Estimation of Electronic Band Gap Energy From Material Properties Using
  Machine Learning
Sagar Prakash Barad Sajag Kumar Subhankar Mishra
Machine learning techniques are utilized to estimate the electronic band gap
energy and forecast the band gap category of materials based on experimentally
quantifiable properties. The determination of band gap energy is critical for
discerning various material properties, such as its metallic nature, and
potential applications in electronic and optoelectronic devices. While
numerical methods exist for computing band gap energy, they often entail high
computational costs and have limitations in accuracy and scalability. A machine
learning-driven model capable of swiftly predicting material band gap energy
using easily obtainable experimental properties would offer a superior
alternative to conventional density functional theory (DFT) methods. Our model
does not require any preliminary DFT-based calculation or knowledge of the
structure of the material. We present a scheme for improving the performance of
simple regression and classification models by partitioning the dataset into
multiple clusters. A new evaluation scheme for comparing the performance of
ML-based models in material sciences involving both regression and
classification tasks is introduced based on traditional evaluation metrics. It
is shown that on this new evaluation metric, our method of clustering the
dataset results in better performance.