Information-driven inverse approach to disordered solids: Applications to amorphous silicon

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 noncrystalline 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 an information-driven 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 approach and from recent high-quality molecular-dynamics simulations is also presented.