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Register a new userStructural Simplicity as a Restraint on the Structure of Amorphous Silicon
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Understanding the structural origins of the properties of amorphous materials remains one of the most important challenges in structural science. In this study we demonstrate that local structural simplicity, embodied by the degree to which atomic environments within a material are similar to each other, is powerful concept for rationalising the structure of canonical amorphous material amorphous silicon (a-Si). We show, by restraining a reverse Monte Carlo refinement against pair distribution function (PDF) data to be simpler, that the simplest model consistent with the PDF is a continuous random network (CRN). A further effect of producing a simple model of a-Si is the generation of a (pseudo)gap in the electronic density of states, suggesting that structural homogeneity drives electronic homogeneity. That this method produces models of a-Si that approach the state-of-the-art without the need for chemically specific restraints (beyond the assumption of homogeneity) suggests that simplicity-based refinement approaches may allow experiment-driven structural modelling techniques to be developed for the wide variety of amorphous semiconductors with strong local order.
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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 next-nearest-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.
Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of machine-learning algorithms to create interatomic potentials. Our best a-Si network is obtained by cooling from the melt in molecular-dynamics simulations, at a rate of 10$^{11}$ K/s (that is, on the 10 ns timescale). This structure shows a defect concentration of below 2% and agrees with experiments regarding excess energies, diffraction data, as well as $^{29}$Si solid-state NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning interatomic potentials for elucidating accurate structures and properties of amorphous functional materials.
Using a combination of quantum and classical computational approaches, we model the electronic structure in amorphous silicon in order gain understanding of the microscopic atomic configurations responsible for light induced degradation of solar cells. We demonstrate that regions of strained silicon bonds could be as important as dangling bonds for creating traps for charge carriers. Further, our results show that defects are preferentially formed when a region in the amorphous silicon contains a hole and a light-induced excitation. These results agree with the puzzling dependencies on temperature, time, and pressure observed experimentally.
The nanostructure of hydrogenated amorphous silicon (a Si:H) is studied by a combination of small-angle X-ray (SAXS) and neutron scattering (SANS) with a spatial resolution of 0.8 nm. The a-Si:H materials were deposited using a range of widely varied conditions and are representative for this class of materials. We identify two different phases which are embedded in the a-Si:H matrix and quantified both according to their scattering cross-sections. First, 1.2 nm sized voids (multivacancies with more than 10 missing atoms) which form a superlattice with 1.6 nm void-to-void distance are detected. The voids are found in concentrations as high as 6*10^19 ccm in a-Si:H material that is deposited at a high rate. Second, dense ordered domains (DOD) that are depleted of hydrogen with 1 nm average diameter are found. The DOD tend to form 10-15 nm sized aggregates and are largely found in all a-Si:H materials considered here. These quantitative findings make it possible to understand the complex correlation between structure and electronic properties of a-Si:H and directly link them to the light-induced formation of defects. Finally, a structural model is derived, which verifies theoretical predictions about the nanostructure of a-Si:H.
By means of theoretical modeling and experimental synthesis and characterization, we investigate the structural properties of amorphous Zr-Si-C. Two chemical compositions are selected, Zr0.31Si0.29C0.40 and Zr0.60Si0.33C0.07. The amorphous structures are generated in the theoretical part of our work, by the stochastic quenching (SQ) method, and detailed comparison is made as regards structure and density of the experimentally synthesized films. These films are analyzed experimentally using X-ray absorption spectroscopy, transmission electron microscopy and X-ray diffraction. Our results demonstrate for the first time a remarkable agreement between theory and experiment concerning bond distances and atomic coordination of this complex amorphous metal carbide. The demonstrated power of the SQ method opens up avenues for theoretical predictions of amorphous materials in general.
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