Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

Memristors (MRs) are considered promising devices with the enormous potential to replace complementary metal-oxide-semiconductor (CMOS) technology, which approaches the scale limit. Efforts to fabricate MRs-based hybrid materials may result in suitable operating parameters coupled to high mechanical flexibility and low cost. Metal–organic frameworks (MOFs) arise as a favorable candidate to cover such demands. The step-by-step growth of MOFs structures on functionalized surfaces, called surface-supported metal–organic frameworks (SURMOFs), opens the possibility for designing new applications in strategic fields such as electronics, optoelectronics, and energy harvesting. However, considering the MRs architecture, the typical high porosity of these hybrid materials may lead to short-circuited devices easily. In this sense, here, it is reported for the first time the integration of SURMOF films in rolled-up scalable-functional devices. A freestanding metallic nanomembrane provides a robust and self-adjusted top mechanical contact on the SURMOF layer. The electrical characterization reveals an ambipolar resistive switching mediated by the humidity level with low-power consumption. The electronic properties are investigated with density functional theory (DFT) calculations. Furthermore, the device concept is versatile, compatible with the current parallelism demands of integration, and transcends the challenge in contacting SURMOF films for scalable-functional devices.

Defects in semiconductors can exhibit multiple charge states, which can be used for charge storage applications. Here we consider such charge storage in a series of oxygen deficient phases of TiO2, known as Magnéli phases. These Magnéli phases (TinO2n−1) present well-defined crystalline structures, i.e., their deviation from stoichiometry is accommodated by changes in space group as opposed to point defects. We show that these phases exhibit intermediate bands with an electronic quadruple donor transitions akin to interstitial Ti defect levels in rutile TiO2. Thus, the Magnéli phases behave as if they contained a very large pseudo-defect density: ½ per formula unit TinO2n−1. Depending on the Fermi Energy the whole material will become charged. These crystals are natural charge storage materials with a storage capacity that rivals the best known supercapacitors.

Tantalum pentoxide (Ta2O5) is a wide-gap semiconductor that presents good catalytic and dielectric properties, conferring to this compound promising prospective use in a variety of technological applications. However, there is a lack of understanding regarding the relations among its crystalline phases, as some of them are not even completely characterized and there is currently no agreement about which models better explain the crystallographic data. Additionally, its phase diagram is unknown. In this work we performed first-principles density functional theory calculations to study the structural properties of the different phases and models of Ta2O5, the equation of state and the zone-centered vibrational frequencies. From our results, we conclude that the phases that are built up from only distorted octahedra instead of combinations with pentagonal and/or hexagonal bipyramids are energetically more favorable and dynamically stable. More importantly, this study establishes that, given the pressure range considered, the B-phase is the most favorable structure and there is no a crystallographic phase transition to another phase at high-pressure. Additionally, for the equilibrium volume of the B-phase and the λ-model, the description of the electronic structure and optical properties were performed using semi-local and hybrid functionals.

Novel two-dimensional non-van der Waals materials have been reported, boosting efforts to probe their properties and identify key applications. In this work, we report the synthesis, by means of a novel route via sonication of synthetic hematite, and characterization by transmission electron and atomic force microscopy of samples composed of two-dimensional hematite ([001]-cut layered α-Fe2O3). Microscopy images show a layered material with a handful of possible crystalline orientations, of which the [001] is the most abundant, presenting thickness of up to approximately 100 nm. Next, we employed first-principles calculations to study their structural stability and evaluate their thickness distribution. The stability of single, double, and triple layered structures is confirmed by phonon spectra and the formation energy is obtained, pointing out to the possibility of few layers, freestanding, stable samples. Further statistical modeling suggests that even though such thin samples are stable, their abundance is very small in comparison to thicker layers. We show that the antiferromagnetic ordering of the bulk phase is preserved in the nanostructured material, from the double-layered sample onward; however, a nonzero magnetization arises due to distinct localized moments in surface Fe atoms. Finally, our calculated band structures present narrower gaps in the layered structures in comparison to the bulk, and a charge-trapping acceptor level is identified at the surface Fe atoms.

Mayaro virus (MAYV) is an emerging arbovirus of the Americas that may cause a debilitating arthritogenic disease. The biology of MAYV is not fully understood and largely inferred from related arthritogenic alphaviruses. Here, we present the structure of MAYV at 4.4 Å resolution, obtained from a preparation of mature, infective virions. MAYV presents typical alphavirus features and organization. Interactions between viral proteins that lead to particle formation are described together with a hydrophobic pocket formed between E1 and E2 spike proteins and conformational epitopes specific of MAYV. We also describe MAYV glycosylation residues in E1 and E2 that may affect MXRA8 host receptor binding, and a molecular “handshake” between MAYV spikes formed by N262 glycosylation in adjacent E2 proteins. The structure of MAYV is suggestive of structural and functional complexity among alphaviruses, which may be targeted for specificity or antiviral activity.

In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures.

Defects in the rutile TiO 2 structures have been extensively studied, but the intrinsic defects of the oxygendeficient Ti n O 2n−1 phases have not been given the same amount of consideration. Those structures, known as Magnéli phases, are characterized by the presence of ordered planes of oxygen vacancies, also known as shear planes, and it has been shown that they form conducting channels inside TiO-based memristor devices. Memristors are excellent candidates for a new generation of memory devices in the electronics industry. In this paper we present density-functional-theory-based electronic structure calculations for Ti n O 2n−1 Magnéli structures using PBESol+U (0 U 5 eV) and Heyd-Scuseria-Ernzerhof functionals, showing that intrinsic defects present in these structures are responsible for the appearance of states inside the band gap, which can act as intrinsic dopants for the enhanced conductivity of TiO 2 memristive devices.