AlloySustainability 0.1.7

Compute and visualize the sustainability impacts of alloys based on their chemical composition.

AlloySustainability

AlloySustainability is a Python package designed to compute and visualize the sustainability impacts of alloys based on their elemental composition. It retrieves key indicators from external data sources (including a file automatically downloaded from GitHub) and produces comprehensive metrics and visualizations.

Features

• Compute Indicators: Given the mass fractions of 18 elements composing an alloy, compute a range of sustainability indicators (e.g., mass price, supply risk, embodied energy, water usage).
• Embedded Data: The package includes two CSV files (gen_RTHEAs_vs_Fe_df.csv, gen_HTHEAs_vs_Ni_df.csv) embedded within the package, providing baseline reference data for comparison.
• Automatic Data Retrieval: A third CSV file (gen_18element_imputed_v202412.csv) is automatically downloaded from GitHub, ensuring that you always have the latest data.
• Visualization: Easily generate comparative plots (e.g., violin plots) to compare the new alloy’s metrics against reference classes such as FCC HEAs, BCC HEAs, Steels, and Ni-based alloys.

Installation

Install AlloySustainability directly from PyPI:

pip install AlloySustainability

Recommended Environment: Google Colab

To ensure the best experience and compatibility, it is highly recommended to use Google Colab for running this package. Google Colab provides a preconfigured Python environment with most dependencies pre-installed, and it allows seamless integration with cloud-based data retrieval.

To get started:

1. Open Google Colab.
2. Install the package.
3. Run the example usage code in a Colab notebook.

This ensures minimal configuration and avoids potential environment-related issues.

Usage

from AlloySustainability.computations import (
    load_element_indicators,
    load_RTHEAs_vs_Fe_df,
    load_HTHEAs_vs_Ni_df,
    compute_impacts
)
from AlloySustainability.visualization import plot_alloy_comparison
import matplotlib.pyplot as plt

# Load data
element_indicators = load_element_indicators()
RTHEAs_Fe_df = load_RTHEAs_vs_Fe_df()
HTHEAs_Ni_df = load_HTHEAs_vs_Ni_df()

# Define the alloy composition (mass fractions of 18 elements)
# Make sure the fractions sum up to 1.0
composition_mass = [0, 0.2, 0.2, 0, 0, 0, 0.2, 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0.2]

# Compute the sustainability impacts
new_alloy_impacts = compute_impacts(composition_mass, element_indicators)

# Visualize the alloy impacts compared to reference classes
fig = plot_alloy_comparison(new_alloy_impacts, RTHEAs_Fe_df, HTHEAs_Ni_df)
plt.show()

Requirements

• Python 3.6+
• numpy
• pandas
• matplotlib
• seaborn
• requests

Further Reading

For more information on sustainability indicators in the context of high entropy alloys, please refer to:

Considering sustainability when searching for new high entropy alloys S. Gorsse, T. Langlois, M. R. Barnett Sustainable Materials and Technologies 40 (2024) e00938 https://doi.org/10.1016/j.susmat.2024.e00938