Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning

arXiv:2403.05119v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cond-mat.mtrl-sci cs.lg electronic energy forecast gap machine machine learning machine learning techniques material materials nature type