Towards Direct-Gap Silicon Phases by the Inverse Band Structure Design Approach

Abstract

Diamond silicon (Si) is the leading material in current solar cell market. However, diamond Si is an indirect band gap semiconductor with a large energy difference (2.4 eV) between the direct gap and the indirect gap, which makes it an inefficient absorber of light. In this work, we develop a novel inverse-band structure design approach based on the particle swarming optimization algorithm to predict the metastable Si phases with better optical properties than diamond Si. Using our new method, we predict a cubic Si20 phase with quasi-direct gaps of 1.55 eV, which is a promising candidate for making thin-film solar cells.

arXiv commentsubmitted to Phys. Rev

References in corpus (6)

• CALYPSO: a method for crystal structure prediction
• Crystal Structure Prediction via Particle Swarm Optimization
• Molecular geometry optimization with a genetic algorithm
• Superconductive "sodalite"-like clathrate calcium hydride at high pressures
• Predicting Two-Dimensional Boron-Carbon Compounds by the Global Optimization Method
• Si3AlP: A new promising material for solar cell absorber

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