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Rare-earth silicates are the current standard material for use as environmental barrier coatings for SiC-based ceramic matrix composites as hot-section components in gas-turbine engines. Expanding the design space to all available rare-earth elements to facilitate optimizing functionality requires an understanding of systematic trends in $RE_2$Si$_2$O$_7$ properties. In this work, we combine first-principles calculations with experimental measurements of Young's modulus, coefficient of thermal expansion, and thermal conductivity for a range of different $RE_2$Si$_2$O$_7$ compositions and phases. Clear trends are observed in these properties as a function of the radius of the rare-earth cation. In the case of Young's modulus and thermal expansion, these trends also hold for multi-component systems; while the thermal conductivity of multi-component systems is noticeably lower, indicating the potential of such materials to also act as thermal barriers. These results provide design rules for developing new thermal and environmental barrier coatings with stiffness and thermal expansion engineered to match that of the substrate, while simultaneously having reduced thermal conductivity., 10 pages, 5 figures

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and enhanced data generation have created a fertile ground for computational materials science. Pairing machine learning and alloys has proven to be particularly instrumental in pushing progress in a wide variety of materials, including metallic glasses, high-entropy alloys, shape-memory alloys, magnets, superalloys, catalysts and structural materials. This Review examines the present state of machine-learning-driven alloy research, discusses the approaches and applications in the field and summarizes theoretical predictions and experimental validations. We foresee that the partnership between machine learning and alloys will lead to the design of new and improved systems. Machine learning is enabling a metallurgical renaissance. This Review discusses recent progress in representations, descriptors and interatomic potentials, overviewing metallic glasses, high-entropy alloys, superalloys and shape-memory alloys, magnets and catalysts, and the prediction of mechanical and thermal properties.

Disordered materials are attracting considerable attention because of their enhanced properties compared to their ordered analogs, making them particularly suitable for high-temperature applications. The feasibility of incorporating these materials into new devices depends on a variety of thermophysical properties. Among them, thermal expansion is critical to device stability, especially in multi-component systems. Its calculation, however, is quite challenging for materials with substitutional disorder, hindering computational screenings. In this work, we introduce QH-POCC to leverage the local tile-expansion of disorder. This method provides an effective partial partition function to calculate thermomechanical properties of substitutionally disordered compounds in the quasi-harmonic approximation. Two systems, AuCu3 and CdMg3, the latter a candidate for long-period superstructures at low temperature, are used to validate the methodology by comparing the calculated values of the coefficient of thermal expansion and isobaric heat capacity with experiment, demonstrating that QH-POCC is a promising approach to study thermomechanical properties of disordered systems., 9 pages, 3 figures

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material., Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.

A metallic covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an $sp^3$ carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene chains. The distance between these $sp^2$ carbon atoms renders the phase metallic, and yields two well-nested nearly parallel bands that span the Fermi level. Calculations show that this phase is a conventional superconductor, with the motions of the $sp^2$ carbons being key contributors to the electron phonon coupling. The $sp^3$ carbon atoms impart superior mechanical properties, with a predicted Vickers hardness of 48~GPa. This phase, metastable at ambient conditions, could be made via cold compression of graphite to 40~GPa. A family of multifunctional materials with tunable superconducting and mechanical properties could be derived from this phase by varying the $sp^2$ versus $sp^3$ carbon content and by doping.

Disordered multicomponent systems, occupying the mostly uncharted centres of phase diagrams, were proposed in 2004 as innovative materials with promising applications. The idea was to maximize the configurational entropy to stabilize (near) equimolar mixtures and achieve more robust systems, which became known as high-entropy materials. Initial research focused mainly on metal alloys and nitride films. In 2015, entropy stabilization was demonstrated in a mixture of oxides. Other high-entropy disordered ceramics rapidly followed, stimulating the addition of more components to obtain materials expressing a blend of properties, often highly enhanced. The systems were soon proven to be useful in wide-ranging technologies, including thermal barrier coatings, thermoelectrics, catalysts, batteries and wear-resistant and corrosion-resistant coatings. In this Review, we discuss the current state of the disordered ceramics field by examining the applications and the high-entropy features fuelling them, covering both theoretical predictions and experimental results. The influence of entropy is unavoidable and can no longer be ignored. In the space of ceramics, it leads to new materials that, both as bulk and thin films, will play important roles in technology in the decades to come. The valuable combination of disorder and non-metallic bonding gives rise to high-entropy ceramics. This Review explores the structures and chemistries of these versatile materials, and their applications in catalysis, water splitting, energy storage, thermoelectricity and thermal, environmental and wear protection.

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets - facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems, Comment: 47 pages, 14 figures

Recent research in materials science opens exciting perspectives to design novel quantum materials and devices, but it calls for quantitative predictions of properties which are not accessible in standard first principles packages. PAOFLOW , is a software tool that constructs tight-binding Hamiltonians from self-consistent electronic wavefunctions by projecting onto a set of atomic orbitals. The electronic structure provides numerous materials properties that otherwise would have to be calculated via phenomenological models. In this paper, we describe recent re-design of the code as well as the new features and improvements in performance. In particular, we have implemented symmetry operations for unfolding equivalent k-points, which drastically reduces the runtime requirements of first principles calculations, and we have provided internal routines of projections onto atomic orbitals enabling generation of real space atomic orbitals. Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models. Importantly, PAOFLOW has been now converted into a Python package, and is streamlined for use directly within other Python codes. The new object oriented design treats PAOFLOW ’s computational routines as class methods, providing an API for explicit control of each calculation.

Two-dimensional (2D) materials are frequently associated with the sheets which form 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, which predicted about 2000 exfoliable compounds. However, several representatives of a new type of non-vdW 2D systems, such as hematene or ilmenene, which have no layered 3D analogues, and which, unlike, e.g. silicene, do not need to strongly interact with the substrate to be stable, were recently manufactured. The family of non-vdW 2D materials is an attractive playground for data-driven high-throughput approaches as computational design principles are still missing. Here, we outline a new set of 8 binary and 20 ternary candidates by filtering the AFLOW-ICSD database according to the structural prototype of the first two template systems realized in experiment. All materials show a strong structural relaxation upon confinement to 2D which is essential to correctly estimate the bonding strength between facets. The oxidation state of the cations at the surface of the sheets is demonstrated to regulate the inter-facet binding energy with low oxidation states leading to weak bonding. We anticipate this descriptor to be highly useful to obtain novel 2D materials, providing clear guidelines for experiments. Our calculations further indicate that the materials exhibit a vast range of appealing electronic, optical and magnetic properties, which is expected to also make them attractive for potential applications particularly in spintronics., 26 pages, 6 figures

High-entropy ceramics are attracting significant interest due to their exceptional chemical stability and physical properties. While configurational entropy descriptors have been successfully implemented to predict their formation and even to discover new materials, the contribution of vibrations to their stability has been contentious. This work unravels the issue by computationally integrating disorder parameterization, phonon modeling, and thermodynamic characterization. Three recently synthesized carbides are used as a testbed: (HfNbTaTiV)C, (HfNbTaTiW)C, and (HfNbTaTiZr)C. It is found that vibrational contributions should not be neglected when precursors or decomposition products have different nearest-neighbor environments from the high-entropy carbide., The contribution of vibrations to the stability of high-entropy ceramics is still controversial. Here the authors computationally integrate disorder parameterization, phonon modelling, and thermodynamic characterization to investigate the role of vibrations to the stability of high-entropy carbides.

At ambient pressure tin transforms from its ground-state semi-metal $\alpha$-Sn (diamond structure) phase to the compact metallic $\beta$-Sn phase at 13$^\circ$C (286K). There may be a further transition to the simple hexagonal $\gamma$-Sn above 450K. These relatively low transition temperatures are due to the small energy differences between the structures, $\approx 20$\,meV/atom between $\alpha$- and $\beta$-Sn. This makes tin an exceptionally sensitive test of the accuracy of density functionals and computational methods. Here we use the high-throughput Automatic-FLOW (AFLOW) method to study the energetics of tin in multiple structures using a variety of density functionals. We look at the successes and deficiencies of each functional. As no functional is completely satisfactory, we look Hubbard U corrections and show that the Coulomb interaction can be chosen to predict the correct phase transition temperature. We also discuss the necessity of testing high-throughput calculations for convergence for systems with small energy differences., Comment: 17 pages

The entropy landscape of high-entropy carbides can be used to understand and predict their structure, properties, and stability. Using first principles calculations, the individual and temperature-dependent contributions of vibrational, electronic, and configurational entropies are analyzed, and compare them qualitatively to the enthalpies of mixing. As an experimental complement, high-entropy carbide thin films are synthesized with high power impulse magnetron sputtering to assess structure and properties. All compositions can be stabilized in the single-phase state despite finite positive, and in some cases substantial, enthalpies of mixing. Density functional theory calculations reveal that configurational entropy dominates the free energy landscape and compensates for the enthalpic penalty, whereas the vibrational and electronic entropies offer negligible contributions. The calculations predict that in many compositions, the single-phase state becomes stable at extremely high temperatures (>3000 K). Consequently, rapid quenching rates are needed to preserve solubility at room temperature and facilitate physical characterization. Physical vapor deposition provides this experimental validation opportunity. The computation/experimental data set generated in this work identifies "valence electron concentration" as an effective descriptor to predict structural and thermodynamic properties of multicomponent carbides and educate new formulation selections.

Synthetic copper sulfides have emerged as promising nontoxic and low-cost materials for thermoelectric power generation in low-grade waste heat recovery systems. Similarly to tetrahedrite and colus...

Serpentine clay minerals are found in many geological settings. The rich diversity, both in chemical composition and crystal structure, alters the elastic behavior of clay rocks significantly, thus modifying seismic and sonic responses to shaley sequences. Computation of the elastic properties is a useful tool to characterize this diversity. In this paper we use first principles methods to compare the mechanical properties of lizardite Mg3(Si2O5)(OH)4, a polymorph of serpentine family, with the new compounds derived by substituting Mg ions with isovalent elements from different chemical groups. New compounds are first selected according to chemical and geometrical stability criteria, then full elastic tensors, bulk and shear modulii, and acoustic velocities are obtained. Overall, the new compounds have a lower anisotropy and are less resistant to mechanical deformation compared to the prototype, thus providing valuable information regarding chemical composition and mechanical properties in these systems.

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed., 25 pages, 7 figures, chapter in a book

The computational design of materials with ionic bonds poses a critical challenge to thermodynamic modeling since density functional theory yields inaccurate predictions of their formation enthalpies. Progress requires leveraging physically insightful correction methods. The recently introduced coordination corrected enthalpies (CCE) method delivers accurate formation enthalpies with mean absolute errors close to room temperature thermal energy, i.e., 25meV/atom. The CCE scheme, depending on the number of cation-anion bonds and oxidation state of the cation, requires an automated analysis of the system to determine and apply the correction. Here, we present AFLOW-CCE -- our implementation of CCE into the AFLOW framework for computational materials design. It features a command line tool, a web interface and a Python environment. The workflow includes a structural analysis, automatically determines oxidation numbers, and accounts for temperature effects by parametrizing vibrational contributions to the formation enthalpy per bond., Comment: 11 pages, 4 figures, 2 tables

Most of the known bulk metallic glasses lack sufficient ductility or toughness when fabricated under conditions resulting in bulk glass formation. To address this major shortcoming, processing techniques to improve ductility that mechanically affect the glass have been developed, however it remains unclear for which metallic glass formers they work and by how much. Instead of manipulating the glass state, we show here that an applied strain rate can excite the liquid, and simultaneous cooling results in freezing of the excited liquid into a glass with a higher fictive temperature. Microscopically, straining causes the structure to dilate, hence “pulls” the structure energetically up the potential energy landscape. Upon further cooling, the resulting excited liquid freezes into an excited glass that exhibits enhanced ductility. We use Zr44Ti11Cu10Ni10Be25 as an example alloy to pull bulk metallic glasses through this excited liquid cooling method, which can lead to tripling of the bending ductility. Bulk metallic glasses typically display limited ductility. Here, straining a bulk metallic glass during cooling from the supercooled region is shown to enhance bending ductility, attributed to the structure being pulled up the potential energy landscape.

While rhenium is an ideal material for rapid thermal cycling applications under high temperatures, such as rocket engine nozzles, its high cost limits its widespread use and prompts an exploration of viable cost-effective substitutes. In prior work, we identified a promising pool of candidate substitute alloys consisting of Mo, Ru, Ta, and W. In this work we demonstrate, based on density functional theory melting temperature calculations, that one of the candidates, Mo0.292Ru0.555Ta0.031W0.122, exhibits a high melting temperature (around 2626 K), thus supporting its use in high-temperature applications.

Disorder enhances desired properties, as well as creating new avenues for synthesizing materials. For instance, hardness and yield stress are improved by solid-solution strengthening, a result of distortions and atomic size mismatches. Thermo-chemical stability is increased by the preference of chemically disordered mixtures for high-symmetry super-lattices. Vibrational thermal conductivity is decreased by force-constant disorder without sacrificing mechanical strength and stiffness. Thus, high-entropy ceramics propel a wide range of applications: from wear resistant coatings and thermal and environmental barriers to catalysts, batteries, thermoelectrics and nuclear energy management. Here, we discuss recent progress of the field, with a particular emphasis on disorder-enhanced properties and applications., Comment: 6 pages, 2 pics, super-short review