Computational Analysis of Physical Properties of Renewable Energy Materials

Abstract

The paper is a computational design and analysis of material to be used in renewable energy conversion with the aim of improving energy conversion efficiency, sustainability and performance of the device. Structural, electronic, optical, and transport properties of a variety of materials, including carbons, lead-free halide perovskites, nanostructured materials, organic dyes, and carbon-based electrodes have been investigated using advanced computational methods, including first-principles, density functional theory, and machine learning-based studies. The methodology also used simulations in silico and predictive modeling to come up with good candidates of solar cells, energy storage devices and hybrid photovoltaic/thermal devices. The important results include that certain nanostructured materials have optimized bandgaps, enhanced carrier mobility, and desirable stability whereas the computational-based optimizations of perovskite and organic dyes structures have been found to improve light absorption and charge transport. Also, computational -experimental methods expose the promise of lignocellulosic and other bio-derived materials to sustainable energy use. On the whole, the research has shown that computational material design can be used to speed up the process of identifying high-performance renewable energy materials as well as give mechanistic understanding of how they may behave under their working conditions. Machine learning integration also allows high-speed screening of large chemical spaces, and thus advocates the creation of affordable and ecologically appropriate energy systems. These findings highlight the transformative role of computational approaches in guiding the design of next-generation materials for renewable energy technologies.