In this paper, we propose an efficient implementation of combining Dynamical Mean field theory (DMFT) with electronic structure calculation based on the local density approximation (LDA). The pseudo-potential-plane-wave method is used in the LDA part
, which makes it possible to be applied to large systems. The full loop self consistency of the charge density has been reached in our implementation which allows us to compute the total energy related properties. The procedure of LDA+DMFT is introduced in detail with a complete flow chart. We have also applied our code to study the electronic structure of several typical strong correlated materials, including Cerium, Americium and NiO. Our results fit quite well with both the experimental data and previous studies.
In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed al
gorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.
