First-principles data for solid-solution strengthening of magnesium: From geometry and chemistry to properties

Solid-solution strengthening results from solutes impeding the glide of dislocations. Existing theories of strength rely on solute-dislocation interactions, but do not consider dislocation core structures, which need an accurate treatment of chemical bonding. Here, we focus on strengthening of Mg, the lightest of all structural metals and a promising replacement for heavier steel and aluminum alloys. Elasticity theory, which is commonly used to predict the requisite solute-dislocation interaction energetics, is replaced with quantum-mechanical first-principles calculations to construct a predictive mesoscale model for solute strengthening of Mg. Results for 29 different solutes are displayed in a "strengthening design map" as a function of solute misfits that quantify volumetric strain and slip effects. Our strengthening model is validated with available experimental data for several solutes, including Al and Zn, the two most common solutes in Mg. These new results highlight the ability of quantum-mechanical first-principles calculations to predict complex material properties such as strength.Comment: 9 pages, 7 figures, 2 table

Adaptive efficient compression of genomes

Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. However, memory requirements of the current algorithms are high and run times often are slow. In this paper, we propose an adaptive, parallel and highly efficient referential sequence compression method which allows fine-tuning of the trade-off between required memory and compression speed. When using 12 MB of memory, our method is for human genomes on-par with the best previous algorithms in terms of compression ratio (400:1) and compression speed. In contrast, it compresses a complete human genome in just 11 seconds when provided with 9 GB of main memory, which is almost three times faster than the best competitor while using less main memory

Enabling QM-accurate simulation of dislocation motion in γ−Ni and α−Fe using a hybrid multiscale approach

We present an extension of the ‘learn on the fly’ method to the study of the motion of dislocations in metallic systems, developed with the aim of producing information-efficient force models that can be systematically validated against reference QM calculations. Nye tensor analysis is used to dynamically track the quantum region centered at the core of a dislocation, thus enabling quantum mechanics/molecular mechanics simulations. The technique is used to study the motion of screw dislocations in Ni-Al systems, relevant to plastic deformation in Ni-based alloys, at a variety of temperature/strain conditions. These simulations reveal only a moderate spacing ( ∼ 5 Å ) between Shockley partial dislocations, at variance with the predictions of traditional molecular dynamics (MD) simulation using interatomic potentials, which yields a much larger spacing in the high stress regime. The discrepancy can be rationalized in terms of the elastic properties of an hcp crystal, which influence the behavior of the stacking fault region between Shockley partial dislocations. The transferability of this technique to more challenging systems is addressed, focusing on the expected accuracy of such calculations. The bcc α − Fe phase is a prime example, as its magnetic properties at the open surfaces make it particularly challenging for embedding-based QM/MM techniques. Our tests reveal that high accuracy can still be obtained at the core of a dislocation, albeit at a significant computational cost for fully converged results. However, we find this cost can be reduced by using a machine learning approach to progressively reduce the rate of expensive QM calculations required during the dynamical simulations, as the size of the QM database increases

Complex Mechanical Properties of Steel

Whereas considerable progress has been reported on the quantitative estimation of the microstructure of steels as a function of most of the important determining variables, it remains the case that it is impossible to calculate all but the simplest of mechanical properties given a comprehensive description of the structure at all conceivable scales. Properties which are important but fall into this category are impact toughness, fatigue, creep and combinations of these phenomena. The work presented in this thesis is an attempt to progress in this area of complex mechanical properties in the context of steels, although the outcomes may be more widely applied. The approach used relies on the creation of physically meaningful models based on the neural network and genetic programming techniques. It appears that the hot–strength, of ferritic steels used in the power plant industry, diminishes in concert with the dependence of solid solution strengthening on temperature, until a critical temperature is reached where it is believed that climb processes begin to contribute. It is demonstrated that in this latter regime, the slope of the hot–strength versus temperature plot is identical to that of creep rupture–strength versus temperature. This significant outcome can help dramatically reduce the requirement for expensive creep testing. Similarly, a model created to estimate the fatigue crack growth rates for a wide range of ferritic and austenitic steels on the basis of static mechanical data has the remarkable outcome that it applies without modification to nickel based superalloys and titanium alloys. It has therefore been possible to estimate blindly the fatigue performance of alloys whose chemical composition is not known. Residual stress is a very complex phenomenon especially in bearings due to the Hertzian contact which takes place. A model has been developed that is able to quantify the residual stress distribution, under the raceway of martensitic ball bearings, using the running conditions. It is evident that a well–formulated neural network model can not only be extrapolated even beyond material type, but can reveal physical relationships which are found to be informative and useful in practice

Procedure to construct a multi-scale coarse-grained model of DNA-coated colloids from experimental data

We present a quantitative, multi-scale coarse-grained model of DNA coated colloids. The parameters of this model are transferable and are solely based on experimental data. As a test case, we focus on nano-sized colloids carrying single-stranded DNA strands of length comparable to the colloids' size. We show that in this regime, the common theoretical approach of assuming pairwise additivity of the colloidal pair interactions leads to quantitatively and sometimes even qualitatively wrong predictions of the phase behaviour of DNA-grafted colloids. Comparing to experimental data, we find that our coarse-grained model correctly predicts the equilibrium structure and melting temperature of the formed solids. Due to limited experimental information on the persistence length of single-stranded DNA, some quantitative discrepancies are found in the prediction of spatial quantities. With the availability of better experimental data, the present approach provides a path for the rational design of DNA-functionalised building blocks that can self-assemble in complex, three-dimensional structures.Comment: 17 pages, 10 figures; to be published in Soft Matte

Vacancies and dopants in two-dimensional tin monoxide: An ab initio study

Layered tin monoxide (SnO) offers an exciting two-dimensional (2D) semiconducting system with great technological potential for next-generation electronics and photocatalytic applications. Using a combination of first-principles simulations and strain field analysis, this study investigates the structural dynamics of oxygen (O) vacancies in monolayer SnO and their functionalization by complementary lightweight dopants, namely C, Si, N, P, S, F, Cl, H and H$_{2}$. Our results show that O vacancies are the dominant native defect under Sn-rich growth conditions with active diffusion characteristics that are comparable to that of graphene vacancies. Depending on the choice of substitutional species and its concentration within the material, significant opportunities are revealed in the doped-SnO system for facilitating $n$/$p$-type tendencies, work function reduction, and metallization of the monolayer. N and F dopants are found to demonstrate superior mechanical compatibility with the host lattice, with F being especially likely to take part in substitution and lead to degenerately doped phases with high open-air stability. The findings reported here suggest that post-growth filling of O vacancies in Sn-rich conditions presents a viable channel for doping 2D tin monoxide, opening up new avenues in harnessing defect-engineered SnO nanostructures for emergent technologies

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware

The complexity landscape of viral genomes

Background Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes' organization, relation, and fundamental characteristics. Results This work provides a comprehensive landscape of the viral genome's complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. Conclusions This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes' organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.Peer reviewe