This calculator is published at http://ai.eecs.northwestern.edu/MPpredictor
The basic requirement for using the files are a Python 3.6.8 environment with the packages listed in requirements.txt. It is advisable to create an virtual environment with the correct dependencies.
The work related experiments was performed on Linux Fedora 8.8 Ootpa. The code should be able to work on other Operating Systems as well but it has not been tested elsewhere.
Here is a brief description of the files and folders:
-
crossproperty.py: code to launch the predictor. -
functions.py: code that contains functions to perform featurization. -
templates: folder contains HTML files for the homepage and response page. -
static: folder contains the static elements (CSS, javascript files).
The code was developed by Vishu Gupta from the CUCIS group at the Electrical and Computer Engineering Department at Northwestern University.
- Vishu Gupta, Kamal Choudhary, Yuwei Mao, Kewei Wang, Francesca Tavazza, Carelyn Campbell, Wei-keng Liao, Alok Choudhary, and Ankit Agrawal. "MPpredictor: An artificial intelligence-driven web tool for composition-based material property prediction." Journal of Chemical Information and Modeling 63, no. 7 (2023): 1865-1871. [DOI] [PDF]
@article{gupta2023mppredictor,
title={MPpredictor: An artificial intelligence-driven web tool for composition-based material property prediction},
author={Gupta, Vishu and Choudhary, Kamal and Mao, Yuwei and Wang, Kewei and Tavazza, Francesca and Campbell, Carelyn and Liao, Wei-keng and Choudhary, Alok and Agrawal, Ankit},
journal={Journal of Chemical Information and Modeling},
volume={63},
number={7},
pages={1865--1871},
year={2023},
publisher={ACS Publications}
}The research code shared in this repository is shared without any support or guarantee of its quality. However, please do raise an issue if you find anything wrong, and I will try my best to address it.
email: [email protected]
Copyright (C) 2023, Northwestern University.
See COPYRIGHT notice in top-level directory.
This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). Partial support is also acknowledged from NSF award CMMI-2053929, and DOE awards DE-SC0019358, DE-SC0021399, and Northwestern Center for Nanocombinatorics.