decomnano package

Submodules

decomnano.cli module

Console script for decomnano.

decomnano.cli.parse_array_in_config(config_dict)

Parse array in config file to numpy array.

decomnano.cli.run_decomnano(config_dict=None, output='result.csv', kernel=None)

Run DecomNano with config_dict.

decomnano.cli.run_sweep(config_dict=None, output='result.csv', interval=100, kernel=None, resolution=0.1)

Run SweepDecomNano with config_dict.

decomnano.decomnano module

Main module.

class decomnano.decomnano.DecomNano(wolfram_kernel=None, input=None, fix_bulk_fraction=False, hollow_shell=False)

Bases: object

DecomNano class for solving the heterogeneity analysis of bimetallic nanoparticles using coordination numbers obtained from XAS analysis.

Parameters:

• wolfram_kernel (str) – Path to the Wolfram kernel. Defaults to None, which searches system default.

• input (dict) – Input dictionary for the heterogeneity analysis. Defaults are {“dP”: 2.77, “dA”: 2.88, “fA”: 0.2, “nAA”: 6.5, “nPP”: 9.9, “nAP”: 0.6, “nPA”: 1.13, “DA”: 0, “DAP”: 18, “DP”: 5}.

session

Wolfram Language session

Type:

WolframLanguageSession

input

Input dictionary for the heterogeneity analysis. Defaults are {“dP”: 2.77, “dA”: 2.88, “fA”: 0.2, “nAA”: 6.5, “nPP”: 9.9, “nAP”: 0.6, “nPA”: 1.13, “DA”: 0, “DAP”: 18, “DP”: 5}.

Type:

dict

results

Results of the heterogeneity analysis

Type:

pd.DataFrame

column

Column names of the results

Type:

list

dict_regex

Regular expression for converting the mathematica results to a dictionary

Type:

re.Pattern

Note

The variables in the input dictionary different from the paper due to the naming rules of python. Following table shows the correspondence between the variables in the paper and the input dictionary.

Notation in paper	Keys	Descriptions
\(d_P\)	dP	Interatomic spacing of Pt nanoparticles
\(d_A\)	dA	Interatomic spacing of Au nanoparticles
\(\frac{M_A}{M_A + M_P}\)	fA	Molar fraction of Au
\(n_{A-A}\)	nAA	Total first nearest neighbor coordination number of Au-Au bonds.
\(n_{P-P}\)	nPP	Total first nearest neighbor coordination number of Pt-Pt bonds.
\(n_{A-P}\)	nAP	Total first nearest neighbor coordination number of Au-Pt bonds.
\(n_{P-A}\)	nPA	Total first nearest neighbor coordination number of Pt-Au bonds.
\(D_A\)	DA	Diameter of Au nanoparticles
\(D_{AP}\)	DAP	Diameter of PtAu nanoparticles
\(D_P\)	DP	Diameter of Pt nanoparticles
\(n_{A-M,AP}\)	nAM_AP	First nearest neighbor coordination number of Au-metal bonds
\(n_{P-M,AP}\)	nPM_AP	First nearest neighbor coordination number of Pt-metal bonds
\(n_{A-A,AP}\)	nAA_AP	First nearest neighbor coordination number of Au-Au bonds
\(n_{A-P,AP}\)	nAP_AP	First nearest neighbor coordination number of Au-Pt bonds
\(n_{P-A,AP}\)	nPA_AP	First nearest neighbor coordination number of Pt-Au bonds
\(n_{P-P,AP}\)	nPP_AP	First nearest neighbor coordination number of Pt-Pt bonds
\(X_A\)	XA	Molar ratio of Au nanoparticles
\(X_{AP}\)	XAP	Molar ratio of PtAu nanoparticles
\(X_P\)	XP	Molar ratio of Pt nanoparticles
\(y\)	y	Molar fraction of Au in PtAu nanoparticles

calc_decomnano(**kwargs)

Initialize and solve the heterogeneity analysis of bimetallic nanoparticles using coordination numbers obtained from XAS analysis.

Parameters:

**kwargs – Keyword arguments for the input dictionary. Defaults are {“dP”: 2.77, “dA”: 2.88, “fA”: 0.2, “nAA”: 6.5, “nPP”: 9.9, “nAP”: 0.6, “nPA”: 1.13, “DA”: 0, “DAP”: 18, “DP”: 5}.

Examples

>>> dn = DecomNano()
>>> dn.calc_decomnano(RPt=2.77, RAu=2.88, fAu=0.2, nAuAu=6.5, nPtPt=9.9, nAuPt=0.6, nPtAu=1.13, DA=0, DAP=18, DP=5)

check_duplicate_input()

Checks if the input dictionary is already solved.

Returns:

True if the input dictionary is already solved, False otherwise.

Return type:

bool

column = ['dP', 'dA', 'fA', 'nAA', 'nPP', 'nAP', 'nPA', 'DA', 'DAP', 'DP', 'nAM_AP', 'nPM_AP', 'nAA_AP', 'nAP_AP', 'nPA_AP', 'nPP_AP', 'XAP', 'XA', 'XP', 'y']

column_hollow = ['dP', 'dA', 'fA', 'nAA', 'nPP', 'nAP', 'nPA', 'DA', 'DAP', 'DP', 'DAPh', 'nAM_AP', 'nPM_AP', 'nAA_AP', 'nAP_AP', 'nPA_AP', 'nPP_AP', 'XAP', 'XA', 'XP', 'y']

convert_dict(result)

Helper function to convert the result of WolframLanguageSession.evaluate() to a dictionary.

Returns:

Dictionary of the result.

Return type:

dict

decomnano_equation = 'DecomNano[{dP}, {dA}, {fA}, {nAA}, {nPP}, {nAP}, {nPA}, {DA}, {DAP}, {DP}]'

decomnano_equation_hollow = 'DecomNanoh[{dP}, {dA}, {fA}, {nAA}, {nPP}, {nAP}, {nPA}, {DA}, {DAP}, {DP}, {DAPh}]'

init_input(**kwargs)

Initialize the input dictionary of DecomNano class.

Parameters:

**kwargs – Keyword arguments for the input dictionary. Defaults are {“dP”: 2.77, “dA”: 2.88, “fA”: 0.2, “nAA”: 6.5, “nPP”: 9.9, “nAP”: 0.6, “nPA”: 1.13, “DA”: 0, “DAP”: 18, “DP”: 5}.

Examples

>>> dn = DecomNano()
>>> dn.init_input(RPt=2.77, RAu=2.88, fAu=0.2, nAuAu=6.5, nPtPt=9.9, nAuPt=0.6, nPtAu=1.13, DA=0, DAP=18, DP=5)

load_results(filename='results.csv')

Loads the results to the DecomNano class.

Parameters:

filename (str) – Filename of the results. Defaults to “results.csv”.

print_input()

Prints the input dictionary of DecomNano class.

print_results()

Prints the results of the heterogeneity analysis of bimetallic nanoparticles using coordination numbers obtained from XAS analysis.

respawn_kernel()

save_results(filename='results.csv')

Saves the results of the heterogeneity analysis of bimetallic nanoparticles using coordination numbers obtained from XAS analysis.

Parameters:

filename (str) – Filename of the results. Defaults to “results.csv”.

solve_decomnano()

Solves the heterogeneity analysis of bimetallic nanoparticles using coordination numbers obtained from XAS analysis.

terminate()

Terminates the WolframLanguageSession.

decomnano.sweep module

class decomnano.sweep.SweepDecomNano(wolfram_kernel=None, input_default=None, input_config=None, fix_bulk_fraction=False, respawn_interval=10000, hollow_shell=False)

Bases: object

SweepDecomNano class for calculating decomnano with various input parameters.

Parameters:

wolfam_kernel (str) – Path to the Wolfram kernel. Defaults to None, which uses the default kernel.

dn

DecomNano class for calculating decomnano.

Type:

DecomNano

input

Current input parameters used for calculating DecomNano solver.

Type:

dict

input_default

Default input parameters used for calculating DecomNano solver. input will be generated by updating input_default with input_config.

Type:

dict

input_config

Configuration of input range parameters used for calculating DecomNano solver. input_default will be generated by updating input_default with range specified in input_config. The range of input parameters can be specified by list or numpy array. If the range is specified by list or numpy array, the input parameters varies from the minimum value to the maximum value with the step size of the difference between the maximum value and the minimum value divided by the number of elements in the list or numpy array minus 1. If the range is specified by a single value, the input parameters will be varied from range(input_default-value, input_default+value, resolution). The resolution is set to 0.1 by default.

Type:

dict

Examples

>>> from decomnano import SweepDecomNano
>>> sd = SweepDecomNano()
>>> sd.calc_sweep("sweep_results")
>>> Pt40Au60BP1_input_default = dict(
        RPt = 2.77,
        RAu = 2.88,
        fAu = 0.6,
        nAuAu = 4.2,
        nPtPt = 10.0,
        nAuPt = 2.8,
        nPtAu = 0.71,
        DA = 10,
        DAP = 17,
        DP = 17,
    )
>>> Pt40Au60BP1_input_config = dict(
        nAuAu = 0.6,
        nPtPt = 0.7,
        nAuPt = 0.5,
        nPtAu = 0.8,
        DP = list(range(0, 18, 2)),
        DA = list(range(0, 18, 2))
        )
>>> sd.update_input_default(Pt40Ag60BP1_input_default)
>>> sd.update_input_config(Pt40Ag60BP1_input_config)
>>> sd.calc_sweep("sweep_results.csv")

Note

The variables in the input dictionary different from the paper due to the naming rules of python. Following table shows the correspondence between the variables in the paper and the input dictionary.

Notation in paper	Keys	Descriptions
\(d_P\)	dP	Interatomic spacing of Pt nanoparticles
\(d_A\)	dA	Interatomic spacing of Au nanoparticles
\(\frac{M_A}{M_A + M_P}\)	fA	Molar fraction of Au
\(n_{A-A}\)	nAA	Total first nearest neighbor coordination number of Au-Au bonds.
\(n_{P-P}\)	nPP	Total first nearest neighbor coordination number of Pt-Pt bonds.
\(n_{A-P}\)	nAP	Total first nearest neighbor coordination number of Au-Pt bonds.
\(n_{P-A}\)	nPA	Total first nearest neighbor coordination number of Pt-Au bonds.
\(D_A\)	DA	Diameter of Au nanoparticles
\(D_{AP}\)	DAP	Diameter of PtAu nanoparticles
\(D_P\)	DP	Diameter of Pt nanoparticles
\(n_{A-M,AP}\)	nAM_AP	First nearest neighbor coordination number of Au-metal bonds
\(n_{P-M,AP}\)	nPM_AP	First nearest neighbor coordination number of Pt-metal bonds
\(n_{A-A,AP}\)	nAA_AP	First nearest neighbor coordination number of Au-Au bonds
\(n_{A-P,AP}\)	nAP_AP	First nearest neighbor coordination number of Au-Pt bonds
\(n_{P-A,AP}\)	nPA_AP	First nearest neighbor coordination number of Pt-Au bonds
\(n_{P-P,AP}\)	nPP_AP	First nearest neighbor coordination number of Pt-Pt bonds
\(X_A\)	XA	Molar ratio of Au nanoparticles
\(X_{AP}\)	XAP	Molar ratio of PtAu nanoparticles
\(X_P\)	XP	Molar ratio of Pt nanoparticles
\(y\)	y	Molar fraction of Au in PtAu nanoparticles

calc_input_range(resolution=0.1)

Calculate input_range from input_default and input_config.

Parameters:

resolution (float) – Resolution of input_range. Defaults to 0.1. input_default will be generated by updating input_default with range specified in input_config. The range of input parameters can be specified by list or numpy array. If the range is specified by list or numpy array, the input parameters varies from the minimum value to the maximum value with the step size of the difference between the maximum value and the minimum value divided by the number of elements in the list or numpy array minus 1. If the range is specified by a single value, the input parameters will be varied from range(input_default-value, input_default+value, resolution). The resolution is set to 0.1 by default.

calc_sweep(savepath='result.csv', save_interval=100)

Calculate DecomNano solver with sweep of input parameters.

Parameters:

• savepath (str) – Path to save results. Defaults to “result.csv”.

• save_interval (int) – Interval of saving results. The results will be saved after each save_interval. Defaults to 100.

calc_sweep_const_2param(parameters=['DA', 'DP'], savepath='result.csv', save_interval=100)

Calculate DecomNano solver with sweep of input parameters with constraining two parameters to be the same.

Parameters:

• parameters (list) – List of two parameters to be constrained to be the same. Defaults to [“DA”, “DP”]. Input config of the first parameter will be applied to the second parameter.

• savepath (str) – Path to save results. Defaults to “result.csv”.

• save_interval (int) – Interval of saving results. The results will be saved after each save_interval. Defaults to 100.

Examples

>>> # To constrain DA = DP, run
>>> sd.calc_sweep_const_2param(["DA", "DP"], savepath, save_interval=100)

init_input(input_default=None, input_config=None)

Initialize input parameters for calculating DecomNano solver. Called in __init__().

print_input_range()

Print input_range.

terminate()

Terminate DecomNano solver.

update_input(input_dict)

Update input from dictionary.

Parameters:

input_dict (dict) – Dictionary of input parameters.

update_input_config(input_config, resolution=0.1)

Update input_config and calculate input_range. Copies input_config to self.input_config and calculates input_range.

Parameters:

• input_config (dict) – Dictionary to replace input_config.

• resolution (float) – Resolution of input_range. Defaults to 0.1. input_default will be generated by updating input_default with range specified in input_config. The range of input parameters can be specified by list or numpy array. If the range is specified by list or numpy array, the input parameters varies from the minimum value to the maximum value with the step size of the difference between the maximum value and the minimum value divided by the number of elements in the list or numpy array minus 1. If the range is specified by a single value, the input parameters will be varied from range(input_default-value, input_default+value, resolution). The resolution is set to 0.1 by default.

update_input_default(input_default, resolution=0.1)

Update input_default and calculate input_range. Copies input_default to self.input_default and calculates input_range.

Parameters:

• input_default (dict) – Dictionary to replace input_default.

• resolution (float) – Resolution of input_range. Defaults to 0.1. input_default will be generated by updating input_default with range specified in input_config. The range of input parameters can be specified by list or numpy array. If the range is specified by list or numpy array, the input parameters varies from the minimum value to the maximum value with the step size of the difference between the maximum value and the minimum value divided by the number of elements in the list or numpy array minus 1. If the range is specified by a single value, the input parameters will be varied from range(input_default-value, input_default+value, resolution). The resolution is set to 0.1 by default.

update_input_from_list(input_list, label=None)

Update input from list.

Parameters:

• input_list (list) – List of input parameters.

• label (list) – List of labels for input parameters. Defaults to None. If label is None, the order of input parameters should be the same as the order of input parameters in self.input. If label is not None, the order of input parameters should be the same as the order of labels.

Module contents

Top-level package for DecomNano.