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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.

A set of embedded atom model (EAM) interatomic potentials was developed to represent highly idealized face-centered cubic (FCC) mixtures of Fe–Ni–Cr–Co–Al at near-equiatomic compositions. Potential functions for the transition metals and their crossed interactions are taken from our previous work for Fe–Ni–Cr–Co–Cu [D. Farkas and A. Caro: J. Mater. Res. 33 (19), 3218–3225, 2018], while cross-pair interactions involving Al were developed using a mix of the component pair functions fitted to known intermetallic properties. The resulting heats of mixing of all binary equiatomic random FCC mixtures not containing Al is low, but significant short-range ordering appears in those containing Al, driven by a large atomic size difference. The potentials are utilized to predict the relative stability of FCC quinary mixtures, as well as ordered L12 and B2 phases as a function of Al content. These predictions are in qualitative agreement with experiments. This interatomic potential set is developed to resemble but not model precisely the properties of this complex system, aiming at providing a tool to explore the consequences of the addition of a large size-misfit component into a high entropy mixture that develops multiphase microstructures.

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.

This article presents a brief review of our case studies of data-driven Integrated Computational Materials Engineering (ICME) for intelligently discovering advanced structural metal materials, including light-weight materials (Ti, Mg, and Al alloys), refractory high-entropy alloys, and superalloys. The basic bonding in terms of topology and electronic structures is recommended to be considered as the building blocks/units constructing the microstructures of advanced materials. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of materials but also reveal the fundamental strengthening/embrittlement mechanisms and the local phase transformations of planar defects, paving a path in accelerating the development of advanced metal materials via interfacial engineering. Perspectives on the knowledge-based modeling/simulations, machine-learning knowledge base, platform, and next-generation workforce for sustainable ecosystem of ICME are highlighted, thus to call for more duty on the developments of advanced structural metal materials and enhancement of research productivity and collaboration.

Dissolution of oxides in aqueous solutions is fundamentally important for a range of applications and a critical process that determines the chemical durability of industrial ceramics, the performance of nuclear waste forms, and the chemical weathering of minerals. The thermodynamic equilibrium and kinetics of dissolution reactions are key to determining the rate at which oxides dissolve. The increase in collaborative research across disciplines in materials research necessitates a common background to tackle shared scientific problems across different fields. This review selectively examines the fundamentals of dissolution theories that have been developed in chemistry, geochemistry, and materials science, and assembles them into a single collective document for the broader materials science community. Applications of the theories are highlighted using examples from specific areas, but can be similarly applied to other areas. Challenges and future research needs for a predictive-level understanding are discussed in light of the current literature.

We apply the first-principles calculations to investigate the structure, mechanical, and thermodynamic properties of WB12 and TiB12 under high pressure (0–100 GPa). The calculated results show that WB12 and TiB12 are thermodynamically stable at the 0 GPa or high pressure. WB12 is more thermodynamically stable than TiB12. In particular, the calculated Vickers hardness of WB12 and TiB12 at the ground state is 29.9 GPa and 43.2 GPa, respectively, indicating that TiB12 is a potential superhard material. With increasing pressure, the calculated elastic modulus of WB12 and TiB12 increases gradually. The calculated electronic structure shows that the high Vickers hardness and elastic properties of WB12 and TiB12 derive from the 3D network B–B covalent bonds. In addition, the calculated Debye temperature at the ground state is 927 K for WB12 and 1339 K for TiB12, respectively. With increasing pressure, the calculated Debye temperature of WB12 and TiB12 increases gradually. Our work shows that TiB12 not only exhibits high hardness but also shows better thermodynamic properties in comparison with WB12.

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

This study examines the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load–displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hypothetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.

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.

Iridium (Ir) has an extremely high melting point (2443 °C), high chemical stability and is one of the most promising high-temperature materials. However, Ir is more difficult to process compared with other face-centered cubic metals, such as Ni and Al, which limits its applications. To solve this problem, we study the effect of 32 alloying elements (X) on stacking fault energy of dilute Ir-based alloys generated by shear deformation using the first-principles calculations. The investigation reveals that there are many alloying elements studied herein decrease the stacking fault energy of face-centered cubic (fcc) Ir, and the most effective element in reducing stacking fault energy of fcc Ir is Zn. The microscopic mechanism is caused by electron redistribution in the local stacking fault area. These results are expected to provide valuable guidance for the further design and application of Ir-based alloys.