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.

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

2D topological insulators promise novel approaches towards electronic, spintronic, and quantum device applications. This is owing to unique features of their electronic band structure, in which bulk-boundary correspondences enforces the existence of 1D spin–momentum locked metallic edge states—both helical and chiral—surrounding an electrically insulating bulk. Forty years since the first discoveries of topological phases in condensed matter, the abstract concept of band topology has sprung into realization with several materials now available in which sizable bulk energy gaps—up to a few hundred meV—promise to enable topology for applications even at room-temperature. Further, the possibility of combining 2D TIs in heterostructures with functional materials such as multiferroics, ferromagnets, and superconductors, vastly extends the range of applicability beyond their intrinsic properties. While 2D TIs remain a unique testbed for questions of fundamental condensed matter physics, proposals seek to control the topologically protected bulk or boundary states electrically, or even induce topological phase transitions to engender switching functionality. Induction of superconducting pairing in 2D TIs strives to realize non-Abelian quasiparticles, promising avenues towards fault-tolerant topological quantum computing. This roadmap aims to present a status update of the field, reviewing recent advances and remaining challenges in theoretical understanding, materials synthesis, physical characterization and, ultimately, device perspectives.

In recent years, the notion of 'Quantum Materials' has emerged as a powerful unifying concept across diverse fields of science and engineering, from condensed-matter and coldatom physics to materials science and quantum computing. Beyond traditional quantum materials such as unconventional superconductors, heavy fermions, and multiferroics, the field has significantly expanded to encompass topological quantum matter, two-dimensional materials and their van der Waals heterostructures, Moiré materials, Floquet time crystals, as well as materials and devices for quantum computation with Majorana fermions. In this Roadmap collection we aim to capture a snapshot of the most recent developments in the field, and to identify outstanding challenges and emerging opportunities. The format of the Roadmap, whereby experts in each discipline share their viewpoint and articulate their vision for quantum materials, reflects the dynamic and multifaceted nature of this research area, and is meant to encourage exchanges and discussions across traditional disciplinary boundaries. It is our hope that this collective vision will contribute to sparking new fascinating questions and activities at the intersection of materials science, condensed matter physics, device engineering, and quantum information, and to shaping a clearer landscape of quantum materials science as a new frontier of interdisciplinary scientific inquiry. We stress that this article is not meant to be a fully comprehensive review but rather an up-to-date snapshot of different areas of research on quantum materials with a minimal number of references focusing on the latest developments.

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of 'DFT+ML' promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

This paper provides a pedagogical introduction to recent developments in geometrical and topological band theory following the discovery of graphene and topological insulators. Amusingly, many of these developments have a connection to contributions in high-energy physics by Dirac. The review starts by a presentation of the Dirac magnetic monopole, goes on with the Berry phase in a two-level system and the geometrical/topological band theory for Bloch electrons in crystals. Next, specific examples of tight-binding models giving rise to lattice versions of the Dirac equation in various space dimension are presented: in 1D (Su–Schrieffer–Heeger (SSH) and Rice–Mele models), 2D (graphene, boron nitride, Haldane model) and 3D (Weyl semi-metals). The focus is on topological insulators and topological semi-metals. The latter have a Fermi surface that is characterized as a topological defect. For topological insulators, the two alternative view points of twisted fiber bundles and of topological textures are developed. The minimal mathematical background in topology (essentially on homotopy groups and fiber bundles) is provided when needed. Topics rarely reviewed include: periodic versus canonical Bloch Hamiltonian (basis I/II issue), Zak versus Berry phase, the vanishing electric polarization of the SSH model and Dirac insulators.

Wide and ultrawide-bandgap (U/WBG) materials have garnered significant attention within the semiconductor device community due to their potential to enhance device performance through their substantial bandgap properties. These exceptional material characteristics can enable more robust and efficient devices, particularly in scenarios involving high power, high frequency, and extreme environmental conditions. Despite the promising outlook, the physics of UWBG materials remains inadequately understood, leading to a notable gap between theoretical predictions and experimental device behavior. To address this knowledge gap and pinpoint areas where further research can have the most significant impact, this review provides an overview of the progress and limitations in U/WBG materials. The review commences by discussing Gallium Nitride, a more mature WBG material that serves as a foundation for establishing fundamental concepts and addressing associated challenges. Subsequently, the focus shifts to the examination of various UWBG materials, including AlGaN/AlN, Diamond, and Ga₂O₃. For each of these materials, the review delves into their unique properties, growth methods, and current state-of-the-art devices, with a primary emphasis on their applications in power and radio-frequency electronics.

Lone pairs critically influence material properties, from local structure to bonding interactions, yet their direct visualization in solids has remained elusive. We address this gap with a method using Wannier functions and Hamiltonian rotation. Bonding analyses have also been constrained by the use of spherical s-orbitals derived from orbital projectors. In this study, we directly visualize lone pair orbitals using first-principles calculations and Wannier functions obtained through a simple Hamiltonian rotation via a similarity transform. This method offers a direct understanding of their role in solids through the resulting tight-binding model and qualitative information from the resulting 3D representation of the wavefunctions. We apply our approach to two materials from the bismuth oxyhalide family, confirming previous findings from the Revised Lone Pair Model. Additionally, our model enables us to manipulate inter-orbital hopping, highlighting the significant role of lone pairs in shaping the materials' electronic structure and band gap.

The extraordinary magnetoresistance (EMR) effect is a form of geometric magnetoresistance that occurs when the current redistribution inside a hybrid metal-semiconductor device changes when subjected to an external out-of-plane magnetic field. While the influence of material and geometrical properties on sensor performance has been extensively studied, the topography of EMR devices has not yet received much attention. Typically, flat, 2D topographies are assumed when optimizing EMR devices, which does not reflect the significant topography present in actual devices. This study fills this gap by numerically investigating concentric circular EMR devices with different topographies using a 3D finite element model. We show that EMR devices with metal top contacts, which are more straightforward to fabricate, behave similarly to conventional EMR devices where the metal shunt of the same thickness is embedded into the devices. For InSb/Au devices, both types of devices produce a magnetoresistance of $4.9\times10^5$% at 1 T. Our results also reveal that the magnetoresistance increases with the thickness for a large range of metal thicknesses. In addition, we also explore sidewall dimensions and show that it is desirable to have a thicker shunt with thinner sidewalls, compared to the opposite case.

We report the growth of high-quality single crystals of hole-doped composition ${\mathrm{Sr}_{0.95}\mathrm{K}_{0.05}\mathrm{Co}_2\mathrm{As}_2}$ and present an investigation of its structural, electronic, thermal, and magnetic properties. Our results show that hole doping leads to a substantial suppression of ferromagnetic (FM) correlations present in ${\mathrm{SrCo}_2\mathrm{As}_2}$ leading to nearly isotropic magnetic susceptibilities $\chi_{ab,c}(T)$ of ${\mathrm{Sr}_{0.95}\mathrm{K}_{0.05}\mathrm{Co}_2\mathrm{As}_2}$. The presence of FM fluctuations is considered one of the detrimental reasons why ${\mathrm{SrCo}_2\mathrm{As}_2}$ does not achieve a superconducting ground state despite having a precursor for it in the form of stripe-like antiferromagnetic fluctuations. The suppression of FM fluctuations leads to qualitative similarities between the properties of hole-doped ${\mathrm{SrCo}_2\mathrm{As}_2}$ and the end member of superconducting 122 iron-arsenide family KFe₂As₂. Our results also infer the presence of strongly correlated electronic states in ${\mathrm{Sr}_{0.95}\mathrm{K}_{0.05}\mathrm{Co}_2\mathrm{As}_2}$. Further, they show that the electronic structure of ${\mathrm{SrCo}_2\mathrm{As}_2}$ as well as this hole-doped composition is exceptionally unstable in the vicinity of Fermi level $E_{\mathrm{F}}$ for small alterations in the structural parameters and sensitively depends upon the details of simulation.

This study explores the influence of different hydrocarbons, methane and propane, on the properties of 4H-SiC epitaxial layers grown by chloride-based chemical vapor deposition. By systematically varying the C/Si and N/C ratios during epitaxial growth, and employing a comprehensive suite of characterization techniques, a better understanding of how growth conditions influence material properties is gained. We show that the n-type dopant incorporation strongly depends on the choice of hydrocarbon especially at lower doping levels. Furthermore, we have observed that methane contributes to a relatively longer carrier lifetime value compared to propane, though a similar lifetime limiting carbon vacancy defect concentration has been observed for both hydrocarbons in as-grown epitaxial layers. Moreover, additional defect levels are also suggested by deep-level transient spectroscopy, potentially related to chlorine complexes, with varying concentrations depending on the choice of hydrocarbon and C/Si ratio. These observations offer insights into the complicated interplay of factors influencing doping, minority carrier lifetime, and defect formation in 4H-SiC epitaxial layers during the epitaxial growth process, and contribute to the optimization of growth parameters depending on the application in question.

We report on the epitaxial growth of Bi₂WO₆ thin films by Pulsed Laser Deposition using a high-power infrared Nd:YAG laser source. X-ray diffraction investigation confirms that single (00l)-oriented thin films can be obtained on both LSAT and SrTiO₃ substrates by using a LaNiO₃ adapting layer. Moreover, reciprocal space maps show that the films coherently grow on such substrates with the in-plane lattice parameters fully matching those of the substrates. In-situ x-ray photoemission spectroscopy experiments show that a UHV annealing process makes the film more conductive even though it also affects the Bi:W chemical ratio by reducing the Bi content. Alternately, the conductivity of the films can be effectively tuned by either growing the film in Ar atmosphere or by depositing potassium on its surface without modifying the Bi:W chemical ratio. Our results provide a viable route to synthesize high-quality Bi₂WO₆ thin films with tailored electronic properties.

Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of 'DFT+ML' promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Nanocomposite thin films, comprising two or more distinct materials at nanoscale, have attracted significant research interest considering their potential of integrating multiple functionalities for advanced applications in electronics, energy storage, photonics, photovoltaics, and sensing. Among various fabrication technologies, a one-step pulsed laser deposition process enables the self-assembly of materials into vertically aligned nanocomposites (VANs). The demonstrated VAN systems include oxide–oxide, oxide–metal, and nitride–metal VAN films and their growth mechanisms are vastly different. These complexities pose challenges in the designs, materials selection, and prediction of the resulted VAN morphologies and properties. The review examines the key roles that surface energy plays in the VAN growth and provides a generalized materials design guideline combining the two key factors of surface energy and lattice strain/mismatch, along with other factors related to growth kinetics that collectively influence the morphology of VAN films. This review aims to offer valuable guidelines for future material selection and microstructure design in the development of self-assembled VAN films.

Carbon nanomaterials exhibit unique morphological and physical properties. When used as fillers in various matrices such as polymers, they can provide enhanced electrical, thermal and mechanical characteristics. The emerging field of sensing technologies has witnessed remarkable advancements, resulting from the integration of carbon-based nanocomposites. This paper presents a comprehensive review of the latest a developments in key carbon-based nanocomposite sensors. First, the unique properties of carbon nanomaterials are reviewed covering the full dimensional spectrum, followed by main synthesis routes addressing critical aspects such as morphology, surface functionalization, and doping strategies. Later, the synergistic effects arising from the combination of carbon nanomaterials with other components, such as polymers, are explored in detail, emphasizing the role of percolation levels in the overall sensing performance. The different sensing applications presented in this review cover a broad range, including strain, temperature, gas and biosensing. The mechanisms and principles governing the sensing capabilities of carbon-based nanocomposites are provided, shedding light on the interactions between analytes and nanocomposite surfaces. A critical analysis of current challenges and prospects is also presented, outlining potential avenues for further research and innovation. Finally, this review aims to serve as a valuable resource for researchers interested in carbon-based nanocomposites and their evolving role in advancing sensing technologies.

The use of nanomaterials for cancer ferroptosis presents a promising avenue for research and clinical applications. The unique properties of nanomaterials, such as their small size, large surface area, and ability to be engineered for specific tasks, make them ideal candidates for ferroptosis inducing cancer therapies. Ferroptosis is a new type of cell death mechanism that is distinct from apoptosis and necrosis. It has been shown to be critical in the treatment of various tumors. The ferroptotic mechanism has been mainly linked with the regulation of iron, amino acid, glutathione, and lipid metabolism of cells. The relationship between ferroptosis mechanisms and cancer nanomedicine has attracted considerable interest in recent years. It has been reported that the combination of nanomedicine and ferroptosis can achieve high therapeutic efficacy for the treatment of different cancer types. This review will provide an overview of recent work in ferroptosis-related cancer nanomedicine. First, general information is given about the definition of ferroptosis and its differences from other cell death mechanisms. Later, studies exploring the role of ferroptosis in the cancer nanomedicine field are discussed in detail. Specific focus has been given to the use of combinatorial treatment strategies which combine ferroptosis with chemodynamic therapy, photodynamic therapy, photothermal therapy, immunotherapy and sonodynamic therapy. Considering the fact that ferroptosis inducing nanoparticles (NPs) have already been introduced into clinical studies, nanoscientists can further accelerate this clinical translation as they tailor the physicochemical characteristics of nanomaterials. This review provides enlightening information for all researchers interested in the molecular characterization and relationship between ferroptosis and cancer-directed NPs.

With the advancements in Web of Things, Artificial Intelligence, and other emerging technologies, there is an increasing demand for artificial visual systems to perceive and learn about external environments. However, traditional sensing and computing systems are limited by the physical separation of sense, processing, and memory units that results in the challenges such as high energy consumption, large additional hardware costs, and long latency time. Integrating neuromorphic computing functions into the sensing unit is an effective way to overcome these challenges. Therefore, it is extremely important to design neuromorphic devices with sensing ability and the properties of low power consumption and high switching speed for exploring in-sensor computing devices and systems. In this review, we provide an elementary introduction to the structures and properties of two common optoelectronic materials, perovskites and transition metal dichalcogenides (TMDs). Subsequently, we discuss the fundamental concepts of neuromorphic devices, including device structures and working mechanisms. Furthermore, we summarize and extensively discuss the applications of perovskites and TMDs in in-sensor computing. Finally, we propose potential strategies to address challenges and offer a brief outlook on the application of optoelectronic materials in term of in-sensor computing.

AlN-based acoustic filters are key devices in Radio Frequency communications. The AlN material crystal quality on silicon substrates limits the current performances of these devices. ALD-grown 2D-MoS2 thin filmquality. can be used as a template on silicon to improve AlN crystal. However, after deposition of thick sputtered AlN films (≥ 200nm), a systematic delamination of the AlN/MoS2 stack appears, drastically limiting the integration of these materials in RF devices. We propose a new elaboration process to grow thick AlN films with improved crystal quality while avoiding the delamination issue. This process is based on the chemical conversion of the MoS2 layer through a very thin 5nm AlN seed using a 1000°C NH3 heat treatment at 400mbar. We show a trade-off between the AlN thickness and the reactive annealing conditions to allow the diffusion of nitrogen towards the underlaying MoS2 and its conversion into Mo(Ox)Ny. Then, we observe that after the heat treatment, it is still possible to grow thick AlN epitaxial films without any delamination on the substrate while still improving the AlN crystal quality. Using this new route, a 1µm thick sputtered AlN layer with a record AlN (002) Rocking Curve (RC) value of 0.71° on a silicon-based substrate was obtained without any cracks or delamination.

Phonon systems have emerged as potential platforms for exploring nontrivial band topologies. Meanwhile, two (or more) closed nodal lines (CNLs) may nest with each other and form a Hopf-link mode in momentum space. However, nontrivial Hopf-link modes in phonon systems have yet to be reported in realistic materials. In this work, based on the first principles calculations and symmetry analysis, we predict the presence of the clean Hopf-link mode in a phonon material, namely Ba2OsH6. Specifically, two CNLs around X (X') point hook each other and form a Hopf-link configuration, which are protected by two perpendicular mirror planes. More importantly, the Hopf-link mode exhibits the topological feature of topological nodal-line-related drumhead-like surface states, which can be clearly observed. This work presents a potential candidate for exploring Hopf-link mode in spinless systems and provides a way to understand the unique geometry of CNLs.

The increasing fascination with 2D van der Waals (vdW) magnetic materials arises from their distinctive properties and promising applications in spintronics, magnonics, and quantum information technologies. Among them, CrSBr is a semiconductor that stands out owing to its high Curie temperature (TC ~ 146 K), air stability and tunable electronic and magnetic properties. Here, we present a systematic investigation of the effects of Dy doping (12.5%, 25%, and 50%) on the structural, electronic and magnetic properties of CrSBr monolayer. Our results reveal that Dy incorporation enhances magnetic anisotropy and modulates TC that arise from strong ferromagnetic and weak antiferromagnetic interactions. Additionally, we investigate the properties of DySBr, DySI and DySeI monolayers, which are isostructural to the CrSBr. Our results reveal the feasibility of exfoliating them down to the single layer and the presence of long-range magnetic order at low temperatures, relying on the combination of both weak exchange interactions and large spin-orbit coupling. This work provides insights into tuning the properties of CrSBr through rare earth doping, unlocking new possibilities for advanced applications at the 2D limit.

Two-dimensional tin halide perovskites are gaining attention for their potential in high-performance field-effect transistors (FETs) due to their ease of processibility and high mobility. However, their complex charge transport mechanism remains poorly understood with no definitive transport models established. While temperature-dependent mobility analysis is a proven method for constructing accurate charge transport models in a given material system, systematic temperature dependence studies in prototypical 2D tin perovskites, PEA₂SnI₄, have been rarely reported. Here, we investigate the temperature-dependent transport properties of PEA₂SnI₄ in FETs, employing contact resistance analyses to decouple intrinsic channel mobility from contact resistance contributions. Our results reveal that the extracted mobility values are significantly contact-limited, particularly at lower temperatures, leading to substantial deviations in apparent mobility trends. By correcting for contact resistance, we establish that the intrinsic mobility of PEA₂SnI₄ remains nearly temperature-independent from 100K to 300K. Our resutls clearly address the critical need to account for contact effects in determining carrier mobility of perovskite materials within the community, offering a refined framework for accurately evaluating and enhancing the performance of perovskite-based electronic devices.

This perspective addresses the topic of harnessing the tools of Artificial Intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.