Identifying Direct Bandgap Silicon Structures with High-throughput Search and Machine Learning Methods

TL;DR

This paper employs high-throughput screening and machine learning to discover new direct-bandgap silicon structures with potential photovoltaic applications, significantly advancing silicon material research.

Contribution

It introduces a systematic high-throughput approach combined with machine learning to identify novel direct-gap silicon allotropes with desirable electronic properties.

Findings

Identified 47 direct-gap silicon structures with potential for photovoltaics.

Discovered Si12-P1 with a 1.69 eV bandgap and highest efficiency among candidates.

Recalculated bandgaps using HSE06 functional for accurate property assessment.

Abstract

Utilizations of silicon-based luminescent devices are restricted by the indirect-gap nature of diamond silicon. In this study, the high-throughput method is employed to expedite discoveries of direct-gap silicon crystals. The machine learning (ML) potential is utilized to construct a dataset comprising 2637 silicon allotropes, which is subsequently screened using an ML Hamiltonian model and density functional theory calculations, resulting in identification of 47 direct-gap Si structures. We calculate transition dipole moments (TDM), energies, and phonon bandstructures of these structures to validate their performance. Additionally, we recalculate bandgaps of these structures employing the HSE06 functional. 22 silicon allotropes are identified as potential photovoltaic materials. Among them, the energy per atom of Si22-Pm, which has a direct bandgap of 1.27 eV, is 0.026 eV/atom higher than diamond silicon. Si18-C2/m, which has a direct bandgap of 0.796 eV, exhibits the highest TDM among identified structures. Si16-P21/c, which has a direct bandgap of 0.907 eV, has the mass density of 2.316 g/cm3, which is the highest among identified structures and higher than that of diamond silicon. The structure Si12-P1, which possesses a direct bandgap of 1.69 eV, exhibits the highest spectroscopic limited maximum efficiency (SLME) among identified structures at 32.28%, surpassing that of diamond silicon. This study offers insights into properties of silicon crystals while presenting a systematic high-throughput method for material discovery.