def test_run_single_reaxff(db_test_app, get_structure,
pyrite_potential_reaxff_lowtol, data_regression):
# type: (AiidaTestApp) -> None
code = db_test_app.get_or_create_code("gulp.single")
builder = code.get_builder()
builder._update({
"metadata": {
"options": {
"withmpi": False,
"resources": {
"num_machines": 1,
"num_mpiprocs_per_machine": 1
},
"max_wallclock_seconds": 30,
}
}
})
builder.structure = get_structure("pyrite")
builder.potential = pyrite_potential_reaxff_lowtol
calc_node = run_get_node(builder).node
db_test_app.check_calculation(calc_node, ["results"])
result = recursive_round(calc_node.outputs.results.get_dict(), 6)
for key in [
"parser_version",
"peak_dynamic_memory_mb",
"opt_time_second",
"total_time_second",
]:
result.pop(key, None)
data_regression.check(result)
def test_parse_failed_optimize(data_regression):
# type: (AiidaTestApp) -> None
with open_resource_text("gulp", "failed", "opt_step_limit.gout") as handle:
data, exit_code = parse_file(handle, "test_class")
result = recursive_round(data, 6)
result.pop("parser_version")
data_regression.check(result)
assert exit_code == "ERROR_OPTIMISE_MAX_ATTEMPTS"
def test_parse_polymer_opt(data_regression):
# type: (AiidaTestApp) -> None
""" this is a surface calculation """
with open_resource_text("gulp", "s2_polymer_opt", "main.gout") as handle:
data, exit_code = parse_file(handle, "test_class")
result = recursive_round(data, 6)
result.pop("parser_version")
data_regression.check(result)
assert exit_code is None
def test_parse_non_primitive_opt(data_regression):
# type: (AiidaTestApp) -> None
with open_resource_text("gulp", "non_primitive_opt",
"main.gout") as handle:
data, exit_code = parse_file(handle, "test_class")
result = recursive_round(data, 6)
result.pop("parser_version")
data_regression.check(result)
assert exit_code is None
def test_run_optimize_lj_with_symm(db_test_app, get_structure,
pyrite_potential_lj, data_regression):
# type: (AiidaTestApp) -> None
from aiida.engine import run_get_node
structure = get_structure("pyrite")
symmetry = DataFactory("gulp.symmetry")(
data=compute_symmetry_dict(structure, 0.01, None))
parameters = db_test_app.get_data_node(
"dict",
dict={
"minimize": {
"style": "cg",
"max_iterations": 100
},
"relax": {
"type": "conp"
},
},
)
code = db_test_app.get_or_create_code("gulp.optimize")
builder = code.get_builder()
builder._update({
"metadata": {
"options": {
"withmpi": False,
"resources": {
"num_machines": 1,
"num_mpiprocs_per_machine": 1
},
"max_wallclock_seconds": 30,
}
}
})
builder.structure = structure
builder.potential = pyrite_potential_lj
builder.parameters = parameters
builder.symmetry = symmetry
calc_node = run_get_node(builder).node
db_test_app.check_calculation(calc_node,
["results", "structure", "retrieved"])
result = recursive_round(calc_node.outputs.results.get_dict(), 6)
for key in [
"parser_version",
"peak_dynamic_memory_mb",
"opt_time_second",
"total_time_second",
]:
result.pop(key, None)
data_regression.check(result)
def test_run_reaxff_fes(db_test_app, get_structure, data_regression):
"""Test submitting a calculation."""
code = db_test_app.get_or_create_code("gulp.fitting")
builder = code.get_builder()
builder.metadata = db_test_app.get_default_metadata()
potential_cls = DataFactory("gulp.potential")
pot_data = read_lammps_format(
read_resource_text("gulp", "potentials",
"FeCrOSCH.reaxff").splitlines())
pot_data = filter_by_species(pot_data, ["Fe core", "S core"])
builder.settings = {"observables": {"energy": {}}}
builder.potential = potential_cls(
"reaxff",
pot_data,
fitting_data={
"species": ["Fe core", "S core"],
"global": ["reaxff0_boc1", "reaxff0_boc2"],
},
)
builder.structures = {
"pyrite": get_structure("pyrite"),
"marcasite": get_structure("marcasite"),
"zincblende": get_structure("zincblende"),
}
builder.observables = {
"pyrite": Dict(dict={
"energy": -1,
"energy_units": "eV"
}),
"marcasite": Dict(dict={
"energy": -1,
"energy_units": "eV"
}),
"zincblende": Dict(dict={
"energy": 1,
"energy_units": "eV"
}),
}
builder.metadata["options"]["allow_create_potential_fail"] = True
calc_node = run_get_node(builder).node
db_test_app.check_calculation(calc_node, ["results"])
result = recursive_round(calc_node.outputs.results.get_dict(), 6)
for key in [
"parser_version",
"peak_dynamic_memory_mb",
"opt_time_second",
"total_time_second",
]:
result.pop(key, None)
data_regression.check(result)
def test_parse_single_lj_pyrite(data_regression):
# type: (AiidaTestApp) -> None
with open_resource_text("gulp", "single_lj_pyrite", "main.gout") as handle:
data, exit_code = parse_file(handle,
"test_class",
single_point_only=True)
result = recursive_round(data, 6)
result.pop("parser_version")
data_regression.check(result)
assert exit_code is None
def test_run_lj_fes(db_test_app, get_structure, data_regression):
"""Test running a calculation."""
code = db_test_app.get_or_create_code("gulp.fitting")
builder = code.get_builder()
builder.metadata = db_test_app.get_default_metadata()
potential_cls = DataFactory("gulp.potential")
builder.potential = potential_cls(
"lj",
{
"species": ["Fe core", "S core"],
"2body": {
"0-0": {
"lj_A": 1.0,
"lj_B": 1.0,
"lj_rmax": 12.0
},
"0-1": {
"lj_A": 1.0,
"lj_B": 1.0,
"lj_rmax": 12.0
},
"1-1": {
"lj_A": 1.0,
"lj_B": 1.0,
"lj_rmax": 12.0
},
},
},
fitting_data={
"species": ["Fe core", "S core"],
"2body": {
"0-0": ["lj_A", "lj_B"],
"0-1": [],
"1-1": []
},
},
)
builder.settings = {"observables": {"energy": {}}}
builder.structures = {
"pyrite": get_structure("pyrite"),
"marcasite": get_structure("marcasite"),
"zincblende": get_structure("zincblende"),
}
builder.observables = {
"pyrite": Dict(dict={
"energy": -1,
"energy_units": "eV"
}),
"marcasite": Dict(dict={
"energy": -1,
"energy_units": "eV"
}),
"zincblende": Dict(dict={
"energy": 1,
"energy_units": "eV"
}),
}
calc_node = run_get_node(builder).node
db_test_app.check_calculation(calc_node, ["results", "potential"])
result = recursive_round(calc_node.outputs.results.get_dict(), 6)
for key in [
"parser_version",
"peak_dynamic_memory_mb",
"opt_time_second",
"total_time_second",
]:
result.pop(key, None)
data_regression.check({
"results": result,
"potential": calc_node.outputs.potential.attributes
})