schrodinger.application.matsci.ml_prediction_gui_utils module¶

GUI utilities and widgets for managing ML prediction models

schrodinger.application.matsci.ml_prediction_gui_utils.load_model_categories()¶

Load and parse the model categories from the file.

Returns:: A nested dictionary whose keys are the model names and values are subdictionaries of material categories. The categories are keys and values are booleans indicating whether the model belongs to the corresponding category.
Return type:: dict[dict]

schrodinger.application.matsci.ml_prediction_gui_utils.get_model_categories()¶

Creates a cached dictionary of model categories.

Returns:: A nested dictionary whose keys are the model names and values are subdictionaries of material categories.
Return type:: dict[dict]

schrodinger.application.matsci.ml_prediction_gui_utils.get_material_categories()¶

Creates a cached set of material categories.

Returns:: Material category names.
Return type:: set[str]

class schrodinger.application.matsci.ml_prediction_gui_utils.PropertyEdit(*args, validator=None, **kwargs)¶

Bases: EditWithFocusOutEvent

Line edit for property inputs

__init__(*args, validator=None, **kwargs)¶

Create an instance

Parameters:: validator (QtGui.QValidator) – Validator for the line edit

class schrodinger.application.matsci.ml_prediction_gui_utils.PredictFrame(*, model, default_val, units_dict, input_name, layout, batch_mode_signal=None, default_step_start_end=None)¶

Bases: SFrame

Frame for specifying parameters for property prediction. Has interval and single-value modes.

AT_TEXT = 'at'¶

EVERY_TEXT = 'every'¶

__init__(*, model, default_val, units_dict, input_name, layout, batch_mode_signal=None, default_step_start_end=None)¶

Create an instance

Parameters:

model (BaseModelInfo) – The model for this property
default_val (float) – The default value for the property for the single-value mode
units_dict (dict) – Dict mapping unit names to their conversion function to the unit that the driver uses
input_name (str) – The name of the input parameter, used in tooltips
layout (QLayout) – The layout to add this frame to
batch_mode_signal (QtCore.pyqtSignal) – The signal emitted when batch mode changes. Should be passed when default_step_start_end is passed.
default_step_start_end (dict) – Dict mapping units to their default step, min, and max for the interval mode

unitsChanged()¶: Update the default step, min and max range when units change

atEveryChanged()¶: React to a switch between interval and single-value mode

getInputParams(original_units)¶

Get input parameters required to predict the property

Parameters:: original_units (bool) – Whether the params should be in original units
Returns:: values of temperature or pressure
Return type:: list

setEveryEnabled(is_batch)¶

Enabled or disable the “every” option in the combobox based on the panel run mode

Parameters:: is_batch (bool) – Whether the panel is in batch mode

setInputParamChangedSignal(func)¶

Connect the signals required for when in parameters change

Parameters:: func (callable) – The slot to connect the signals to

reset()¶: Reset the frame

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseSolventFrame(layout, add_combo_label=True, solvent_file=None)¶

Bases: SFrame

Base class for solvent frame

PREFGROUP = None¶

SOLVENT_PREF = 'solvent_name'¶

SOLVENT_FILE = None¶

__init__(layout, add_combo_label=True, solvent_file=None)¶

Create an instance

Parameters:

layout (QBoxLayout) – The layout to place this frame in
add_combo_label (bool) – Whether to add a label to the solvent combobox
solvent_file (str) – The file containing name of all the solvents

SOLVENTS = {}¶

updateSolventPref(solvent)¶

Update the stored solvent preference to the new solvent

Parameters:: solvent (str) – The new default solvent

getFlags()¶

Get command line flags based on widget settings

Return type:: list
Returns:: Command line flags

reset()¶: Reset this widget

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoSolventFrame(layout, solvent_file='opto_training_solvents.csv')¶

Bases: BaseSolventFrame

Allow the user to choose between solid and solution states for opto-electronics models

SOLVENTS = {}¶

PURE = 'Thin film'¶

SOLVENT = 'Solution in:'¶

PREFGROUP = 'OptoelectronicsMLPredSolventFrame'¶

__init__(layout, solvent_file='opto_training_solvents.csv')¶

Create a SolventFrame instance

Parameters:

layout (QBoxLayout) – The layout to place this frame in
solvent_file (str) – File containing name of the all solvent for which opto-electronics formulation model is trained.

getFlags()¶

Get command line flags based on widget settings

Return type:: list
Returns:: Command line flags

reset()¶: Reset this widget

class schrodinger.application.matsci.ml_prediction_gui_utils.NonAqSolventFrame(layout, add_combo_label=True, solvent_file=None)¶

Bases: BaseSolventFrame

Allow the user to choose the solvent for Non-aqueous solvent

SOLVENTS = {}¶

PREFGROUP = 'NonAqSolventMLPredSolventFrame'¶

class schrodinger.application.matsci.ml_prediction_gui_utils.ModelFrame(base_dir, url, layout=None)¶

Bases: SFrame, MessageBoxMixin

Contains widgets and functionality for downloading and switching models

__init__(base_dir, url, layout=None)¶

Create an instance

Parameters:

base_dir (str) – The subdir name for the default model
url (str) – The url to download the custom model from
layout (QBoxLayout) – The layout to add the frame to

modelExists()¶

Get whether the directory for the custom model exists

Return type:: bool
Returns:: True if custom model directory exists, False if not

showHideWidgets()¶: Show and hide widgets based on whether a custom model exists

downloadModel()¶: Download the custom model and unzip it to a directory

getModelDir()¶

Get the current model directory, default or custom

Return type:: str
Returns:: The model directory

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseModelInfo(panel)¶

Bases: object

Create a base mixin class for different models

CHEMICAL_SPACE_ROW_ID = 5¶

HEADER = None¶

CAN_HAVE_MULTIPLE_PARAMS = False¶

RANGE_ERROR = None¶

UNCERTAINTY_TOOLTIP = '90% confidence interval'¶

PIXMAP_SIZE = 310¶

PARITY_PLOT_FILE = 'train_test_parity.png'¶

GROUP = ''¶

DATA = None¶

BASE_DIR = ''¶

ADDITIONAL_FEATURE_DICT = {}¶

UNIT_LABEL = None¶

__init__(panel)¶

Instantiate BaseModelInfo class

Parameters:: panel (class) – Panel

loadData()¶: Load the model data from files

readInfoDict()¶: Read the model info dict from json

addExtraWidgets()¶: Add additional widgets for child classes

addModelFrame()¶: Add the custom model frame if model info contains a custom model link

addModelInfoTable()¶: Add ML model info to the layout

updateModelInfoTable()¶: Update model info table

addParityPlot()¶: Create parity plot for ML model

addResultGroup()¶: Add ML predicted result to the layout

resetResultsPlot()¶: Reset the results plot. Should be implemented for child classes that can calculate properties at intervals

getResultHeader()¶

Get Result table header for polymer Tg prediction

Return type:: str
Returns:: Result table header for Tg prediction model

validatePredictRange()¶

Validate the range to predict property

Return type:: bool or (bool, str)
Returns:: The bool is True if everything is OK, False if not. str is given if a dialog should pop up to show the given message. If bool is True, str will post as Question dialog.

getInputParams(original_units=False)¶

Get input parameters required to predict the property

Parameters:: original_units (bool) – Whether the params should be in original units
Returns:: values of temperature, pressure, etc.
Return type:: list

setInputParamChangedSignal()¶: Set signal for input param changed

getModelInfoDict()¶

Get model info dictionary directory

Returns:: copy of model info dict
Return type:: dict

getModelDir()¶

Get Model directory

Returns:: name of the model directory
Return type:: str

getFlags()¶

Get flags for the model

Return type:: list
Returns:: list of flags for the model

resetMethod()¶: Reset widgets

class schrodinger.application.matsci.ml_prediction_gui_utils.ModelWithInput(panel)¶

Bases: BaseModelInfo

Base class for models that take an input parameter

getInputParams(original_units=False)¶

Get input parameters required to predict the property

Parameters:: original_units (bool) – Whether the params should be in original units
Returns:: values of input parameter
Return type:: list

resetMethod()¶: Reset widgets

setInputParamChangedSignal()¶: Set signal for input param changed

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseOptoelectronicsModel(panel)¶

Bases: BaseModelInfo

Base model for optoelectronics models

addExtraWidgets()¶: See parent class for documentation

getFlags()¶

Get command line flags based on widget settings

Return type:: list
Returns:: Command line flags

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoAbsorptionPeakModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the absorption Lmax

HEADER = 'Peak'¶

DATA = ModelData(group='optoelectronics', name='Absorption peak position', flag='-absorption_lmax_predict', prop='r_matsci_Absorption_Lmax_(nm)', directory='opto_absorption_lambda_max', skip_standardization=False)¶

BASE_DIR = 'opto_absorption_lambda_max'¶

GROUP = 'Absorption peak position'¶

UNIT_LABEL = 'nanometer'¶

GENOPT_DEFAULTS = (550, 10, 0.1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoAbsorptionBWModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the absorption bandwidth

HEADER = 'Bandwidth'¶

DATA = ModelData(group='optoelectronics', name='Absorption bandwidth', flag='-absorption_fwhm_predict', prop='r_matsci_Absorption_Bandwidth_(cm-1)', directory='opto_absorption_FWHM', skip_standardization=False)¶

BASE_DIR = 'opto_absorption_FWHM'¶

GROUP = 'Absorption bandwidth'¶

UNIT_LABEL = 'centimeters⁻¹'¶

GENOPT_DEFAULTS = (100, 10, 0.1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoExtinctionModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the absorption extinction coefficient

HEADER = 'log(ε)'¶

DATA = ModelData(group='optoelectronics', name='Extinction coefficient', flag='-extinction_predict', prop='r_matsci_Extinction_Coefficient_(log_mol-1_dm3_cm-1)', directory='opto_log_e', skip_standardization=False)¶

BASE_DIR = 'opto_log_e'¶

GROUP = 'Extinction coefficient'¶

UNIT_LABEL = 'log10(mol⁻¹dm³cm⁻¹)'¶

GENOPT_DEFAULTS = (5.0, 0.5, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoEmissionPeakModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the emission Emax

HEADER = 'Peak'¶

DATA = ModelData(group='optoelectronics', name='Emission peak position', flag='-emission_lmax_predict', prop='r_matsci_Emission_Emax_(nm)', directory='opto_emission_lambda_max', skip_standardization=False)¶

BASE_DIR = 'opto_emission_lambda_max'¶

GROUP = 'Emission peak position'¶

UNIT_LABEL = 'nanometer'¶

GENOPT_DEFAULTS = (550, 10, 0.1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoEmissionBWModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the emission bandwidth

HEADER = 'Bandwidth'¶

DATA = ModelData(group='optoelectronics', name='Emission bandwidth', flag='-emission_fwhm_predict', prop='r_matsci_Emission_Bandwidth_(cm-1)', directory='opto_emission_FWHM', skip_standardization=False)¶

BASE_DIR = 'opto_emission_FWHM'¶

GROUP = 'Emission bandwidth'¶

UNIT_LABEL = 'centimeters⁻¹'¶

GENOPT_DEFAULTS = (100, 10, 0.1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoEmissionLifetimeModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the emission lifetime

HEADER = 'log(Lifetime)'¶

DATA = ModelData(group='optoelectronics', name='Emission lifetime', flag='-emission_lifetime_predict', prop='r_matsci_Emission_Lifetime_(log_ns)', directory='opto_log_lifetime', skip_standardization=False)¶

BASE_DIR = 'opto_log_lifetime'¶

GROUP = 'Emission lifetime'¶

UNIT_LABEL = 'log10(Nanoseconds)'¶

GENOPT_DEFAULTS = (2.0, 0.2, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OptoPLQYModel(panel)¶

Bases: BaseOptoelectronicsModel

Model to predict the photoluminescence quantum yield

HEADER = 'PLQY'¶

DATA = ModelData(group='optoelectronics', name='Photoluminescence quantum yield', flag='-plqy_predict', prop='r_matsci_Photoluminescence_Quantum_Yield', directory='opto_quantum_yield', skip_standardization=False)¶

BASE_DIR = 'opto_quantum_yield'¶

GROUP = 'Photoluminescence quantum yield'¶

UNIT_LABEL = '%'¶

GENOPT_DEFAULTS = (0.9, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OrganicDensityModel(panel)¶

Bases: BaseModelInfo

Model for organic density prediction

GROUP = 'Density of molecular liquids'¶

DATA = ModelData(group='single_molecule', name='Density', flag='-density_predict', prop='r_matsci_Density_(g/cm3)_at_%.2f_K', directory='organic_density', skip_standardization=False)¶

BASE_DIR = 'organic_density'¶

HEADER = 'Density'¶

UNIT_LABEL = 'gram/centimeter³'¶

GENOPT_DEFAULTS = (1.0, 0.1, 1)¶

ADDITIONAL_FEATURE_DICT = {'additional_feature_0': {'name': '~Temperature (K)', 'value': 293.15}}¶

getFlags()¶

Get the flags for the model

Returns:: flags for the model
Return type:: list

class schrodinger.application.matsci.ml_prediction_gui_utils.NonAqueousSolubilityModel(panel)¶

Bases: ModelWithInput

Model for non-aqueous solubility prediction

GROUP = 'Non-aqueous solubility'¶

DATA = ModelData(group='formulation', name='Non-aqueous solubility', flag='-non_aqua_solubility_predict', prop='r_matsci_NonAq_Solubility_(log_mol_frac)_at_%s_K', directory='nonaqueous_solubility', skip_standardization=False)¶

BASE_DIR = 'nonaqueous_solubility'¶

HEADER = 'Non-aqueous\nSolubility'¶

UNIT_LABEL = 'log10(mole fraction)'¶

GENOPT_DEFAULTS = (0.0, 0.1, 1)¶

RANGE_ERROR = 'Temperature range for predicting vapor pressure is %s-%s K'¶

ADDITIONAL_FEATURE_DICT = {'additional_feature_0': {'name': '~Temperature (K)', 'value': 298.15}}¶

addExtraWidgets()¶: Add additional widgets for this model

getFlags(param_idx=0)¶

Create flags for non-aqueous solubility prediction

Parameters:: param_idx (int) – The index of the input parameter to use. Can be used to get multiple lists of flags for multiple input parameters
Return type:: list
Returns:: List of strings

class schrodinger.application.matsci.ml_prediction_gui_utils.AqueousSolubilityModel(panel)¶

Bases: BaseModelInfo

Model for aqueous solubility prediction

DATA = ModelData(group='single_molecule', name='Aqueous solubility', flag='-aqua_solubility_predict', prop='r_matsci_Aqueous_Solubility_(log_mol/L)', directory='aqueous_solubility', skip_standardization=False)¶

GROUP = 'Aqueous solubility'¶

BASE_DIR = 'aqueous_solubility'¶

HEADER = 'Aqueous\nSolubility'¶

UNIT_LABEL = 'log10(mol/liter)'¶

GENOPT_DEFAULTS = (0.0, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.MeltingPointModel(panel)¶

Bases: BaseModelInfo

Model for Melting point prediction

DATA = ModelData(group='single_molecule', name='Melting point', flag='-melting_point_predict', prop='r_matsci_Melting_Point_(K)', directory='melting_temp', skip_standardization=False)¶

GROUP = 'Melting point'¶

BASE_DIR = 'melting_temp'¶

HEADER = 'Melting\nPoint'¶

UNIT_LABEL = 'Kelvin'¶

GENOPT_DEFAULTS = (300, 20, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.ElectronReorganizationEnergyModel(panel)¶

Bases: BaseModelInfo

Model for electron reorganization energy prediction

DATA = ModelData(group='oled', name='Electron reorganization energy', flag='-electron_reorg_predict', prop='r_matsci_Electron_Reorganization_(eV)', directory='oled_electron_reorg', skip_standardization=True)¶

GROUP = 'Electron reorganization energy'¶

BASE_DIR = 'oled_electron_reorg'¶

HEADER = 'Electron\nReorganization\nEnergy'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (0.3, 0.05, 10)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.HoleReorganizationEnergyModel(panel)¶

Bases: BaseModelInfo

Model for hole reorganization energy prediction

DATA = ModelData(group='oled', name='Hole reorganization energy', flag='-hole_reorg_predict', prop='r_matsci_Hole_Reorganization_(eV)', directory='oled_hole_reorg', skip_standardization=True)¶

GROUP = 'Hole reorganization energy'¶

BASE_DIR = 'oled_hole_reorg'¶

HEADER = 'Hole\nReorganization\nEnergy'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (0.3, 0.05, 10)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.TripletEnergyModel(panel)¶

Bases: BaseModelInfo

Model for triplet energy prediction

DATA = ModelData(group='oled', name='Triplet Energy (E(S0T1))', flag='-triplet_energy_predict', prop='r_matsci_Triplet_Energy_(eV)', directory='oled_triplet_energy', skip_standardization=True)¶

GROUP = 'Triplet Energy (E(S0T1))'¶

BASE_DIR = 'oled_triplet_energy'¶

HEADER = 'Triplet Energy'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (2.5, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.ScaledHOMOModel(panel)¶

Bases: BaseModelInfo

Model for HOMO prediction

DATA = ModelData(group='oled', name='Scaled HOMO', flag='-scaled_homo_predict', prop='r_matsci_scaled_HOMO_Energy_(eV)', directory='oled_scaled_homo', skip_standardization=True)¶

GROUP = 'Scaled HOMO'¶

BASE_DIR = 'oled_scaled_homo'¶

HEADER = 'Scaled HOMO'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (-5.0, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.ScaledLUMOModel(panel)¶

Bases: BaseModelInfo

Model for LUMO prediction

DATA = ModelData(group='oled', name='Scaled LUMO', flag='-scaled_lumo_predict', prop='r_matsci_scaled_LUMO_Energy_(eV)', directory='oled_scaled_lumo', skip_standardization=True)¶

GROUP = 'Scaled LUMO'¶

BASE_DIR = 'oled_scaled_lumo'¶

HEADER = 'Scaled LUMO'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (-3.0, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.TripletReorganizationEnergyModel(panel)¶

Bases: BaseModelInfo

Model for LUMO prediction

DATA = ModelData(group='oled', name='Triplet reorganization energy', flag='-triplet_reorg_predict', prop='r_matsci_Triplet_Reorganization_Energy_(eV)', directory='oled_triplet_reorg', skip_standardization=True)¶

GROUP = 'Triplet reorganization energy'¶

BASE_DIR = 'oled_triplet_reorg'¶

HEADER = 'Triplet\nReorganization\nEnergy'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (0.3, 0.05, 10)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.SingletTripletEnergyGapModel(panel)¶

Bases: BaseModelInfo

Model for singlet-triplet energy gap prediction

DATA = ModelData(group='oled', name='Singlet-triplet energy gap', flag='-de_s1_t1_gap_predict', prop='r_matsci_Singlet_Triplet_Energy_Gap_(eV)', directory='oled_singlet_triplet_gap', skip_standardization=True)¶

GROUP = 'Singlet-triplet energy gap'¶

BASE_DIR = 'oled_singlet_triplet_gap'¶

HEADER = 'ΔE(S1-T1)'¶

UNIT_LABEL = 'electron volt'¶

GENOPT_DEFAULTS = (0.1, 0.05, 10)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseBoilingPointModel(panel)¶

Bases: ModelWithInput

Base model class for boiling point calculation

STEP_MIN_MAX = None¶

HEADER = 'Boiling Point'¶

RANGE_ERROR = 'Pressure range for predicting boiling/sublimation point is %s-%s Torr'¶

UNIT_LABEL = 'Kelvin'¶

ADDITIONAL_FEATURE_DICT = {'additional_feature_0': {'name': '~log (Pressure (atm))', 'value': 2.88081359228}}¶

addExtraWidgets()¶: Add additional widgets for Boiling point class

getInputAndUnit()¶

Get the name of the input parameter and its unit

Return type:: str
Returns:: The input name and unit

getResultHeader()¶

Get result table header for the model

Return type:: str
Returns:: Result table header

getFlags(param_idx=0)¶

Create flags for boiling point prediction

Parameters:: param_idx (int) – The index of the input parameter to use. Can be used to get multiple lists of flags for multiple input parameters
Returns:: List of strings
Return type:: List

class schrodinger.application.matsci.ml_prediction_gui_utils.OrgBoilingPointModel(panel)¶

Bases: BaseBoilingPointModel

Boiling point model class for organics

DATA = ModelData(group='single_molecule', name='Boiling point', flag='-semi_organic_bp_predict', prop='r_matsci_Evaporation_temperature_(K)_at_%.2f_Torr', directory='semi_organic_bp', skip_standardization=False)¶

GROUP = 'Boiling point of organic molecules'¶

BASE_DIR = 'semi_organic_bp'¶

GENOPT_DEFAULTS = (400, 20, 1)¶

STEP_MIN_MAX = {'Torr': (4000, 1, 20000), 'atm': (5, 1, 25), 'bar': (5, 1, 25)}¶

CAN_HAVE_MULTIPLE_PARAMS = True¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OrgmetalBoilingPointModel(panel)¶

Bases: BaseBoilingPointModel

Boiling point model class for organometallics

DATA = ModelData(group='single_molecule', name='Boiling point', flag='-semi_organometal_bp_predict', prop='r_matsci_Evaporation_temperature_(K)_at_%.2f_Torr', directory='semi_organometallic_bp', skip_standardization=True)¶

GROUP = 'Boiling point of organometallic molecules'¶

BASE_DIR = 'semi_organometallic_bp'¶

GENOPT_DEFAULTS = (400, 20, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseVaporPressureModel(panel)¶

Bases: ModelWithInput

Base model class for vapor pressure calculation

HEADER = 'Vapor Pressure'¶

UNIT_LABEL = 'Torr'¶

RANGE_ERROR = 'Temperature range for predicting vapor pressure is %s-%s K'¶

ADDITIONAL_FEATURE_DICT = {'additional_feature_0': {'name': '~1 / Temperature (K)', 'value': 0.00335401643}}¶

addExtraWidgets()¶: Add additional widgets for this model

getResultHeader()¶

Get result table header for the model

Return type:: str
Returns:: Result table header

getFlags(param_idx=0)¶

Create flags for vapor pressure prediction

Parameters:: param_idx (int) – The index of the input parameter to use. Can be used to get multiple lists of flags for multiple input parameters
Returns:: List of strings
Return type:: List

class schrodinger.application.matsci.ml_prediction_gui_utils.OrgVaporPressureModel(panel)¶

Bases: BaseVaporPressureModel

Vapor pressure model class for organics

DATA = ModelData(group='single_molecule', name='Vapor pressure', flag='-semi_organic_vp_predict', prop='r_matsci_Vapor_pressure_(Torr)_at_%s_K', directory='semi_organic_vp', skip_standardization=False)¶

GROUP = 'Vapor pressure of organic molecules'¶

BASE_DIR = 'semi_organic_vp'¶

GENOPT_DEFAULTS = (100, 10, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OrganicViscosityModel(panel)¶

Bases: ModelWithInput

Viscosity model for organics

GROUP = 'Viscosity of organic liquids'¶

DATA = ModelData(group='single_molecule', name='Viscosity', flag='-viscosity_predict', prop='r_matsci_Viscosity_(cP)_at_%.2f_K', directory='organic_viscosity', skip_standardization=False)¶

BASE_DIR = 'organic_viscosity'¶

HEADER = 'Viscosity'¶

UNIT_LABEL = 'Centipoise'¶

GENOPT_DEFAULTS = (1.0, 0.1, 1)¶

RANGE_ERROR = 'Temperature range for predicting viscosity is %s-%s K'¶

ADDITIONAL_FEATURE_DICT = {'additional_feature_0': {'name': '~1 / Temperature (K)', 'value': 0.00335401643}}¶

addExtraWidgets()¶: Add additional widgets for this model

getFlags(param_idx=0)¶

Create flags for viscosity prediction

Parameters:: param_idx (int) – The index of the input parameter to use. Can be used to get multiple lists of flags for multiple input parameters
Return type:: list
Returns:: List of strings

class schrodinger.application.matsci.ml_prediction_gui_utils.ReductionPotentialModel(panel)¶

Bases: BaseModelInfo

Class to create layout for reduction potential model

DATA = ModelData(group='single_molecule', name='Reduction potential', flag='-reduction_potential_predict', prop='r_matsci_Reduction_Potential_(V)', directory='reduction_potential', skip_standardization=False)¶

GROUP = 'Reduction potential'¶

HEADER = 'Reduction\nPotential'¶

BASE_DIR = 'reduction_potential'¶

UNIT_LABEL = 'volt'¶

GENOPT_DEFAULTS = (-1.0, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.OxidationPotentialModel(panel)¶

Bases: BaseModelInfo

Class to create layout for oxidation potential model

DATA = ModelData(group='single_molecule', name='Oxidation potential', flag='-oxidation_potential_predict', prop='r_matsci_Oxidation_Potential_(V)', directory='oxidation_potential', skip_standardization=False)¶

GROUP = 'Oxidation potential'¶

HEADER = 'Oxidation\nPotential'¶

BASE_DIR = 'oxidation_potential'¶

UNIT_LABEL = 'volt'¶

GENOPT_DEFAULTS = (1.0, 0.1, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.PolymerTgModel(panel)¶

Bases: BaseModelInfo

Create layout for Tg prediction of polymers

DATA = ModelData(group='polymer', name='Polymer glass transition temperature', flag='-tg_predict', prop='r_matsci_Polymer_Tg_(K)', directory='polymer_tg', skip_standardization=False)¶

GROUP = 'Polymer glass transition temperature'¶

HEADER = 'Tg'¶

BASE_DIR = 'polymer_tg'¶

UNIT_LABEL = 'Kelvin'¶

GENOPT_DEFAULTS = (150.0, 10.0, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.BaseDielectricModel(panel)¶

Bases: BaseModelInfo

Contains common functionality used in Polymer Dk and Polymer DF models

RANGE_ERROR = 'Valid frequency range for predicting dielectric values is %s-%s Hz'¶

MODEL_FLAG = None¶

UNCERTAINTY_TOOLTIP = 'Gaussian Process Regression std. dev.'¶

addExtraWidgets()¶: Add additional widgets for dielectric models

resetMethod()¶: Reset method for base dielectric calculation class

getInputParams()¶

Get input frequency required to predict the property

Returns:: values of frequency
Return type:: list

getFlags(param_idx=0)¶

Create flags for polymer dielectric properties prediction

Parameters:: param_idx (int) – The index of the input parameter to use. Can be used to get multiple lists of flags for multiple input parameters
Returns:: List of command line flags
Return type:: List

setInputParamChangedSignal()¶: Set signal for input param changed

class schrodinger.application.matsci.ml_prediction_gui_utils.PolymerDkModel(panel)¶

Bases: BaseDielectricModel

Create layout for dielectric relative permittivity prediction class

DATA = ModelData(group='polymer', name='Dielectric constant', flag='-dk_predict', prop='r_matsci_Polymer_Dk_at_%.2f_Hz', directory='polymer_dk', skip_standardization=False)¶

GROUP = 'Polymer dielectric constant'¶

BASE_DIR = 'polymer_dk'¶

HEADER = 'Dk'¶

GENOPT_DEFAULTS = (3.0, 0.3, 1)¶

class schrodinger.application.matsci.ml_prediction_gui_utils.PolymerDfModel(panel)¶

Bases: BaseDielectricModel

Create dissipation loss prediction class

DATA = ModelData(group='polymer', name='Dielectric loss', flag='-df_predict', prop='r_matsci_Polymer_Df_at_%.2f_Hz', directory='polymer_df', skip_standardization=False)¶

GROUP = 'Polymer dissipation loss'¶

BASE_DIR = 'polymer_df'¶

HEADER = 'Df'¶

GENOPT_DEFAULTS = (0.01, 0.001, 1)¶

schrodinger.application.matsci.ml_prediction_gui_utils.model_class¶: alias of TripletReorganizationEnergyModel