Phase transition to the state with nonzero average helicity in dense neutron matter

The possibility of the appearance of the states with a nonzero average helicity in neutron matter is studied in the model with the Skyrme effective interaction. By providing the analysis of the self-consistent equations at zero temperature, it is shown that neutron matter with the Skyrme BSk18 effective force undergoes at high densities a phase transition to the state in which the degeneracy with respect to helicity of neutrons is spontaneously removed.Comment: 4 pages, 3 figures; v2: journal versio

STUDY OF EXISTING MODES OF REDUCTIONS DURING PRODUCTION OF RAILWAY AXLES

Study of existing modes of reductions during production of railway axles

STUDY OF EXISTING MODES OF REDUCTIONS DURING PRODUCTION OF RAILWAY AXLES

Study of existing modes of reductions during production of railway axles

Effects of different deoxidization methods on high-temperature physical properties of high-strength low-alloy steels

This study was aimed at examining the effects of different deoxidization methods on the physical properties of metallic melts by measuring the changes in the kinematic viscosity, electrical resistivity, surface tension, and density of the metallurgical melts during the heating and cooling processes. Our results indicate that high-temperature physical properties are consistently affected by specific elements and compounds. © 2020 Ke-lin Zhang et al., published by De Gruyter 2020.2019010701011382National Natural Science Foundation of China, NSFC: U1532268, 51671149The authors gratefully acknowledge support from the National Natural Science Foundation of China (U1532268, 51671149), Wuhan Science and Technology Program (Grant No. 2019010701011382), and the 111 project

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

<p>There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.</p

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy

Spontaneous breaking of rotational symmetry in superconductors

We show that homogeneous superconductors with broken spin/isospin symmetry lower their energy via a transition to a novel superconducting state where the Fermi-surfaces are deformed to a quasi-ellipsoidal form at zero total momentum of Cooper pairs. In this state, the gain in the condensation energy of the pairs dominates over the loss in the kinetic energy caused by the lowest order (quadrupole) deformation of Fermi-surfaces from the spherically symmetric form. There are two energy minima in general, corresponding to the deformations of the Fermi-spheres into either prolate or oblate forms. The phase transition from spherically symmetric state to the superconducting state with broken rotational symmetry is of the first order.Comment: 5 pages, including 3 figures, published versio

Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides

Motivation: Non-ribosomal peptide synthetases (NRPSs) are modular enzymatic machines that catalyze the ribosome-independent production of structurally complex small peptides, many of which have important clinical applications as antibiotics, antifungals and anti-cancer agents. Several groups have tried to expand natural product diversity by intermixing different NRPS modules to create synthetic peptides. This approach has not been as successful as anticipated, suggesting that these modules are not fully interchangeable. Results: We explored whether Inter-Modular Linkers (IMLs) impact the ability of NRPS modules to communicate during the synthesis of NRPs. We developed a parser to extract 39 804 IMLs from both well annotated and putative NRPS biosynthetic gene clusters from 39 232 bacterial genomes and established the first IMLs database. We analyzed these IMLs and identified a striking relationship between IMLs and the amino acid substrates of their adjacent modules. More than 92% of the identified IMLs connect modules that activate a particular pair of substrates, suggesting that significant specificity is embedded within these sequences. We therefore propose that incorporating the correct IML is critical when attempting combinatorial biosynthesis of novel NRPS. Availability and implementation: The IMLs database as well as the NRPS-Parser have been made available on the web at https://nrps-linker.unc.edu. The entire source code of the project is hosted in GitHub repository (https://github.com/SWFarag/nrps-linker). Supplementary information: Supplementary data are available at Bioinformatics online

Competition of ferromagnetic and antiferromagnetic spin ordering in nuclear matter

In the framework of a Fermi liquid theory it is considered the possibility of ferromagnetic and antiferromagnetic phase transitions in symmetric nuclear matter with Skyrme effective interaction. The zero temperature dependence of ferromagnetic and antiferromagnetic spin polarization parameters as functions of density is found for SkM$^*$, SGII effective forces. It is shown that in the density domain, where both type of solutions of self--consistent equations exist, ferromagnetic spin state is more preferable than antiferromagnetic one.Comment: 9p., 3 figure

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure