def test_ohch3f_anion(calc_cls, calc_kwargs, ref_cycle, ccl_dist, oc_dist):
"""Example (R1) from
https://aip.scitation.org/doi/pdf/10.1063/1.3457903?class=pdf
See Fig. 2 and Fig. 4
"""
geom = geom_loader("lib:ohch3f_anion_cs.xyz")
# OH group is a fragment
fragment_indices = [
(5, 6),
]
gamma = 100
calc = calc_cls(charge=-1, **calc_kwargs)
afir = AFIR(calc, fragment_indices, gamma, ignore_hydrogen=True)
geom.set_calculator(afir)
opt = RFOptimizer(geom, dump=True, trust_max=0.3)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == ref_cycle
# Broken C-Cl bond
c3d = geom.coords3d
assert np.linalg.norm(c3d[0] - c3d[4]) == pytest.approx(ccl_dist, abs=1e-4)
# Formed O-C bond
assert np.linalg.norm(c3d[0] - c3d[5]) == pytest.approx(oc_dist, abs=1e-4)
def test_artemisin():
# geom = geom_from_library("birkholz/artemisin.xyz", coord_type="redund")
geom = geom_from_library("birkholz/artemisin.xyz")
calc = XTB(charge=0, mult=1, pal=4)
# geom = geom_from_library("birkholz/zn_edta.xyz", coord_type="redund")
# geom = geom_from_library("birkholz/zn_edta.xyz")
# calc = XTB(charge=-2, mult=1, pal=4)
geom.set_calculator(calc)
opt_kwargs_base = {
"max_cycles": 50,
# "max_cycles": 15,
# "max_cycles": 8,
"thresh": "gau",
"trust_radius": 0.5,
"trust_update": True,
"hessian_update": "damped_bfgs",
# "hessian_init": "fischer",
"hessian_init": "calc",
"hessian_recalc": 1,
"line_search": True,
# "hybrid": True,
# "dump": True,
}
opt = RFOptimizer(geom, **opt_kwargs_base)
opt.run()
H = opt.H
w, v = np.linalg.eigh(H)
print(w)
def test_rfoptimizer(calc_cls, start, ref_cycle, ref_coords):
geom = calc_cls.get_geom(start)
print("@Using", calc_cls)
opt_kwargs = {
"thresh": "gau_tight",
"dump": True,
"overachieve_factor": 2.,
# "max_micro_cycles": 1,
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
# geom.calculator.plot_opt(opt, show=True)
# import matplotlib.pyplot as plt
# calc = geom.calculator
# calc.plot()
# coords = np.array(opt.coords)
# ax = calc.ax
# ax.plot(*coords.T[:2], "ro-")
# plt.show()
assert opt.is_converged
assert opt.cur_cycle == ref_cycle
ref_coords = np.array(ref_coords)
diff = ref_coords - geom.coords
diff_norm = np.linalg.norm(diff)
print(f"@\tnorm(diff)={diff_norm:.8f}")
assert diff_norm < 6e-5
print("@\tFinal coords", geom.coords)
def test_afir():
"""Example (R1) from
https://aip.scitation.org/doi/pdf/10.1063/1.3457903?class=pdf
See Fig. 2 and Fig. 4
"""
geom = geom_loader("lib:ohch3f_anion_cs.xyz")
# OH group is a fragment
fragment_indices = [
(5, 6),
]
calc = PySCF(basis="6-31g*", xc="b3lyp", pal=2, charge=-1)
gamma = 100
afir = AFIR(calc, fragment_indices, gamma)
geom.set_calculator(afir)
opt = RFOptimizer(geom, dump=True, trust_max=.3)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 47
# Broken C-Cl bond
c3d = geom.coords3d
assert np.linalg.norm(c3d[0] - c3d[4]) == pytest.approx(4.805665, abs=1e-4)
# Formed O-C bond
assert np.linalg.norm(c3d[0] - c3d[5]) == pytest.approx(2.674330, abs=1e-4)
def test_h2o_opt():
geom = geom_loader("lib:h2o.xyz", coord_type="redund")
calc = Dalton(basis="3-21G")
geom.set_calculator(calc)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 4
assert geom.energy == pytest.approx(-75.58595976)
def test_opt_dump():
geom = AnaPot().get_geom((1, 1, 0.))
calc = geom.calculator
opt_kwargs = {
"dump": True,
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
calc.plot_opt(opt) #, show=True)
def test_save_hessian(geom):
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
hess_fn = "_h2o_hessian.h5"
save_hessian(hess_fn, geom)
with h5py.File(hess_fn, "r") as handle:
hessian = handle["hessian"][:]
np.testing.assert_allclose(hessian, geom.cart_hessian)
def test_allene_opt():
geom = geom_loader("lib:08_allene.xyz", coord_type="redund")
calc = PySCF(basis="321g", pal=1)
geom.set_calculator(calc)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert opt.is_converged
# assert opt.cur_cycle == 7
assert geom.energy == pytest.approx(-115.21991342)
def test_rastrigin_minimum():
geom = Rastrigin().get_minima()[0]
# calc = geom.calculator
# calc.plot(show=True)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 0
assert geom.energy == pytest.approx(0.)
def test_opt_linear_dihedrals():
geom = geom_loader("lib:dihedral_gen_test.cjson", coord_type="redund")
geom.set_calculator(XTB())
opt_kwargs = {
"thresh": "gau_tight",
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 16
assert geom.energy == pytest.approx(-10.48063133)
def test_ar_cluster():
geom = geom_from_library("ar14cluster.xyz")
geom.set_calculator(LennardJones())
opt_kwargs = {
"max_cycles": 150,
"gediis": True,
"thresh": "gau_vtight",
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 93
def test_thermostat():
geom = geom_loader("lib:dynamics/10_water.xyz")
from pysisyphus.calculators.XTB import XTB
geom.set_calculator(XTB(pal=2))
opt = RFOptimizer(geom, thresh="gau_loose", max_cycles=25)
opt.run()
T = 298.15
seed = 20182503
v0 = get_mb_velocities_for_geom(geom, T, seed=seed).flatten()
# steps = 6000
steps = 250
dt = 0.5
md_kwargs = {
"v0": v0,
"steps": steps,
"dt": dt,
"remove_com_v": True,
"thermostat": "csvr",
"timecon": 25,
}
res = md(geom, **md_kwargs)
# assert dt * steps / 1000 == pytest.approx(res.t)
import matplotlib.pyplot as plt
E_tot = res.E_tot
E_tot -= E_tot.mean()
from pysisyphus.constants import AU2KJPERMOL
E_tot *= AU2KJPERMOL
T = res.T
fig, (ax0, ax1) = plt.subplots(nrows=2)
ax0.plot(E_tot)
ax0.axhline(E_tot.mean())
ax0.set_title("$\Delta$E$_{tot}$ / kJ mol⁻¹")
ax1.plot(T)
T_mean = T.mean()
print("T_mean", T_mean, "K")
ax1.axhline(T_mean)
ax1.set_title(f"T / K, avg. = {T_mean:.2f} K")
plt.tight_layout()
plt.show()
fig.savefig("md.pdf")
def test_opt_load_save_hessian(this_dir):
geom = geom_loader("lib:acetaldehyd.xyz", coord_type="redund")
calc = PySCF(basis="321g", pal=2)
geom.set_calculator(calc)
opt_kwargs = {
"hessian_recalc": 1,
"hessian_init": this_dir / "inp_hess_calc_cyc_0.h5",
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 2
assert geom.energy == pytest.approx(-152.05524620313963)
def test_cytosin_s1_opt():
geom = geom_loader("lib:cytosin.xyz", coord_type="redund")
calc_kwargs = {
"parameter": "mio-ext",
"root": 1,
"track": True,
}
calc = DFTBp(**calc_kwargs)
geom.set_calculator(calc)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert opt.is_converged
assert geom.energy == pytest.approx(-19.13130711)
def run_opt(line_search=False, hessian_recalc=None):
geom = AnaPot.get_geom((0.667, 1.609, 0.))
rfo_kwargs = {
"trust_radius": 0.75,
"thresh": "gau_tight",
"hessian_recalc": hessian_recalc,
"line_search": line_search,
# "hessian_init": "calc",
}
rfo = RFOptimizer(geom, **rfo_kwargs)
rfo.run()
conv = rfo.is_converged
cycs = rfo.cur_cycle
return (conv, cycs)
def test_guess_hessians(hessian_init, ref_cycle):
geom = geom_loader("lib:h2o2_hf_321g_opt.xyz", coord_type="redund")
geom.set_calculator(PySCF(basis="def2svp", pal=2))
print("@\tguess_hessian:", hessian_init)
opt_kwargs = {
"hessian_init": hessian_init,
"thresh": "gau_tight",
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
print("@\tcur_cycle:", opt.cur_cycle)
assert opt.is_converged
assert opt.cur_cycle == ref_cycle
assert geom.energy == pytest.approx(-150.65298169)
def test_dftbp_opt(slakos, this_dir):
geom = geom_loader("lib:h2o.xyz", coord_type="redund")
if slakos:
slakos = this_dir / slakos
calc_kwargs = {
"slakos": slakos,
"parameter": "mio-ext",
}
calc = DFTBp(**calc_kwargs)
geom.set_calculator(calc)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert opt.is_converged
assert geom.energy == pytest.approx(-4.07793793)
def test_acetaldehyde_opt():
"""
From https://doi.org/10.1016/S0166-1280(98)00475-8
"""
geom = geom_loader("lib:acetaldehyd_oniom.xyz", coord_type="redund")
real = set(range(len(geom.atoms)))
high = [4, 5, 6]
calcs = {
"real": {
"type": "g16",
"route": "hf sto-3g"
},
"high": {
"type": "g16",
"route": "b3lyp d95v"
}
}
for key, calc in calcs.items():
calc["pal"] = 2
calc["mult"] = 1
calc["charge"] = 0
models = {
"high": {
"inds": high,
"calc": "high",
},
}
oniom = ONIOM(calcs, models, geom, layers=None)
geom.set_calculator(oniom)
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
assert geom.energy == pytest.approx(-153.07526171)
hess_result = do_final_hessian(geom, save_hessian=False)
nus = hess_result.nus
print("wavenumbers / cm⁻¹:", nus)
assert nus[-1] == pytest.approx(3759.5872)
assert nus[-3] == pytest.approx(3560.9944)
def test_baker_gs_opt(name, geom, ref_energy, results_bag):
opt_kwargs = {
"thresh": "baker",
}
print(f"@Running {name}")
pal = min(os.cpu_count(), 4)
geom.set_calculator(PySCF(basis="sto3g", pal=pal))
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
results_bag.cycles = opt.cur_cycle + 1
results_bag.is_converged = opt.is_converged
results_bag.energy = geom.energy
results_bag.ref_energy = ref_energy
assert np.allclose(geom.energy, ref_energy)
return opt.cur_cycle + 1, opt.is_converged
def test_diis():
geom = AnaPot.get_geom((0.4333, 3.14286, 0.))
opt_kwargs = {
# "max_step": 0.5,
# "trust_max": 0.1,
"thresh": "gau_tight",
# "eins": True,
"zwei": True,
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
calc = geom.calculator
calc.plot()
ax = calc.ax
cs = np.array(opt.coords)
ax.plot(*cs.T[:2], "ro-")
plt.show()
def test_rfoptimizer():
kwargs = {
#"max_cycles": 10,
"trust_radius": 0.5,
"max_step": 0.5,
}
atoms = ("X", )
# http://www.applied-mathematics.net/optimization/optimizationIntro.html
coords = (0.7, -3.3, 0)
geom = Geometry(atoms, coords)
ap4 = AnaPot4()
geom.set_calculator(ap4)
opt = RFOptimizer(geom, **kwargs)
#opt = SteepestDescent(geom, **kwargs)
#opt = BFGS(geom, max_step=1.0)
opt.run()
rfop = RFOPlotter(ap4, opt, save=True, title=False)
rfop.plot()
plt.show()
def run_geom_opt(self, geom):
if self.calc_getter is not None:
calc = self.calc_getter(calc_number=self.calc_counter)
geom.set_calculator(calc)
opt_kwargs = {
"gdiis": False,
"thresh": "gau_loose",
"overachieve_factor": 2,
"max_cycles": 75,
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
opt_geom = geom if opt.is_converged else None
else:
calc = XTB(calc_number=self.calc_counter, **self.calc_kwargs)
opt_result = calc.run_opt(geom.atoms, geom.coords, keep=False)
opt_geom = opt_result.opt_geom
self.calc_counter += 1
return opt_geom
def test_pyscf_cytosin_td_opt():
calc_kwargs = {
"xc": "pbe0",
"method": "tddft",
# "method": "dft",
"basis": "def2SVP",
# "auxbasis": "weigend",
# "basis": "sto3g",
"pal": 4,
"nstates": 2,
"root": 1,
"track": True,
"ovlp_with": "adapt",
"ovlp_type": "tden",
}
pyscf_ = PySCF(**calc_kwargs)
geom = geom_from_library("cytosin.xyz", coord_type="redund")
geom.set_calculator(pyscf_)
opt = RFOptimizer(geom)
opt.run()
def test_three_frag_afir():
geom = geom_loader("lib:afir3test.xyz", coord_type="redund")
fragment_indices = [
(0, 1, 2),
(3, 4, 5, 6),
]
calc = XTB()
gamma = 150
afir = AFIR(calc, fragment_indices, gamma, ignore_hydrogen=False)
geom.set_calculator(afir)
opt = RFOptimizer(geom, dump=True, overachieve_factor=2)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 28
assert geom.energy == pytest.approx(-22.57975056)
c3d = geom.coords3d
assert np.linalg.norm(c3d[3] - c3d[9]) == pytest.approx(2.6099288505)
assert np.linalg.norm(c3d[2] - c3d[0]) == pytest.approx(3.96382029)
def run():
xyz_fn = "fluorethylene.xyz"
geom = geom_from_library(xyz_fn, coord_type="dlc")
freeze = ((0, 1), (2, 0, 3))
geom.internal.freeze_primitives(freeze)
# XTB
calc = XTB()
# Psi4
# from pysisyphus.calculators.Psi4 import Psi4
# calc = Psi4(method="hf", basis="sto-3g")
geom.set_calculator(calc)
opt_kwargs = {
# "max_cycles": 1,
"thresh": "gau_tight",
"trust_max": 0.3,
"trust_radius": 0.1,
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
assert opt.is_converged
def test_mb_velocities():
geom = geom_loader("lib:h2o.xyz")
geom.set_calculator(PySCF(basis="sto3g"))
# Preoptimization
opt = RFOptimizer(geom, thresh="gau_tight")
opt.run()
print()
T = 298.15
seed = 20182503
v0 = get_mb_velocities_for_geom(geom, T, seed=seed).flatten()
steps = 100
dt = 0.5
res = md(geom, v0, steps, dt)
assert dt * steps / 1000 == pytest.approx(res.t)
# import pdb; pdb.set_trace()
from pysisyphus.xyzloader import coords_to_trj
coords = res.coords
trj_fn = "md.trj"
atoms = geom.atoms
coords_to_trj(trj_fn, atoms, coords)
def run():
ovlp_types = "wf tden nto_org nto".split()
# ovlp_types = ("nto", )
ovlp_withs = "adapt first previous".split()
for i, (ovlp_type,
ovlp_with) in enumerate(it.product(ovlp_types, ovlp_withs)):
# ovlp_type = "wf"
# ovlp_with = "adapt"
print(
highlight_text(
f"i={i:02d}, ovlp_type={ovlp_type}, ovlp_with={ovlp_with}"))
geom = geom_from_library("cytosin.xyz", coord_type="redund")
calc = get_calc(ovlp_type, ovlp_with)
geom.set_calculator(calc)
opt = RFOptimizer(geom)
opt.run()
assert calc.root_flips[2] # == True
assert all([
flipped == False for i, flipped in enumerate(calc.root_flips)
if i != 2
])
assert calc.root == 2
assert opt.cur_cycle == 4
print()
def test_linesearch():
geom = AnaPot.get_geom((0.667, 1.609, 0.))
rfo_kwargs = {
# "trust_radius": 0.75,
"trust_radius": 0.75,
"thresh": "gau_tight",
# "max_cycles": 14,
# "hessian_recalc": 3,
# "hessian_init": "calc",
"line_search": True,
}
rfo = RFOptimizer(geom, **rfo_kwargs)
rfo.run()
coords = np.array(rfo.coords)
calc = geom.calculator
calc.plot()
ax = calc.ax
ax.plot(*coords.T[:2], "o-")
for i, coords in enumerate(coords):
ax.annotate(f"{i}", coords[:2])
plt.show()
def test_birkholz_set(xyz_fn, charge, mult, results_bag):
print(f"@{xyz_fn}")
coord_type = "redund"
# coord_type = "cart"
geom = geom_loader(f"lib:birkholz/{xyz_fn}", coord_type=coord_type)
calc = XTB(charge=charge, mult=mult, pal=4)
geom.set_calculator(calc)
opt_kwargs_base = {
"max_cycles": 150,
"thresh": "baker",
"overachieve_factor": 2,
"dump": True,
"adapt_step_func": True,
}
opt_kwargs = opt_kwargs_base.copy()
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
results_bag.cycles = opt.cur_cycle + 1
results_bag.is_converged = opt.is_converged
assert opt.is_converged
def test_openmolcas_s1_opt(this_dir):
geom = geom_loader(this_dir / "trans_butadien.xyz", coord_type="redund")
inporb_fn = this_dir / "butadien_vdzp.RasOrb"
kwargs = {
"basis": "ano-rcc-vdzp",
"inporb": inporb_fn,
"charge": 0,
"mult": 1,
"roots": 5,
"mdrlxroot": 2,
}
calc = OpenMolcas(**kwargs)
geom.set_calculator(calc)
opt_kwargs = {
"dump": True,
"thresh": "gau",
}
opt = RFOptimizer(geom, **opt_kwargs)
opt.run()
assert opt.is_converged
assert opt.cur_cycle == 6
assert geom.energy == pytest.approx(-154.8758862347)