Abstract
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Coimbra, Portugal, by NewsRx correspondents, research stated, “In this study we analyze the dielectric properties of a recently published dataset to identify high-refractiveindex and highband- gap materials that are crucial for modern optoelectronic applications. We employ advanced crystal graph convolutional neural networks and density functional perturbation theory calculations to accelerate the discovery of such materials.” Financial support for this research came from Fundao para a Cincia e Tecnologia, Portugal. Our news editors obtained a quote from the research from the University of Coimbra, “Our analysis confirms the traditional inverse relationship between band gap and dielectric constant, which persists even in this large dataset. However, our study reveals several promising materials that possess competitive properties compared to current industry standards.” According to the news editors, the research concluded: “Our findings provide valuable insights into the field of dielectric materials and demonstrate the potential of advanced machine learning and computational techniques for accelerating materials discovery.”
NETL
NSTL