We present an information-based total-energy optimization method to produce nearly defect-free structural models of amorphous silicon. Using geometrical, structural, and topological information from disordered tetrahedral networks, we have shown that it is possible to generate structural configurations of amorphous silicon, which are superior than the models obtained from conventional reverse Monte Carlo and molecular dynamics simulations. The new data-driven hybrid approach presented here is capable of producing atomistic models with structural and electronic properties which are on a par with those obtained from the modified Wooten-Winer-Weaire (WWW) models of amorphous silicon. Structural, electronic, and thermodynamic properties of the hybrid models are compared with the best dynamical models obtained from using machine-intelligence-based algorithms and efficient classical molecular dynamics simulations, reported in the recent literature. We have shown that, together with the WWW models, our hybrid models represent one of the best structural models so far produced by total-energy-based Monte Carlo methods in conjunction with experimental diffraction data.