Massive development of new materials

The ab-initio approach coupled with data mining

von Mohamed Boubchir, Hafid Aourag, Rachid Boubchir

272 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

Perovskites and double halide perovskites (A¿BX¿) have attracted significant attention due to their tunable structural, electronic, and magnetic properties, enabling applications in photovoltaics, optoelectronics, and spintronics. Density Functional Theory (DFT) plays a crucial role in understanding these materials by providing accurate predictions of structural stability, electronic band structures, density of states, and magnetic behavior. In particular, DFT has been widely used to investigate bandgap engineering and half-metallicity in double perovskites.However, the high computational cost of DFT limits large-scale exploration of the vast compositional space. To overcome this challenge, Machine Learning (ML) techniques have emerged as efficient tools for rapid property prediction. By training on DFT-generated datasets, ML models can accurately estimate key properties such as bandgaps and formation energies. The integration of DFT and ML offers a powerful framework for accelerating the discovery and design of advanced perovskite materials.

• Themen: Mathematik und Naturwissenschaften Physik Relativitätstheorie
• ISBN: 9786209824586
• Sprache: Englisch
• Erscheinungsdatum: 11.06.2026
• Größe: 220 x 150 mm
• Verlag: GlobeEdit