The band gap is a fundamental property in materials science which plays a significant role in determining the applications of a material in fields such as optoelectronics, quantum computing, etc. The process of determining the band gap of a material through direct means (opticalabsorption spectroscopy, photoluminescence spectroscopy, and photoconductivity measurements) or indirect means involving theoretical calculations (Density Functional Theory) requires significant time and capital investment.
A simple Feed Forward Neural Network (FFNN) showed promising results in predicting the band gap of crystalline materials. Further experimentation with different models, learning rates and data samples is needed to improve accuracy to a sufficient degree.
Band gap data compiled in /DataFiles/UpdatedMaterialsDF.csv was taken from MaterialsProject.org
- Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1 July 2013; 1 (1): 011002. https://doi.org/10.1063/1.4812323
Elemental data used for featurization listed in /DataFiles/PeriodicTable.csv was taken from https://gist.github.com/GoodmanSciences/c2dd862cd38f21b0ad36b8f96b4bf1ee