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Register a new userQuantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
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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.
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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.
We investigate exponential families of random graph distributions as a framework for systematic quantification of structure in networks. In this paper we restrict ourselves to undirected unlabeled graphs. For these graphs, the counts of subgraphs with no more than k links are a sufficient statistics for the exponential families of graphs with interactions between at most k links. In this framework we investigate the dependencies between several observables commonly used to quantify structure in networks, such as the degree distribution, cluster and assortativity coefficients.
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.
Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces determine the strengthening capacities in high temperature. By means of high resolution scanning transmission electron microscopy (HRSTEM) and 3D atom probe (3DAP) tomography, Srinivasan et al. proposed a new point that in nickel base superalloys there exist two different interfacial widths across the {gamma}/{gamma} interface, one corresponding to an order-disorder transition, and the other to the composition transition. We argue about this conclusion in this comment.
Recently amorphous oxide semiconductors (AOS) have gained commercial interest due to their low-temperature processability, high mobility and areal uniformity for display backplanes and other large area applications. A multi-cation amorphous oxide (a-IGZO) has been researched extensively and is now being used in commercial applications. It is proposed in the literature that overlapping In-5s orbitals form the conduction path and the carrier mobility is limited due to the presence of multiple cations which create a potential barrier for the electronic transport in a-IGZO semiconductors. A multi-anion approach towards amorphous semiconductors has been suggested to overcome this limitation and has been shown to achieve hall mobilities up to an order of magnitude higher compared to multi-cation amorphous semiconductors. In the present work, we compare the electronic structure and electronic transport in a multi-cation amorphous semiconductor, a-IGZO and a multi-anion amorphous semiconductor, a-ZnON using computational methods. Our results show that in a-IGZO, the carrier transport path is through the overlap of outer s-orbitals of mixed cations and in a-ZnON, the transport path is formed by the overlap of Zn-4s orbitals, which is the only type of metal cation present. We also show that for multi-component ionic amorphous semiconductors, electron transport can be explained in terms of orbital overlap integral which can be calculated from structural information and has a direct correlation with the carrier effective mass which is calculated using computationally expensive first principle DFT methods.
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