hmetni/Superconducting_TM_C_N

Dataset and code used in "Predicting the Superconducting Critical Temperature in Transition Metal Carbides and Nitrides using Machine Learning"

Superconducting_TM_C_N

Dataset and code used in "Predicting the Superconducting Critical Temperature in Transition Metal Carbides and Nitrides using Machine Learning".

The dataset is contained in the file: dataset.csv. It contains the following columns:

- num: whether the compound was extracted (EXT) or was found in the SuperCon database (the corresponding compound number)

- compound: the compound

- Tc: the experimentally measured critical temperature

- a: the lattice parameter (estimated in some cases, see paper)

- elements: the elements present, therefore the substitution series. (=BASE for pure compounds)

- 1, 2: pure compounds 1 and 2 when the compound is an alloy.

- perc_1_in_2: the fraction of compound 1 in the compound

The dataset_features.csv contains the above-mentioned columns as well as additional MagPie features generated using matminer.

To use the code, Python 3 should be installed on the local machine. A recommended way of installation is through Anaconda, see https://docs.anaconda.com/anaconda/install/index.html.

First clone this repository into the desired directory and access it using the following two commands from a terminal window:

git clone https://github.com/hmetni/Superconducting_TM_C_N.git
cd Superconducting_TM_C_N

Once Anaconda is installed and the repository cloned, create a virtual environment using:

conda create --name Superconducting_TM_C_N python=3.7

Then activate your newly created environment:

conda activate Superconducting_TM_C_N

The next step is to install the requirements. The main packages required for this work are: pandas, scikit-learn, umap, seaborn, matplotlib and matminer.

Once the requirements are installed, the notebooks can be executed in interfaces such as Jupyter Notebook.