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wfl.fit.modify_database package

wfl.fit.modify_database package#

Submodules#

wfl.fit.modify_database.gap_rss_set_config_sigmas_from_convex_hull module#

wfl.fit.modify_database.gap_rss_set_config_sigmas_from_convex_hull.modify(configs, overall_error_scale_factor=1.0, field_error_scale_factors=None, property_prefix='REF_')#
wfl.fit.modify_database.gap_rss_set_config_sigmas_from_convex_hull.piecewise_linear(x, vals)#

wfl.fit.modify_database.scale_orig module#

wfl.fit.modify_database.scale_orig.modify(configs, default_factor=1.0, property_factors={}, config_type_exclude=[])#

wfl.fit.modify_database.simple_factor_nonperiodic module#

wfl.fit.modify_database.simple_factor_nonperiodic.list_to_sigma_dict(sigma_values)#
wfl.fit.modify_database.simple_factor_nonperiodic.modify(configs, overall_error_scale_factor=1.0, field_error_scale_factors=None, property_prefix='REF_')#

Modify the database (atoms objects) with a simple scaling of default_sigma values

Parameters

  • configs (list(Atoms)) –

  • overall_error_scale_factor (float, default=1.0) –

  • field_error_scale_factors (dict) – this is a trick for now

  • property_prefix –

wfl.fit.modify_database.simple_factor_nonperiodic.modify_cell(at: Atoms, extra_space=20.0)#
wfl.fit.modify_database.simple_factor_nonperiodic.modify_with_factor(at, factor=1.0, energy_sigma=None, force_sigma=None, virial_sigma=None, hessian_sigma=None, property_prefix=None)#

Set the kernel regularisation values and remove results if given but not

Parameters

  • at –

  • factor (float, default=1.0) –

  • energy_sigma –

  • force_sigma –

  • virial_sigma –

  • hessian_sigma –

  • property_prefix –

Return type

None

Module contents#

Contents