Prediction-model/CITATION.cff at main · neweracy/Prediction-model · GitHub

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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
title: "Predicting Materials Band Gaps from Chemical Composition"
version: 1.0.0
date-released: 2024-01-01
url: "https://github.com/neweracy/Prediction-model"
repository-code: "https://github.com/neweracy/Prediction-model"
license: MIT
authors:
  - family-names: "Your"
    given-names: "Name"
    orcid: "https://orcid.org/0000-0000-0000-0000"
keywords:
  - materials-informatics
  - band-gap-prediction
  - machine-learning
  - materials-science
  - compositional-descriptors
  - magpie
  - xgboost
  - random-forest
  - materials-project
  - pymatgen
  - matminer
abstract: >-
  A composition-based machine learning framework for predicting electronic
  band gaps of inorganic materials. Uses Magpie compositional descriptors
  derived from chemical formulas and trains XGBoost and Random Forest
  regressors on 15,537 thermodynamically stable compounds from the
  Materials Project database.