Disorder by design: A data-driven approach to amorphous semiconductors without total-energy functionals

Dil K Limbu,Stephen R Elliott,Raymond Atta-Fynn,Parthapratim Biswas

doi:10.1038/s41598-020-64327-3

Abstract

X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multiobjective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects (≤1%), a narrow bond-angle distribution of width 9–11.5°, and an electronic gap of 0.8–1.4 eV. These data-driven information-based models have been found to produce electronic and vibrational properties of a-Si that match accurately with experimental data and rival that of the Wooten-Winer-Weaire models. The study confirms the effectiveness of a multiobjective optimization approach to the structural determination of complex materials, and resolves a long-standing dispute concerning the uniqueness of a model of tetrahedral amorphous semiconductors obtained via inversion of diffraction data.

Highlights

X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods
While a variant of the reverse Monte Carlo (RMC) method was used by Strong and Kaplow[4,5] as early as in the 1960s to predict the structure of crystalline B2O34 and vitreous selenium[5], by refining X-ray diffraction data via random walks, it was McGreevy and co-workers[3,6,7] who first applied the method systematically to model the structure of disordered solids[3]
A number of RMC or RMC-derived studies[3,6,7,8,9,10,11,12,13,14] on amorphous silicon (a-Si)/a-Ge have been reported in the past, none of these could demonstrate the presence of a gap in the electronic spectrum and a low concentration of coordination defects (≤1%), as observed in optical measurements[15,16] and electron spin resonance (ESR)[17] experiments, respectively, for these materials

Summary

Introduction

X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. Since an ensemble of three-dimensional structures can lead to an identical two-body correlation function, additional information is required to uniquely determine the correct structure of a material by suitably reducing the volume of the solution space While this reduction is generally achieved by imposing structural constraints during RMC simulations, the hierarchy and conflictive nature of the constraints render the resulting optimization problem very difficult, leading to poor structural solutions. To overcome this problem, a new breed of hybrid approaches have been developed in recent years[18,19,20,21,22], which can successfully address the uniqueness problem by simultaneously employing experimental data and a total-energy functional. We discuss an extension of the scheme with a few examples to incorporate microstructural properties of realistic samples of a-Si and a-Ge as observed in experiments

Objectives

Results

Conclusion