Table of contents

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.

The dynamic spray-gun deposition method was developed in 2006 to fabricate field effect transistors based on random arrays of carbon nanotubes (CNTs) field effect transistors for gas sensing applications. Thanks to this deposition method, we were able to fabricate hundreds of operational devices in a reproducible way that were integrated in electronic chips. Following this first implementation, we decided to widen the application of the deposition technique to the field of Energy and specifically to the fabrication of supercapacitors. In this context, we demonstrated in 2012 the fabrication of nanostructured electrodes for supercapacitors, using mixtures of graphene/graphite and CNTs increasing the device capacitance and the power delivered of a factor 2.5 compared to CNT based electrochemical-double-layer-capacitors. Indeed, with high quality graphene we could reach a value of around 100 W Kg⁻¹. This value is extremely promising also considering that it has been obtained with an industrially suitable technique. This dynamic spray-gun deposition has been also exploited for the fabrication of resistance based random access memories, making use of thin layers of graphene oxide and of oxidized carbon nanofibers. In the first case, 5000 cycles of 'write' and 'read' phases were demonstrated. These results pave the way for the fabrication of very low cost memories that can be embedded in smart-cards, patches for health monitoring (e.g. diabetes), ID cards, RFID tags and more generally smart packaging. Finally we are also working on the utilization of this technique for the fabrication of layers for electro-magnetic interference shielding application. Thanks to a new machine with four nozzles, developed within the frame of the Graphene Flagship project, we are able to deposit four different nanomaterials at the same time or alternatively on a large surface (30 cm × 30 cm) creating specific nano-structuration and therefore ad hoc architectures allowing the smart absorption of specific frequencies (e.g. X-band). All these applications demonstrate the extreme versatility of this technique that constitutes a real breakthrough for exploiting the nanomaterials characteristics in real devices, using an industrial suitable fabrication method that can be implemented using roll-to-roll technique.

Scaffold designs in combination with drug, growth factor and other bioactive chemicals account for lasting progress of vascular tissue engineering in the past decades. It is a great achievement to adjust tissue matrix composition and cell behaviour effectively. However, regenerating the innate physiologies of a blood vessel still needs its precise architecture to supply the vessel with structural basis for vascular functionality. Recent developments in biomaterial engineering have been explored in designing anisotropic surface geometries, and in turn to direct biological effects for recapitulating vascular tissue architecture. Here, we present current efforts, and propose future perspectives for the guidance on the architectural reconstruction and scaffold design of blood vessel.

Bio-responsive polymers are the foundation for the construction of the smart systems that exhibit designed biomedical functions after receiving specific stimuli such as biological signals and pathological abnormalities. These stimulus-responsive systems have shown great promise of developing novel products in precision medicine, and relevant research has grown intensively in recent years. This review aims to outline the basic knowledge and recent progress in the advanced bio-responsive systems as well as the major challenges. The current bio-responsive systems mainly rely on physical, chemical and biological cues, and this review focuses on the strategies of molecular design for the incorporation of appropriate responsive building blocks. The potential applications, including controlled drug delivery, diagnostics and tissue regeneration, are introduced and promising research directions that benefit the medical translation and commercialization are also discussed.

Faithful representations of atomic environments and general models for regression can be harnessed to learn electron densities that are close to the ground state. One of the applications of data-derived electron densities is orbital-free density functional theory (DFT). However, extrapolations of densities learned from a training set to dissimilar structures could result in inaccurate results, which would limit the applicability of the method. Here, we show that a non-Bayesian approach can produce estimates of uncertainty which can successfully distinguish accurate from inaccurate predictions of electron density. We apply our approach to DFT where we initialise calculations with data-derived densities only when we are confident about their quality. This results in a guaranteed acceleration to self-consistency for configurations that are similar to those seen during training and could be useful for sampling-based methods, where previous ground state densities cannot be used to initialise subsequent calculations.

A major goal of computation is to optimize an objective for which a forward calculation is possible, but no inverse solution exists. Examples include tuning parameters in a nuclear reactor design, optimizing structures in protein folding, or predicting an optimal materials composition for a functional application. In such instances, directing calculations in an optimal manner is important to obtaining the best possible solution within a fixed computational budget. Here, we introduce Rocketsled, an open-source Python-based software framework to help users optimize arbitrary objective functions. Rocketsled is built upon the existing FireWorks workflow software, which allows its computations to scale to supercomputing centers and for its objective functions to be complex, long-running, and error-prone workflows. Other unique features of Rocketsled include its ability to easily swap out the underlying optimizer, the ability to handle multiple competing objectives, the possibility to inject domain knowledge into the optimizer through feature engineering, incorporation of uncertainty estimates, and its parallelization scheme for running in high-throughput at massive scale. We demonstrate the generality of Rocketsled by applying it to optimize several common test functions (Branin-Hoo, Rosenbrock 2D, and Hartmann 6D). We highlight its potential impact through two example use cases for computational materials science. In a search for photocatalysts for hydrogen production among 18 928 perovskites previously calculated with density functional theory, the untuned Rocketsled Random Forest optimizer explores the search space with approximately 6–28 times fewer calculations than random search. In a search among 7394 materials for superhard candidates, Rocketsled requires approximately 61 times fewer calculations than random search to discover interesting candidates. Thus, Rocketsled provides a practical framework for establishing complex optimization schemes with minimal code infrastructure and enables the efficient exploration of otherwise prohibitively large search spaces.

Data visualisation is an important part of understanding the distributions, trends, correlations and relationships in materials data sets, as well as communicating results to others. Traditionally visualisation has been straightforward, particularly when studying single-structure/single-property relationships. It is not so straightforward when confronted with a materials data set represented by a large number of features, and containing multi-structure/multi-property relationships. Here we use Kohonen networks, or self-organising maps, to aid in the visualise sets of silver and platinum nanoparticles based on structural similarity and overlay functional properties to reveal hidden patterns and structure/property relationships. We compare these maps to a popular alternative dimension reduction method and find them superior for our cases where the structure/property relationships are highly nonlinear, and the data set is imbalanced, as they often are in materials science.

Silver, silicon co-substituted hydroxyapatite (Ag, Si-HA) was developed to provide bone repair coupled with antibacterial effect, with the aim to address the problems arise in the treatment of bone tissue infections. In this study, Ag, Si-HA demonstrated substantially reduced attachment of Staphylococcus aureus, Propionibacterium acnes, Escherichia coli, and Pseudomonas aeruginosa as compared to HA at 12 h. Being a prolific opportunistic pathogen in bone tissue infections, we investigated if P. aeruginosa could develop resistance against Ag, Si-HA. Our study showed that despite repeated exposure to fresh population of P. aeruginosa every 48 h, Ag, Si-HA exhibited effective antibacterial properties against the growth of P. aeruginosa over 168 h, indicating low risk of inducing bacterial resistance against Ag, Si-HA. As P. aeruginosa produces exotoxins and harbours endotoxins on its cell call, together these toxins could delay healing process. We therefore examined if the effect of these toxins released by P. aeruginosa during the antibacterial assessment on the attachment of mesenchymal stem cells on HA and Ag, Si-HA. Unlike HA, cell attachment on Ag, Si-HA was not affected by the addition of supernatant obtained from the antibacterial assessment of HA and Ag, Si-HA. This demonstrated that Ag, Si-HA could inhibit the growth of bacteria as well as minimise or prevent the detrimental effect from bacteria toxins.

Interfaces, in which the atomic structures are greatly different from those in the bulk, play a crucial role in the material properties. Therefore, determination of a central structure that is involved with the interface properties is an important task in materials research. However, determination of the interface structure requires a huge number of calculations. We previously proposed a powerful machine learning technique based on virtual screening (VS) to determine interface structures (Kiyohara et al 2016 Sci. Adv. 2 e1600746). Here, we discuss the feasibility, versatility, and robustness of the prediction model for VS. Through this study, the prediction model constructed using only 5 types of grain boundaries determines the energies and structures of the 52 grain boundaries. Furthermore, based on the constructed prediction models, we investigated the geometrical differences between the grain boundaries of different rotation axes. We also investigated the structure-property relationship at the grain boundary (GB). We found that a short bond at the GB is the key factor for preferential vacancy formation at the GB.

Though the effects of scaffold properties such as stiffness and topography on stem cell behavior was well known of, there were multiple theories that explain such behavior and there has been no common consensus thus far. This study deals with using polydimethylsiloxane (PDMS) to mimic a specific microenvironment that favors human mesenchymal stem cells (hMSCs) differentiation into myogenic lineages through the manipulation of specific surface topography and appropriate substrate stiffness. Femtosecond laser was applied to machine microchannels on PDMS substrates in this work. hMSCs were seeded and cultured on lasercut substrates, uncut substrates and controls. Quantitative and qualitative analyses of stem cell behavior were discussed herein with the use of Picogreen Assay for cell proliferation, staining of cytoskeleton for cell orientation, immunostaining of Myosin Heavy Chain for a myogenic biomarker, and quantitative real time polymerase chain reaction for gene expression analysis. It was found that both myogenic differentiation of hMSCs could be achieved by moderate stiffness or microchannels. And differentiation was further boosted by such PDMS substrates with additional microchannels.

Solid-state nuclear magnetic resonance (NMR) spectroscopy is a technique, which can be used to provide insight into the chemical structure of non-crystalline and crystalline materials. Hence, the present study aimed to elucidate the setting mechanism of CPC, which was fabricated using β-tricalcium phosphate (β-TCP)—inositol phosphate (IP6) composite powder using NMR In addition, the effect of IP6 on the local chemical structure of the β-TCP-IP6 composite powder and its hardened cement would also be investigated. The ¹H→³¹P heteronuclear correlation NMR spectrum revealed that an amorphous hydrated layer, along with small amount of hydroxyapatite (HA) was formed on the surface of β-TCP during the ball-milling process. Results demonstrated that the IP6 in the hydrated layer on the surface of β-TCP inhibited the formation of HA. Moreover, the setting reaction of the cement was mainly triggered by the dissolution of the amorphous hydrated layer on β-TCP surface, and subsequent precipitation, followed by the inter-entanglement between the HA crystals on the β-TCP.

The anomalous Hall effect of SrRuO₃ is of special interest, since Weyl nodes appear in the band structure and lead to an unconventional temperature dependence of the anomalous Hall constant. Moreover, it has been proposed that coupling of SrRuO₃ films to materials with strong spin–orbit coupling or with ferroelectric or ferromagnetic order might lead to the formation of skyrmions and a topological contribution to the Hall effect. This latter conjecture is strongly debated. We probed this proposal by interfacing thin SrRuO₃ layers to Pr_0.7Ca_0.3MnO₃, since it is known that the strong antiferromagnetic coupling between these two ferromagnets leads to complex magnetization states. Superlattices with sharp interfaces were grown by pulsed-laser deposition. The epitaxial interfacing with the Pr_0.7Ca_0.3MnO₃ layers led to major modifications of the structural symmetry of the SrRuO₃ layers. High resolution scanning transmission electron microscopy revealed that the individual SrRuO₃ layers of the superlattices had heterogeneous structure with varying oxygen octahedral tilt angles across the layers, turning their structure to be tetragonal-like, with largely suppressed octahedral tilts when the thickness of the neighboring Pr_0.7Ca_0.3MnO₃ layers was increased. These structural modifications were accompanied by major changes in the field dependence of the Hall signal with the mainly tetragonal SrRuO₃ layers showing features strongly reminiscent of a topological Hall effect. However, since there was an intimate link between Hall effect and structure, the Hall data were interpreted as arising from a superposition of Hall effect contributions from tetragonal and orthorhombic SrRuO₃ sub-layers.

We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture models. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature () of binary 3d transition metal- 4f rare-earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

Photoswitchable self-assembled monolayers (SAMs) in contact with a conductive or semiconductive layer can be used to remotely trigger changes in electrical current using light. In this study, we apply full-atomistic simulations to assess the changes in electronic structure and charge-transport properties of a graphene sheet in contact with an amorphous silica dielectric decorated by an azobenzene SAM. The simulations explicitly account for the structural and electrostatic disorder sourced by the dielectric, which turns out to be weakly affected by photoisomerization and spatially correlated over a length scale of 4–5 nm. Most interestingly, by combining large-scale (tight binding) density functional theory with Kubo–Greenwood quantum transport calculations, we predict that the trans-cis isomerization should induce a shift in surface electrostatic potential by a few tenths of a volt, accompanied by a variation in conductivity by a factor of about 3.

NbFeSb is a promising thermoelectric material which according to experimental and theoretical studies exhibits a high power factor of up to 10 mW m⁻¹ K⁻² at room temperature and ZT of 1 at 1000 K. In all previous theoretical studies, κ_latt is calculated using simplified models, which ignore structural defects. In this work, we calculate κ_latt by solving the Boltzmann transport equation and subsequently including the contributions of grain boundaries, point defects and electron–phonon interaction. The results for κ_latt and ZT are in excellent agreement with experimental measurements. In addition, we investigate theoretically the thermoelectric properties of TaFeSb. The material has recently been synthesised experimentally, thus confirming the theoretical hypothesis for its stability. This encourages a full-scale computation of its thermoelectric performance. Our results show that TaFeSb is indeed an excellent thermoelectric material which has a very high power factor of 16 mW m⁻¹ K⁻² at room temperature and ZT of 1.5 at 1000 K.

Two-dimensional atomic crystals (2DACs) can be mechanically assembled with precision for the fabrication of heterostructures, allowing for the combination of material building blocks with great flexibility. In addition, while conventional nanolithography can be detrimental to most of the 2DACs which are not sufficiently inert, mechanical assembly potentially minimizes the nanofabrication processing and preserves the intrinsic physical properties of the 2DACs. In this work we study the interfacial charge transport between various 2DACs and electrical contacts, by fabricating and characterizing 2DAC-superconductor junctions through mechanical transfer. Compared to devices fabricated with conventional nanolithography, mechanically assembled devices show comparable or better interface transparency. Surface roughness at the electrical contacts is identified to be a major limitation to the interface quality.

High-reflection interference mirrors for current gravitational wave detectors (aLIGO, Advanced Virgo, KAGRA) are made of high-quality oxide multi-layers deposited by ion beam sputtering (IBS) at the Laboratoire des Matériaux Avancés (LMA). For this task, LMA uses a large IBS custom-made machine (the grand coater GC) able to deposit very uniform coatings over very large surfaces, with diameter of some tens of cm. We report for the first time about the optical characterization by spectroscopic ellipsometry of oxide coatings deposited by the GC under strictly the same conditions used for the production of interference mirrors. We have investigated oxide materials like silica (SiO₂), tantala (Ta₂O₅) and titania-doped tantala (Ti:Ta₂O₅), providing for each material a broad-band (190–1700 nm) accurate determination of the complex index of refraction, with particular attention to wavelengths used in interferometers. Particular focus has been dedicated to the influence of Ti-doping on tantala coating. The doping induces a red-shift of the optical gap and an increase of the NIR refractive index. Furthermore, doping induces a decrease of the so-called Urbach energy, consistent with the well-known reduction of the internal friction in these kind of systems.

The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.