Antimicrobial peptide identification using multi-scale convolutional network

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN

Calculations of Magnetic Exchange Interactions in Mott--Hubbard Systems

An efficient method to compute magnetic exchange interactions in systems with strong correlations is introduced. It is based on a magnetic force theorem which evaluates linear response due to rotations of magnetic moments and uses a novel spectral density functional framework combining our exact diagonalization based dynamical mean field and local density functional theories. Applications to spin waves and magnetic transition temperatures of 3d metal mono--oxides as well as high--T_{c} superconductors are in good agreement with experiment

Origin of Low Thermal Conductivity in Nuclear Fuels

Using a novel many-body approach, we report lattice dynamical properties of UO2 and PuO2 and uncover various contributions to their thermal conductivities. Via calculated Grueneisen constants, we show that only longitudinal acoustic modes having large phonon group velocities are efficient heat carriers. Despite the fact that some optical modes also show their velocities which are extremely large, they do not participate in the heat transfer due to their unusual anharmonicity. Ways to improve thermal conductivity in these materials are discussed.Comment: 4 pages, 3 figures, 1 tabl

Anisotropy, Itineracy, and Magnetic Frustration in High-Tc Iron Pnictides

Using first-principle density functional theory calculations combined with insight from a tight-binding representation, dynamical mean field theory, and linear response theory, we have extensively investigated the electronic structures and magnetic interactions of nine ferropnictides representing three different structural classes. The calculated magnetic interactions are found to be short-range, and the nearest ($J_{1a}$) and next-nearest ($J_{2}$) exchange constants follow the universal trend of J_{1a}/2J_{2}\sim 1, despite their itinerant origin and extreme sensitivity to the z-position of As. These results bear on the discussion of itineracy versus magnetic frustration as the key factor in stabilizing the superconducting ground state. The calculated spin wave dispersions show strong magnetic anisotropy in the Fe plane, in contrast to cuprates.Comment: Fig.4 updated: Phys. Rev. Lett (in press

Electronic Correlation and Transport Properties of Nuclear Fuel Materials

Actinide elements, such as uranium and plutonium, and their compounds are best known as nuclear materials. When engineering optimal fuel materials for nuclear power, important thermophysical properties to be considered are melting point and thermal conductivity. Understanding the physics underlying transport phenomena due to electrons and lattice vibrations in actinide systems is a crucial step toward the design of better fuels. Using first principle LDA+DMFT method, we conduct a systematic study on the correlated electronic structures and transport properties of select actinide carbides, nitrides, and oxides, many of which are nuclear fuel materials. We find that different mechanisms, electrons--electron and electron--phonon interactions, are responsible for the transport in the uranium nitride and carbide, the best two fuel materials due to their excellent thermophysical properties. Our findings allow us to make predictions on how to improve their thermal conductivities.Comment: Main article: 5 pages, 3 figures. Supplementary info: 2 pages, 1 figur