Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides

Authors

  • Ujjwal Poudel Department of Physics, St. Xavier’s College, Kathmandu, Nepal
  • Madhu Sudhan Bhusal Department of Physics, St. Xavier’s College, Kathmandu, Nepal
  • Manish Bhurtel Institute of Engineering, Tribhuvan University, Kathmandu, Nepal
  • Atish Adhikari Deerhold Ltd, Kathmandu, Nepal
  • Narayan Prasad Adhikari Central Department of Physics, Tribhuvan University, Kathmandu, Nepal

DOI:

https://doi.org/10.3126/jnphyssoc.v8i1.48282

Keywords:

Lattice Constant in Perovskite Oxides, Decision Tree, Artificial Neural Network, Random Forest, K-Nearest Neighbour, Support Vector Machine

Abstract

A sample of 3,115 data of perovskite oxides in the form of ABO3 (A and B being the cations) was taken for this study of the application of machine learning in predicting the lattice constants (a determining factor in material design). The ANN, DT, RF, KNN, and SVM models were used to predict the lattice constants of perovskites because machine learning techniques have been phenomenal in uncovering crystal structures in the field of material research in recent years. These models used properties like ionic radii, formation energy, and band gap as input features. The R2 score was used to assess the regression model’s performance. The Random Forest Regression Model outperforms all other regression models regarding dataset and features.

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Published

2022-12-13

How to Cite

Poudel, U., Bhusal, M. S., Bhurtel, M., Adhikari, A., & Adhikari, N. P. (2022). Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides. Journal of Nepal Physical Society, 8(1), 27–34. https://doi.org/10.3126/jnphyssoc.v8i1.48282