_cluster)
for i in xrange(ntries):
x_ts, energy_ts, rms, tmp = _uphill_search(minimum.coords, search,
push, push_minrms)
ret1, ret2 = minima_from_ts(potential, x_ts, displace=1e-2)
min1 = graph.addMinimum(ret1[1], ret1[0])
min2 = graph.addMinimum(ret2[1], ret2[0])
if (not min1 is minimum and not min2 is minimum):
print "Warning in single ended search: did not find initial minimum during quench from transition state"
if (min1 is min2):
print "Warning in single ended search: downhill search from transistion state ended in same minimum"
ts = graph.addTransitionState(energy_ts, x_ts, min1, min2)
print "found transition state: ", min1._id, min2._id, min1.energy, ts.energy, min2.energy
search.findNextTS()
def _refineTS(pot, coords, tsSearchParams=dict(), eigenvec0=None):
"""
find nearest transition state to NEB climbing image. Then fall
off the transition state to find the associated minima.
This would naturally be a part of DoubleEndedConnect. I separated it
to make it more easily parallelizable.
"""
#run ts search algorithm
kwargs = dict(defaults.tsSearchParams.items() + tsSearchParams.items())
ret = findTransitionState(coords, pot, eigenvec0=eigenvec0, **kwargs)
#check to make sure it is a valid transition state
coords = ret.coords
if not ret.success:
print "transition state search failed"
return False, None
if ret.eigenval >= 0.:
print "warning: transition state has positive lowest eigenvalue, skipping:", ret.eigenval, ret.energy, ret.rms
print " not adding transition state"
return False, None
#find the minima which this transition state connects
print "falling off either side of transition state to find new minima"
ret1, ret2 = minima_from_ts(pot.getEnergyGradient, coords, n = ret.eigenvec, \
displace=1e-3, quenchParameters={"tol":1e-7, "iprint":-1})
return True, ret, ret1, ret2
def _refineTS(pot, coords, tsSearchParams=dict(), eigenvec0=None, pushoff_params=dict()):
"""
find nearest transition state to NEB climbing image. Then fall
off the transition state to find the associated minima.
This would naturally be a part of DoubleEndedConnect. I separated it
to make it more easily parallelizable.
"""
#run ts search algorithm
kwargs = dict(tsSearchParams.items())
ret = findTransitionState(coords, pot, eigenvec0=eigenvec0, **kwargs)
#check to make sure it is a valid transition state
coords = ret.coords
if not ret.success:
logger.info("transition state search failed")
return False, ret, None, None
if ret.eigenval >= 0.:
logger.info("transition state has positive lowest eigenvalue, skipping: %s %s %s", ret.eigenval, ret.energy, ret.rms)
logger.info( " not adding transition state")
return False, ret, None, None
#find the minima which this transition state connects
logger.info("falling off either side of transition state to find new minima")
ret1, ret2 = minima_from_ts(pot, coords, n = ret.eigenvec, \
**pushoff_params)
# print "testing", ret1.energy
return True, ret, ret1, ret2
def find_escape_paths(minimum, potential, graph, ntries=1, push=1.e-2, push_minrms=1.e-2):
print "Single ended search for minimum", minimum._id, minimum.energy
search = DimerSearch(minimum.coords, potential, zeroEigenVecs=zeroEV_cluster)
for i in xrange(ntries):
x_ts, energy_ts, rms, tmp = _uphill_search(minimum.coords, search, push, push_minrms)
ret1, ret2 = minima_from_ts(potential, x_ts, displace=1e-2)
min1 = graph.addMinimum(ret1[1], ret1[0])
min2 = graph.addMinimum(ret2[1], ret2[0])
if(not min1 is minimum and not min2 is minimum):
print "Warning in single ended search: did not find initial minimum during quench from transition state"
if(min1 is min2):
print "Warning in single ended search: downhill search from transistion state ended in same minimum"
ts = graph.addTransitionState(energy_ts, x_ts, min1, min2)
print "found transition state: ", min1._id, min2._id, min1.energy,ts.energy,min2.energy
search.findNextTS()
def _refineTS(pot,
coords,
tsSearchParams=dict(),
eigenvec0=None,
pushoff_params=dict()):
"""
find nearest transition state to NEB climbing image. Then fall
off the transition state to find the associated minima.
This would naturally be a part of DoubleEndedConnect. I separated it
to make it more easily parallelizable.
"""
#run ts search algorithm
kwargs = dict(tsSearchParams.items())
ret = findTransitionState(coords, pot, eigenvec0=eigenvec0, **kwargs)
#check to make sure it is a valid transition state
coords = ret.coords
if not ret.success:
logger.info("transition state search failed")
return False, ret, None, None
if ret.eigenval >= 0.:
logger.info(
"transition state has positive lowest eigenvalue, skipping: %s %s %s",
ret.eigenval, ret.energy, ret.rms)
logger.info(" not adding transition state")
return False, ret, None, None
#find the minima which this transition state connects
logger.info(
"falling off either side of transition state to find new minima")
ret1, ret2 = minima_from_ts(pot, coords, n = ret.eigenvec, \
**pushoff_params)
# print "testing", ret1.energy
return True, ret, ret1, ret2
