Disordered enthalpy-entropy descriptor for high-entropy ceramics discovery.

The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics1-3. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor4, most innovation has been slowly driven by experimental means1-3. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy-entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries.

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.

AFLOW-CCE for the thermodynamics of ionic materials.

Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials, such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parametrization of DFT errors with respect to coordination numbers and cation oxidation states, present a simple, yet accurate solution to enable materials stability assessments. Here, we illustrate the computational capabilities of our AFLOW-CCE software by utilizing our previous results for oxides and introducing new results for nitrides. The implementation reduces the deviations between theory and experiment to the order of the room temperature thermal energy scale, i.e., ∼25 meV/atom. The automated corrections for both materials classes are freely available within the AFLOW ecosystem via the AFLOW-CCE module, requiring only structural inputs.

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.

The Microscopic Diamond Anvil Cell: Stabilization of Superhard, Superconducting Carbon Allotropes at Ambient Pressure.

A metallic, covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an sp3 carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene chains. The distance between these sp2 carbon atoms renders the phase metallic, and yields two well-nested nearly parallel bands that cross the Fermi level. Calculations show this phase is a conventional superconductor, with the motions of the sp2 carbons being key contributors to the electron-phonon coupling. The sp3 carbon atoms impart superior mechanical properties, with a predicted Vickers hardness of 48 GPa. This phase, metastable at ambient conditions, could be made by on-surface polymerization of graphene nanoribbons, followed by pressurization of the resulting 2D sheets. A family of multifunctional materials with tunable superconducting and mechanical properties could be derived from this phase by varying the sp2 versus sp3 carbon content, and by doping.

QSAR without borders.

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Correction: QSAR without borders.

Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.

Vibrational spectral fingerprinting for chemical recognition of biominerals.

Pathologies associated with calcified tissue, such as osteoporosis, demand in vivo and/or in situ spectroscopic analysis to assess the role of chemical substitutions in the inorganic component. High energy X-ray or NMR spectroscopies are impractical or damaging in biomedical conditions. Low energy spectroscopies, such as IR and Raman techniques, are often the best alternative. In apatite biominerals, the vibrational signatures of the phosphate group are generally used as fingerprint of the materials although they provide only limited information. Here, we have used first principles calculations to unravel the complexity of the complete vibrational spectra of apatites. We determined the spectroscopic features of all the phonon modes of fluoroapatite, hydroxy-apatite, and carbonated fluoroapatite beyond the analysis of the phosphate groups, focusing on the effect of local corrections induced by the crystalline environment and the specific mineral composition. This provides a clear and unique reference to discriminate structural and chemical variations in biominerals, opening the way to a widespread application of non-invasive spectroscopies for in vivo diagnostics, and biomedical analysis.

High-Throughput Computational Search for Half-Metallic Oxides.

Half metals are a peculiar class of ferromagnets that have a metallic density of states at the Fermi level in one spin channel and simultaneous semiconducting or insulating properties in the opposite one. Even though they are very desirable for spintronics applications, identification of robust half-metallic materials is by no means an easy task. Because their unusual electronic structures emerge from subtleties in the hybridization of the orbitals, there is no simple rule which permits to select a priori suitable candidate materials. Here, we have conducted a high-throughput computational search for half-metallic compounds. The analysis of calculated electronic properties of thousands of materials from the inorganic crystal structure database allowed us to identify potential half metals. Remarkably, we have found over two-hundred strong half-metallic oxides; several of them have never been reported before. Considering the fact that oxides represent an important class of prospective spintronics materials, we have discussed them in further detail. In particular, they have been classified in different families based on the number of elements, structural formula, and distribution of density of states in the spin channels. We are convinced that such a framework can help to design rules for the exploration of a vaster chemical space and enable the discovery of novel half-metallic oxides with properties on demand.

The Structure and Composition Statistics of 6A Binary and Ternary Crystalline Materials.

The fundamental principles underlying the arrangement of elements into solid compounds with an enormous variety of crystal structures are still largely unknown. This study presents a general overview of the structure types appearing in an important subset of the solid compounds, i.e., binary and ternary compounds of the 6A column oxides, sulfides and selenides. It contains an analysis of these compounds, including the prevalence of various structure types, their symmetry properties, compositions, stoichiometries and unit cell sizes. It is found that these compound families include preferred stoichiometries and structure types that may reflect both their specific chemistry and research bias in the available empirical data. Identification of nonoverlapping gaps and missing stoichiometries in these structure populations may be used as guidance in the search for new materials.

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF3 reaches a mean absolute error of 0.17 eV/Å2 for the interatomic force constants, and it is less expensive than training the complete force field for such compounds. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis.

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis, implemented within the open-source, ab initio framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds characterized in the AFLOW.org repository, and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof of concept, thermodynamic characterizations have been performed for more than 1300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion-including 17 promising C15 b-type structures and 2 half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis and is located at aflow.org/aflow-chull .

All The Catalytic Active Sites of MoS2 for Hydrogen Evolution.

MoS2 presents a promising low-cost catalyst for the hydrogen evolution reaction (HER), but the understanding about its active sites has remained limited. Here we present an unambiguous study of the catalytic activities of all possible reaction sites of MoS2, including edge sites, sulfur vacancies, and grain boundaries. We demonstrate that, in addition to the well-known catalytically active edge sites, sulfur vacancies provide another major active site for the HER, while the catalytic activity of grain boundaries is much weaker. The intrinsic turnover frequencies (Tafel slopes) of the edge sites, sulfur vacancies, and grain boundaries are estimated to be 7.5 s-1 (65-75 mV/dec), 3.2 s-1 (65-85 mV/dec), and 0.1 s-1 (120-160 mV/dec), respectively. We also demonstrate that the catalytic activity of sulfur vacancies strongly depends on the density of the vacancies and the local crystalline structure in proximity to the vacancies. Unlike edge sites, whose catalytic activity linearly depends on the length, sulfur vacancies show optimal catalytic activities when the vacancy density is in the range of 7-10%, and the number of sulfur vacancies in high crystalline quality MoS2 is higher than that in low crystalline quality MoS2, which may be related with the proximity of different local crystalline structures to the vacancies.

Convergence of multi-valley bands as the electronic origin of high thermoelectric performance in CoSb3 skutterudites.

Filled skutterudites R(x)Co4Sb12 are excellent n-type thermoelectric materials owing to their high electronic mobility and high effective mass, combined with low thermal conductivity associated with the addition of filler atoms into the void site. The favourable electronic band structure in n-type CoSb3 is typically attributed to threefold degeneracy at the conduction band minimum accompanied by linear band behaviour at higher carrier concentrations, which is thought to be related to the increase in effective mass as the doping level increases. Using combined experimental and computational studies, we show instead that a secondary conduction band with 12 conducting carrier pockets (which converges with the primary band at high temperatures) is responsible for the extraordinary thermoelectric performance of n-type CoSb3 skutterudites. A theoretical explanation is also provided as to why the linear (or Kane-type) band feature is not beneficial for thermoelectrics.

Dysprosium-doped cadmium oxide as a gateway material for mid-infrared plasmonics.

The interest in plasmonic technologies surrounds many emergent optoelectronic applications, such as plasmon lasers, transistors, sensors and information storage. Although plasmonic materials for ultraviolet-visible and near-infrared wavelengths have been found, the mid-infrared range remains a challenge to address: few known systems can achieve subwavelength optical confinement with low loss in this range. With a combination of experiments and ab initio modelling, here we demonstrate an extreme peak of electron mobility in Dy-doped CdO that is achieved through accurate 'defect equilibrium engineering'. In so doing, we create a tunable plasmon host that satisfies the criteria for mid-infrared spectrum plasmonics, and overcomes the losses seen in conventional plasmonic materials. In particular, extrinsic doping pins the CdO Fermi level above the conduction band minimum and it increases the formation energy of native oxygen vacancies, thus reducing their populations by several orders of magnitude. The substitutional lattice strain induced by Dy doping is sufficiently small, allowing mobility values around 500 cm(2) V(-1) s(-1) for carrier densities above 10(20) cm(-3). Our work shows that CdO:Dy is a model system for intrinsic and extrinsic manipulation of defects affecting electrical, optical and thermal properties, that oxide conductors are ideal candidates for plasmonic devices and that the defect engineering approach for property optimization is generally applicable to other conducting metal oxides.

First principles thermodynamical modeling of the binodal and spinodal curves in lead chalcogenides.

High-throughput ab initio calculations, cluster expansion techniques, and thermodynamic modeling have been synergistically combined to characterize the binodal and the spinodal decompositions features in the pseudo-binary lead chalcogenides PbSe-PbTe, PbS-PbTe, and PbS-PbSe. While our results agree with the available experimental data, our consolute temperatures substantially improve with respect to previous computational modeling. The computed phase diagrams corroborate that in ad hoc synthesis conditions the formation of nanostructure may occur justifying the low thermal conductivities in these alloys. The presented approach, making a rational use of online quantum repositories, can be extended to study thermodynamical and kinetic properties of materials of technological interest.

The high-throughput highway to computational materials design.

High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.

Materials design for hypersonics.

Hypersonic vehicles must withstand extreme conditions during flights that exceed five times the speed of sound. These systems have the potential to facilitate rapid access to space, bolster defense capabilities, and create a new paradigm for transcontinental earth-to-earth travel. However, extreme aerothermal environments create significant challenges for vehicle materials and structures. This work addresses the critical need to develop resilient refractory alloys, composites, and ceramics. We will highlight key design principles for critical vehicle areas such as primary structures, thermal protection, and propulsion systems; the role of theory and computation; and strategies for advancing laboratory-scale materials to manufacturable flight-ready components.

A search model for topological insulators with high-throughput robustness descriptors.

Topological insulators (TI) are becoming one of the most studied classes of novel materials because of their great potential for applications ranging from spintronics to quantum computers. To fully integrate TI materials in electronic devices, high-quality epitaxial single-crystalline phases with sufficiently large bulk bandgaps are necessary. Current efforts have relied mostly on costly and time-consuming trial-and-error procedures. Here we show that by defining a reliable and accessible descriptor , which represents the topological robustness or feasibility of the candidate, and by searching the quantum materials repository aflowlib.org, we have automatically discovered 28 TIs (some of them already known) in five different symmetry families. These include peculiar ternary halides, Cs{Sn,Pb,Ge}{Cl,Br,I}(3), which could have been hardly anticipated without high-throughput means. Our search model, by relying on the significance of repositories in materials development, opens new avenues for the discovery of more TIs in different and unexplored classes of systems.

Plasmonic high-entropy carbides.

Discovering multifunctional materials with tunable plasmonic properties, capable of surviving harsh environments is critical for advanced optical and telecommunication applications. We chose high-entropy transition-metal carbides because of their exceptional thermal, chemical stability, and mechanical properties. By integrating computational thermodynamic disorder modeling and time-dependent density functional theory characterization, we discovered a crossover energy in the infrared and visible range, corresponding to a metal-to-dielectric transition, exploitable for plasmonics. It was also found that the optical response of high-entropy carbides can be largely tuned from the near-IR to visible when changing the transition metal components and their concentration. By monitoring the electronic structures, we suggest rules for optimizing optical properties and designing tailored high-entropy ceramics. Experiments performed on the archetype carbide HfTa4C5 yielded plasmonic properties from room temperature to 1500K. Here we propose plasmonic transition-metal high-entropy carbides as a class of multifunctional materials. Their combination of plasmonic activity, high-hardness, and extraordinary thermal stability will result in yet unexplored applications.