BerryEasy: a GPU enabled python package for diagnosis ofnth-order and spin-resolved topology in the presence of fields and effects

Multiple software packages currently exist for the computation of bulk topological invariants in both idealized tight-binding models and realistic Wannier tight-binding models derived from density functional theory. Currently, only one package is capable of computing nested Wilson loops and spin-resolved Wilson loops. These state-of-the-art techniques are vital for accurate analysis of band topology. In this paper we introduce BerryEasy, a python package harnessing the speed of graphical processing units to allow for efficient topological analysis of supercells in the presence of disorder and impurities. Moreover, the BerryEasy package has built-in functionality to accommodate use of realistic many-band tight-binding models derived from first-principles.

Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation

Controlling the magnetic state of two-dimensional (2D) materials is crucial for spintronics. By employing data-mining and autonomous density functional theory calculations, we demonstrate the switching of magnetic properties of 2D non-van der Waals materials upon hydrogen passivation. The magnetic configurations are tuned to states with flipped and enhanced moments. For 2D CdTiO3─a diamagnetic compound in the pristine case─we observe an onset of ferromagnetism upon hydrogenation. Further investigation of the magnetization density of the pristine and passivated systems provides a detailed analysis of modified local spin symmetries and the emergence of ferromagnetism. Our results indicate that selective surface passivation is a powerful tool for tailoring magnetic properties of nanomaterials, such as non-vdW 2D compounds.

Constitutive Equations for Analyzing Stress Relaxation and Creep of Viscoelastic Materials Based on Standard Linear Solid Model Derived with Finite Loading Rate

The viscoelastic properties of materials such as polymers can be quantitatively evaluated by measuring and analyzing the viscoelastic behaviors such as stress relaxation and creep. The standard linear solid model is a classical and commonly used mathematical model for analyzing stress relaxation and creep behaviors. Traditionally, the constitutive equations for analyzing stress relaxation and creep behaviors based on the standard linear solid model are derived using the assumption that the loading is a step function, implying that the loading rate used in the loading process of stress relaxation and creep tests is infinite. Using such constitutive equations may cause significant errors in analyses since the loading rate must be finite (no matter how fast it is) in a real stress relaxation or creep experiment. The purpose of this paper is to introduce the constitutive equations for analyzing stress relaxation and creep behaviors based on the standard linear solid model derived with a finite loading rate. The finite element computational simulation results demonstrate that the constitutive equations derived with a finite loading rate can produce accurate results in the evaluation of all viscoelastic parameters regardless of the loading rate in most cases. It is recommended that the constitutive equations derived with a finite loading rate should replace the traditional ones derived with an infinite loading rate to analyze stress relaxation and creep behaviors for quantitatively evaluating the viscoelastic properties of materials.

NEUTRON: Neural particle swarm optimization for material-aware inverse design of structural color

Designing optical structures for generating structural colors is challenging because of the complex relationship between the optical structures and the color perceived by human eyes. Machine learning-based approaches have been developed to expedite this design process. However, existing methods solely focus on structural parameters of the optical design, which could lead to suboptimal color generation because of the inability to optimize the selection of materials. To address this issue, an approach known as Neural Particle Swarm Optimization is proposed in this paper. The proposed method achieves high design accuracy and efficiency on two structural color design tasks; the first task is designing environment-friendly alternatives to chrome coatings, and the second task concerns reconstructing pictures with multilayer optical thin films. Several designs that could replace chrome coatings have been discovered; pictures with more than 200,000 pixels and thousands of unique colors can be accurately reconstructed in a few hours.

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NEUTRON combines machine learning and optimization for structural color designs

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On two benchmark tasks, NEUTRON demonstrates high design accuracy and efficiency

Universal machine learning framework for defect predictions in zinc blende semiconductors

We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III–V, and II–VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities. Optimized kernel ridge, Gaussian process, random forest, and neural network regression models are applied to screen impurities with lower formation energy than dominant native defects in all compounds.

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Large computational dataset of defect properties in semiconductors is developed

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Regression algorithms are used to train predictive models for defect properties

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Best models are used for high-throughput prediction and screening

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Lists of low energy “dominating” impurities are generated

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. The power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

Data-Driven Quest for Two-Dimensional Non-van der Waals Materials

Two-dimensional (2D) materials are frequently associated with the sheets forming bulk layered compounds bonded by van der Waals (vdW) forces. The anisotropy and weak interaction between the sheets have also been the main criteria in the computational search for new 2D systems, predicting ∼2000 exfoliable compounds. However, some representatives of a new type of non-vdW 2D systems, without layered 3D analogues, were recently manufactured. For this novel materials class, data-driven design principles are still missing. Here, we outline a set of 8 binary and 20 ternary candidates by filtering the AFLOW-ICSD database according to structural prototypes. The oxidation state of the surface cations regulates the exfoliation energy with low oxidation numbers leading to weak bonding─a useful descriptor to obtain novel 2D materials also providing clear guidelines for experiments. A vast range of appealing electronic, optical, and magnetic properties make the candidates attractive for various applications and particularly spintronics.

Calculating Filament Feed in the Fused Deposition Modeling Process to Correctly Print Continuous Fiber Composites in Curved Paths

Fused deposition modeling (FDM) is a popular additive manufacturing (AM) method that has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the filament feed presented in the production G-code. The filament feed is calculated according to the layer height, the extrusion width, and the length of the printing path. All required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs placed next to each other at a fixed distance. In special curved paths, the distance of adjacent paths is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. The creation of a special computer code that can be used to make various functions of curved paths was investigated in this study. The filament feed parameter was also studied in detail. Next, by introducing a correction technique, the filament feed was changed on the curved path to uniformly distribute the polymer material. Variable-stiffness composite samples consisting of curved fibers can be produced with the proposed method. The high quality of the printed samples confirms the suggested code and technique.

Advances in research on deformation and recrystallization for the development of high-functional steels

In order to exploit full potential of materials, it is necessary to fundamentally control their microstructures through grain refinement and orientation. Deformation and recrystallization are means to control the microstructure. Some recent examples of research on deformation and recrystallization which make use of advanced analytical techniques and computational materials science are examined and current limitations are identified. Finally, the potential for future developments is considered with respect to the unresolved technical problems that must be addressed as part of the development of new steels.

Taxifolin tubes: crystal engineering and characteristics

Taxifolin, also known as dihydroquercetin, is the major flavonoid in larch wood. It is well known as an antioxidant and a bioactive substance. Taxifolin as an active pharmaceutical ingredient is produced industrially in crystalline form during the processing of larch wood. Some information is available on nano- and microstructured particles of taxifolin. This paper reports on the generation of a new form of taxifolin as microtubes. These self-assembled tubes were obtained from raw taxifolin by crystal engineering with urea at ambient temperature and pressure. The parameters of temperature, pH value, molar ratio of taxifolin and urea, and time duration were optimized for yield enhancement of the microtubes. The water solubility and melting point of the new form of taxifolin were established. The microtubes were characterized by X-ray diffraction, X-ray powder diffraction, microscopy, mass spectrometry, ¹H NMR spectroscopy, UV spectroscopy and Fourier transform infrared spectroscopy methods. The experimental results demonstrate that the microtubes and raw taxifolin both exist in crystalline form with the same structure of the crystal unit. However, they are characterized by different morphological and physicochemical properties. Computer simulation was performed to explain the mechanism of the self-assembly process.

Influence of Partially Debonded Interface on Elasticity of Syntactic Foam: A Numerical Study

The effect of interfacial bonding of glass hollow microspheres and a polymer matrix on the elastic properties of syntactic foam was investigated using representative volume element (RVE) models, including partially debonded interfaces. Finite element analysis, with models having different debonding geometries, was performed to numerically estimate the elastic behavior of the models. The models consisted of bonded and debonded regions of interfaces; the bonded region was treated as the perfectly bonded interface, while the Coulomb friction model was used to describe the debonded region with a small friction coefficient. The changes in the tensile and compressive moduli of the foams were investigated in terms of the degree of interfacial debonding and debonding geometry.

Novel phase diagram behavior and materials design in heterostructural semiconductor alloys

Structure and composition control the behavior of materials. Isostructural alloying is historically an extremely successful approach for tuning materials properties, but it is often limited by binodal and spinodal decomposition, which correspond to the thermodynamic solubility limit and the stability against composition fluctuations, respectively. We show that heterostructural alloys can exhibit a markedly increased range of metastable alloy compositions between the binodal and spinodal lines, thereby opening up a vast phase space for novel homogeneous single-phase alloys. We distinguish two types of heterostructural alloys, that is, those between commensurate and incommensurate phases. Because of the structural transition around the critical composition, the properties change in a highly nonlinear or even discontinuous fashion, providing a mechanism for materials design that does not exist in conventional isostructural alloys. The novel phase diagram behavior follows from standard alloy models using mixing enthalpies from first-principles calculations. Thin-film deposition demonstrates the viability of the synthesis of these metastable single-phase domains and validates the computationally predicted phase separation mechanism above the upper temperature bound of the nonequilibrium single-phase region.

Density functional theory in the solid state

Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure-property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program.