Atomistic simulation of nearly defect-free models of amorphous silicon: An information-based approach (original) (raw)
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Scientific Reports
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 e...
Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon
Angewandte Chemie International Edition
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). Based on a similarity function ("kernel"), we define a structural metric that unifies the description of nearest-and nextnearest-neighbor environments in the amorphous state. We apply this to an ensemble of a-Si networks, generated in melt-quench simulations with an ML-based interatomic potential, in which we tailor the degree of ordering by varying the quench rates down to 10 10 K/s (leading to a structural model that is lower in energy than the established WWW network). We then show how "machine-learned" atomic energies permit a chemical interpretation, associating coordination defects in a-Si with distinct energetic stability regions. The approach is straightforward and inexpensive to apply to arbitrary structural models, and it is therefore expected to have more general significance for developing a quantitative understanding of the amorphous state.
Nearly defect-free dynamical models of disordered solids: The case of amorphous silicon
The Journal of Chemical Physics
It is widely accepted in the materials modeling community that defect-free realistic networks of amorphous silicon cannot be prepared by quenching from a molten state of silicon using classical or ab initio molecular-dynamics (MD) simulations. In this work, we address this long-standing problem by producing nearly defect-free ultra-large models of amorphous silicon, consisting of up to half-a-million atoms, using classical molecular-dynamics simulations. The structural, topological, electronic, and vibrational properties of the models are presented and compared with experimental data. A comparison of the models with those obtained from using the modified Wooten-Winer-Weaire bond-switching algorithm shows that the models are on par with the latter, which were generated via event-based total-energy relaxations of atomistic networks in the configuration space. The MD models produced in this work represent the highest quality of amorphous-silicon networks so far reported in the literature using molecular-dynamics simulations.
Information-driven inverse approach to disordered solids: Applications to amorphous silicon
Physical Review Materials
Diffraction data play an important role in the structural characterizations of solids. While reverse Monte Carlo (RMC) and similar methods provide an elegant approach to (re)construct a three-dimensional model of non-crystalline solids, a satisfactory solution to the RMC problem is still not available. Following our earlier efforts, we present here an accurate structural solution of the inverse problem by developing a new informationdriven inverse approach (INDIA). The efficacy of the approach is illustrated by choosing amorphous silicon as an example, which is particularly difficult to model using total-energy-based relaxation methods. We demonstrate that, by introducing a subspace optimization technique that sequentially optimizes two objective functions (involving experimental diffraction data, a total-energy functional, and a few geometric constraints), it is possible to produce models of amorphous silicon with very little or no coordination defects and a pristine gap around the Fermi level in the electronic spectrum. The structural, electronic, and vibrational properties of the resulting INDIA models are shown to be fully compliant with experimental data from X-ray diffraction, Raman spectroscopy, differential scanning calorimetry, and inelastic neutron scattering measurements. A direct comparison of the models with those obtained from the Wooten-Winer-Weaire (WWW) approach and from recent high-quality molecular-dynamics simulations is also presented.
Large and realistic models of amorphous silicon
Journal of Non-Crystalline Solids
Amorphous silicon (a-Si) models are analyzed for structural, electronic and vibrational characteristics. Several models of various sizes have been computationally fabricated for this analysis. It is shown that a recently developed structural modeling algorithm known as force-enhanced atomic refinement (FEAR) provides results in agreement with experimental neutron and x-ray diffraction data while producing a total energy below conventional schemes. We also show that a large model (∼ 500 atoms) and a complete basis is necessary to properly describe vibrational and thermal properties. We compute the density for a-Si, and compare with experimental results.
Microstructural analysis of paracrystalline atomistic models of amorphous silicon
Journal of Non-Crystalline Solids, 2006
A detailed microstructural analysis of amorphous silicon is performed by means of a numerical modeling technique. Paracrystalline models of amorphous silicon, first proposed by Treacy, Gibson and Keblinski, have been generated. Nanocrystallites of various sizes and concentrations have been introduced into a continuous random network that was generated with a vacancy model. Using the conjugate gradient method, the structures have been relaxed by minimizing their total strain energy described by the anharmonic Keating model. The computed pair correlation functions of these structural models bring to the fore a unique behavior of the paracrystalline networks in the context of diffraction experiments; they appear amorphous as the continuous random network model. The paracrystalline model remains denser than the crystalline phase, contrary to experimental observations. However, the former is found to be less homogenous than the CRN model, thus giving a satisfactory explanation of the nanoscale fluctuation electron microscopy data reported recently by Treacy and coworkers.
Reverse Monte Carlo modeling of amorphous silicon
Physical Review B, 2004
An implementation of the Reverse Monte Carlo algorithm is presented for the study of amorphous tetrahedral semiconductors. By taking into account a number of constraints that describe the tetrahedral bonding geometry along with the radial distribution function, we construct a model of amorphous silicon using the reverse monte carlo technique. Starting from a completely random configuration, we generate a model of amorphous silicon containing 500 atoms closely reproducing the experimental static structure factor and bond angle distribution and in improved agreement with electronic properties. Comparison is made to existing Reverse Monte Carlo models, and the importance of suitable constraints beside experimental data is stressed.
Structure and physical properties of paracrystalline atomistic models of amorphous silicon
Journal of Applied Physics, 2001
We have examined the structure and physical properties of paracrystalline molecular dynamics models of amorphous silicon. Simulations from these models show qualitative agreement with the results of recent mesoscale fluctuation electron microscopy experiments on amorphous silicon and germanium. Such agreement is not found in simulations from continuous random network models. The paracrystalline models consist of topologically crystalline grains which are strongly strained and a disordered matrix between them. We present extensive structural and topological characterization of the medium range order present in the paracrystalline models and examine their physical properties, such as the vibrational density of states, Raman spectra, and electron density of states. We show by direct simulation that the ratio of the transverse acoustic mode to transverse optical mode intensities I TA /I TO in the vibrational density of states and the Raman spectrum can provide a measure of medium range order. In general, we conclude that the current paracrystalline models are a good qualitative representation of the paracrystalline structures observed in the experiment and thus provide guidelines toward understanding structure and properties of medium-range-ordered structures of amorphous semiconductors as well as other amorphous materials.
Thermodynamic properties of amorphous silicon via tight binding simulations
2000
An atomic-scale structure of amorphous silicon, generated by reverse Monte Carlo, has been used as a starting con®guration for ®nite temperature molecular dynamics simulations performed by an orthogonal tight binding Hamiltonian. Structural, dynamic, elastic and electronic structure properties have been investigated in the range of temperatures up to and above the melting transition. The amorphous silicon structure undergoes a melting transition at a temperature sensibly smaller than that of the crystalline structure. Above this temperature, the structure has the same properties of an under-cooled liquid and it has a metallic behavior. Ó
Self-organization and Size Effects in Amorphous Silicon
Springer Series in Materials Science 205
Self-organization and size effects in amorphous silicon have been investigated by modelling of the structure at nanoscale. The size effect related to the disorder in silicon is treated by the free energy balance in nanometric clusters using valence force field theory. The computed structural and energetical parameters of three continuous random network (CRN) models of amorphous silicon with 2,052, 156 and 155 atoms are compared with the experimental values. In order to show the importance of the interfaces between different a-Si clusters, two networks of 200 and 205 atoms were modelled separately and then linked using an amorphous and a crystalline interface. Also the voids in the a-Si clusters are investigated.