descmap 1.0.1

Automatic feature selection and volcano curve generation.

The Descriptor-Based Microkinetic Analysis Package (DescMAP) is a Python library developed by the Vlachos Research Group at the University of Delaware. This code was developed to automate descriptor selection and volcano curve generation for heterogeneous catalysis using empirical and semi-empirical approaches coupled with microkinetic modeling. Both electronic and geometric descriptors are supported. Inputting data via spreadsheets and controlling program behavior via template files increases flexibility and supported capabilities.

Documentation

See our documentation page for docstrings and more details.

Getting Started

1. Install using pip (see documentation for full instructions)

$ pip install descmap

2. Look at the provided examples

Developers

• Jonathan Lym ([e-mail])

• Xue Zong ([e-mail])

Dependencies

• Python >= 3.9

• Numpy >= 1.24.2: Used for vector and matrix operations

• Pandas >= 1.5.3: Used to import data from Excel or CSV files

• Scipy >= 1.10.0: Used for curve fitting

• Scikit-Learn >= 1.2.1: Choose descriptors based on DFT data

• RDKit >= 2022.9.4: Used for constructing feasible chemical structures required by pGradd

• Matplotlib >= 3.6.3: Used for generating plots

• Plotly >= 5.13.0: Plots interactive volcano curves

• Chart-studio >= 1.1.0: Provide utilities for interfacing with Plotly’s Chart Studio service

• pMuTT >= 1.3.2: Generates input files for OpenMKM

• pGradd >= 2.9.5: Estimate thermodynamic properties of molecules

• VUnits >= 0.0.4: Unit conversion and constants

• xlsxwriter >= 3.0.8: Create Excel xlsx files

• pyDOE >= 0.3.8: Experimental design package to provide sampling method

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Contributing

If you have any suggestion or find a bug, please post to our Issues page on GitHub.

Questions

If you have any question or run into any issue, please post to our Issues page on GitHub.

Funding

This material is based upon work supported by the Department of Energy’s Office of Energy Efficient and Renewable Energy’s Advanced Manufacturing Office under Award Number DE-EE0007888-9.5.