Polaritonic Index (PI) is a computational tool designed to analyze and classify excitations in hybrid systems composed of a metal nanoparticle and one or more molecules. By examining transition density file, PI can differentiate between metal, molecular and 'hybrid' excitations (these could be polaritonic or charge transfer excitations).
Polaritonic Index (PI) is a computational tool designed to analyze and classify excitations in hybrid systems composed of a metal nanoparticle and one or more molecules. By examining transition density file, PI can differentiate between metal, molecular and "hybrid" excitations (these could be polaritonic or charge transfer excitations).
Excitation Analysis
PI employs a novel algorithm to analyze the nature of excitations in these hybrid systems. This algorithm calculates a Polaritonic Index (PI) for each excitation, which represents the ratio between the transition density on the molecule(s) and the ones on the metal.
The PI is calculated as follows:
$
PI( \omega_k) = \frac{\Delta \rho_{mol} }{\Delta \rho_{cluster} } =
\frac{2^{3/2} \frac{\sum_{I =1}^{Q_{mol}} \sum_{i = 1}^{N} e^{-\left [ \frac{(\vec{r_i} - \vec{R}I)^2}{ 2 \sigma_i ^2} \right ] } \left| \Delta \rho (\vec{r_i}, \omega_k)\right|}{\sum{j}^{Q_{mol}} Z_j \cdot N_j }}
{2^{3/2} \frac{\sum_{J =1}^{Q_{cluster}} \sum_{i = 1}^{N} e^{-\left [ \frac{(\vec{r_i} - \vec{R}j)^2}{ 2 \sigma_j ^2} \right ] } \left| \Delta \rho (\vec{r_i}, \omega_k)\right|}{\sum{j}^{Q_{cluster}} Z_j \cdot N_j }}
$
where:
In general, a higher PI value indicates a stronger molecular character, while a lower value suggests a metallic character.
Interpretation of PI Values:
The code offers two ways to perform analysis: basic and full.
Input Files: Requires two files:
Process:
The ground_state_analysis script analyzes the ground state file. It calculates the contribution of each atom to the total electron density. To estimate atomic contributions to the electron density, a Gaussian function is assigned to each atom. The Gaussian width is optimized by minimizing the difference between theoretical and calculated atomic charges. This width is determined by a weight factor and a radius that characterize the spatial distribution of the electronic charges. The user has the flexibility to parameterize the Gaussian using a variety of radii, including covalent, van der Waals, and atomic.
The final output is a CSV file named 'weights_charges.csv'. The first column of this file lists the optimized weights, while the second column provides the corresponding best-fit values for the ground state charge density. These best-fit values will be used as normalization factors in subsequent excited_state_analysis calculations. The normalization factor is calculated based on the total number of "calculated" valence electrons (Z) and the number of atoms of each type (N).
Following the ground state analysis, the generated 'weights_charges.csv' file can be utilized for subsequent analyses. If the file exists, it is directly employed by the code; otherwise, the user is prompted to provide an alternative file. The polaritonic index is then calculated as described in Equation 1. In both ground state and excited state analyses, the Gaussian broadening can be parameterized using covalent, atomic, or van der Waals radii. It is essential for the user to ensure consistency in the choice of radii throughout the analysis.
Input Files: Requires one file:
Process: In basic analysis mode, the ground state analysis, which involves optimizing weights and calculating charges, is bypassed. Instead, the code directly analyzes the transition density (TD) file. The script attempts to load pre-calculated weights from the 'weights_charges.csv' file. If this file is not found, the user is prompted to either specify an alternative file or provide a uniform weight value between 0.5 and 2. This uniform weight will be assigned to all atoms in the system. The normalization term in the calculation will use the total number of valence electrons (Z) and the number of atoms of each type (N). The polaritonic index is then computed according to Equation 1.
WARNING: Installing via pip from PyPI might not work as expected. You may need to manually install the dependencies (matplotlib, pygenmat, rich-click, and threadpoolctl). However, installation directly from the Git repository functions correctly.
The software requirements to run the application are:
rich_click, numpy, pandas, matplotlib, scikit-learn.read_file, data_analysis, utils)..cube or .cub format file for charge density input.For Git users:
git clone https://github.com/luciacasc/polaritonic_index.git
cd polaritonic_index
pip install -r requirements.txt
pip install .
From PyPI:
pip install polaritonic_index
This tool processes ground state charge density data, extracts atomic information, and calculates weights to minimize the error between the number of valence electrons per atom and the computed electronic charge distribution. The results are saved in a CSV file for further analysis.
.cube or .cub charge density files.scp).weights_charges.csv).ground_state_analysis -f0 <filename> [options]
Below are details about all available options, their purpose, and examples of usage:
-f0, --filenameGroundDescription:
Specifies the path to the ground state charge density file. This file contains the electronic density data required for processing.
Example:
ground_state_analysis -f0 charge_density.cube
-r, --remoteDescription:
Allows the user to specify a remote server location to download the charge density file using scp. This is useful if the file is not stored locally. It requires proper authentication.
The file path must follow the format: username@server:/path/to/file/.
Behavior:
scp.Example:
ground_state_analysis -f0 charge_density.cube -r username@server:/path/to/file
-rd, --radius_typeDescription:
Specifies the parameter used to characterize the spatial charge distribution of atoms in the system.
Options:
covalent (default):van_der_waals:atomic:Example:
ground_state_analysis -f0 <filename> -rd van_der_waals
-v, --verbosityDescription:
Enables verbose mode to provide detailed output in the console during the execution of the script.
Behavior:
Example:
ground_state_analysis -f0 <filename> -v
The script produces the following outputs:
CSV File (weights_charges.csv):
Contains the calculated weights and electronic charge distribution:
#Weights,Computed charge
0.123,1.456
0.789,2.345
...
Console Logs (if -v is enabled):
Displays intermediate calculations and results in a tabular format.
The file <filename> does not exist
The file name must have the extension .cube or .cub
The file <filename> does not exist
.cube or .cub format. The coordinates provided in the file are assumed to be expressed in Bohr.This tool analyzes transition density files to classify excitations in systems composed of metallic nanoparticles and melecule(s). By leveraging automatic or manual clustering methods, the tool calculates Polaritonic Index (PI) to classify excitations as molecular-like, metal-like, or hybrid.
This utility provides detailed results, visualizations, and options to control the clustering and analysis process.
Here is an overview of how the code works:
Data Pre-processing: the code starts with pre-processing the input data. The required format for the input file is ‘cube’.
The system automatically identifies the number and the type of components (molecules and metallic nanoparticles) and segregates them into subsystems. This process facilitates the classification of excitation types by considering one molecule at a time in conjunction with the cluster. The results of this clustering are visualized using 2D and 3D plots, with different components highlighted in distinct colors (using the -p flag). Alternatively, users can manually clustering, entering the line corresponding to the first atom of each component part of your system. For example, if your system consists of one cluster of 20 atoms and a molecule of 24 atoms, the manual clustering would be -m 1 -m 20.
Classification: the code calculates the Polatitonic Index using the Equation 1 formula and classifies the excitations distinguishing the nature bewteen metal, molecular and hybrid.
Transition Density (TD) File Analysis:
.cube files.scp.Clustering:
Excitation Classification Computes Polaritonic Index (PI) to classify excitations.
Visualization: Plots the clustered subsystems in 2D and 3D space, providing a visual representation of the molecular and metallic domains.
Verbose Output: Offers detailed insights into intermediate calculations, normalization terms, and subsystem charges.
excited_state_analysis -f <filenameTD> [options]
-f, --filenameTDDescription:
Specifies the path to the transition density (TD) file. This is the primary input file for analyzing excitations.
Example:
excited_state_analysis -f transition_density.cube
-r, --remoteDescription:
Downloads the specified file(s) from a remote server using scp.
Requirements:
username@server:/path/to/file.Example:
excited_state_analysis -f transition_density.cube -r username@server:/path/to/files/
-rd, --radius_typeDescription:
Specifies the type of radius to use for charge density calculations.
Options:
covalent (default): Uses the covalent radius for bonded atoms.van_der_waals: Uses the Van der Waals radius for non-bonded atoms.atomic: Uses the atomic radius for the overall size of the atom.Example:
excited_state_analysis -f transition_density.cube -rd van_der_waals
-m, --manualDescription:
Enables manual clustering by specifying the first atom of each subsystem. Enter the line corresponding to the first atom of each component part of your system. For example, if your system consists of one cluster of 20 atoms and a molecule of 24 atoms, the manual clustering would be -m 1 -m 20.
Usage:
Provide the starting atom indices for each cluster using -m.
Example:
excited_state_analysis -f transition_density.cube -m 1 -m 20
-p, --plotDescription:
Generates 3D visualizations of the clustered subsystems.
Example:
excited_state_analysis -f transition_density.cube -p
-v, --verbosityDescription:
Enables verbose mode for detailed output during execution.
Example:
excited_state_analysis -f transition_density.cube -v
Polaritonic Index (PI):
Plots (Optional):
Console Logs:
The file <filename> does not exist
.cube or .cub extension:
The file name must have the extension .cube or .cub
The file <filename> does not exist
excited_state_analysis -f transition_density.cube
excited_state_analysis-f transition_density.cube -fd difference_density.cube -r username@server:/path/
excited_state_analysis -f transition_density.cube -m 1 -m 21 -p
.cube or .cub format..cube or .cub format. The coordinates provided in the file are assumed to be expressed in Bohr.FAQs
Polaritonic Index (PI) is a computational tool designed to analyze and classify excitations in hybrid systems composed of a metal nanoparticle and one or more molecules. By examining transition density file, PI can differentiate between metal, molecular and 'hybrid' excitations (these could be polaritonic or charge transfer excitations).
We found that polaritonic-index demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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