Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

Magnetic topological materials in two-dimensional: theory, material realization and application prospects

Ultrafine PdRu Nanoparticles Immobilized in Metal–Organic Frameworks for Efficient Fluorophenol Hydrodefluorination under Mild Aqueous Conditions

Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects

Benzene is an important air pollutant and a key chemical feedstock for the synthesis of cyclohexane. Because of the small difference of 0.6°C in their boiling points, the separation of benzene and cyclohexane is extremely challenging. Here, we report the high adsorption of benzene at low pressure and efficient separation of benzene/cyclohexane, achieved by the control of pore chemistry of two families of robust metal-organic frameworks, UiO-66 and MFM-300. At 298 K, UiO-66-CuII shows an exceptional adsorption of benzene of 3.92 mmol g−1 at 1.2 mbar and MFM-300(Sc) exhibits a high selectivity of 166 for the separation of benzene/cyclohexane (v/v = 1/1) mixture. In situ synchrotron X-ray diffraction and neutron powder diffraction, and multiple spectroscopic techniques reveal the binding mechanisms of benzene and cyclohexane in these materials. We also report the first example of direct visualization of reversible binding of benzene at an open Cu(II) site within metal-organic frameworks.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Metal-organic frameworks (MOFs), also known as coordination polymers, represent an interesting class of crystalline molecular materials that are synthesized by combining metal-connecting points and bridging ligands. The modular nature of and mild conditions for MOF synthesis have permitted the rational structural design of numerous MOFs and the incorporation of various functionalities via constituent building blocks. The resulting designer MOFs have shown promise for applications in a number of areas, including gas storage/separation, nonlinear optics/ferroelectricity, catalysis, energy conversion/storage, chemical sensing, biomedical imaging, and drug delivery. The structure-property relationships of MOFs can also be readily established by taking advantage of the knowledge of their detailed atomic structures, which enables fine-tuning of their functionalities for desired applications. Through the combination of molecular synthesis and crystal engineering, MOFs thus present an unprecedented opportunity for the rational and precise design of functional materials.

This review provides a comprehensive overview of recent advances in cancer biomarker discovery through multi-omics data integration combined with artificial intelligence (AI), highlighting how these approaches facilitate early diagnosis, improve prognostic accuracy, and enable personalized treatment in precision oncology. We conducted a literature review of high-throughput technologies, including genomics, transcriptomics, proteomics, and metabolomics, with particular emphasis on integrative computational strategies, such as translational bioinformatics, AI, and emerging quantum machine learning (QML), that support multidimensional data analysis for biomarker identification. Integrative analyses of multi-omics datasets using advanced computational methods have expanded our understanding of tumor molecular heterogeneity and complexity. Novel approaches, including liquid biopsy, digital pathology, and artificial intelligence-assisted image analysis, enhance early detection, therapy monitoring, and individualized interventions. Despite these advances, data standardization, harmonization of heterogeneous datasets, reproducibility, and ethical considerations remain challenges that must be addressed to enable effective clinical translation. Nevertheless, the integration of multi-omics data with AI and advanced computational methods is transforming cancer biomarker discovery, bridging molecular complexity with actionable clinical insights, and improving early detection, precise patient stratification, and therapy monitoring. Future progress will depend on interdisciplinary collaboration, global data harmonization, and continued technological innovation. Collectively, these advances pave the way toward truly personalized, proactive cancer care and position oncology as a data-driven discipline with improved patient outcomes.

The conceptual framework presented in this Perspective overviews the design principles of innovative thorium-based materials that could address urgent needs of the medicinal, nuclear energy, and waste remediation sectors from the lens of zirconium and uranium analogs. We survey the intersections of Zr, Th, and U chemistry with a focus on how the intrinsic behavior of each metal translates to broader material properties, including, but not limited to, structural and topological diversity, preferential metal-ligand binding, and reactivity. On the example of several classes of materials, including organometallic complexes, polyoxometalates, and the primary focus of this Perspective, metal-organic frameworks (MOFs), the design principles that govern the preparation of Zr-, Th-, and U-compounds, including oxophilicity, variation in oxidation states, and stable coordination environments have been considered. Further, we highlight how the impact of the mentioned variables may shift throughout the progression from discrete molecular systems to extended structures. We discuss the common assumption that zirconium-organic materials are typically considered a close analog of thorium-based congeners in areas such as material design and preparation. Through consideration of fundamental chemistry principles, we shed light on the relationships between Zr-, Th-, and U-based materials and highlight how a critical analysis of their distinct properties can be used to target a desired material performance. As a result, we provide a detailed understanding of Th-based materials chemistry by anchoring their fundamental properties between two well-studied reference points, zirconium- and uranium-containing analogs.

<p>Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available.Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship is discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.The rise of AI methods is affecting multiple aspects of HPE including raising questions about how best to incorporate these models into HPE practice and scholarship. In this commentary, we provide a primer on the AI methods most often used in HPE and discuss the most pressing challenges and opportunities these tools afford.</p> <p><br></p> <p><br></p>

Thermoelectric materials based on porous hybrid and MOFs constitute a very recent development, with many new and undiscovered phenomena for the research field. These porous Hybrid materials and MOF (Metal-Organic-Framework) films represent modern designer materials that exhibit many requirements of a near ideal and tunable future thermoelectric (TE) material. It is anticipated that especially the inherent porosity can be used advantageously in the design and simulation predictions of future thermoelectric materials. In principle, porous Hybrid and MOFs materials can comply with most of the requirements of an ideal and tunable future thermoelectric (TE) material. In contrast to traditional semiconducting bulk thermoelectric materials, porous hybrid MOF templates can be used to overcome some of the constraints of physics in bulk thermoelectric materials. These porous hybrid systems are amenable for simulation and modeling to design novel optimized electron-crystal phonon-glass materials with potentially very high Figure of Merit (ZT) numbers. Porous MOF and Hybrid materials possess an ultra-low thermal conductivity, which can be further modulated by phonon engineering within their complex porous and hierarchical architecture to advance the thermoelectric figure of merit ZT. This class of novel materials offers new approaches to the design of complex and hierarchical porous structures, possessing ultra-low thermal conductivity, which are able to trap, and localize phonons, thus resulting in a new generation of thermoelectric materials with a high ZT number. This work is demonstrating recent results of MOF thermoelectric Materials and provides a future outlook to the search for the next generation thermoelectric porous Hybrid and MOF materials, which could be part of a green renewable energy revolution with novel materials of sustainable high ZT values.

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

Understanding excited-stateprocesses is essential fordesigningnew functional organic materials. Modeling excited states in organiccrystals is challenging due to the need to balance localized and delocalizedprocesses and the competition between intramolecular and intermolecularinteractions. Cluster models have proven highly effective for describingweakly interacting organic crystals; however, nonperiodic calculationson periodic systems must account for mechanical and electrostaticcoupling to the crystal lattice, particularly in cases of extendedcoordination where covalent bonds are severed, such as in organicpolymers and metal–organic frameworks (MOFs). Point chargeembedding is a low-cost method for incorporating long-range electrostatics,enabling the consideration of long-range interactions using Ewaldembedding. Small clusters have been effective for modeling excited-stateprocesses in MOFs, yet embedding has rarely been included in suchstudies. In this work, we examine some of the challenges in describingexcited states in covalently connected organic crystals using ONIOM­(QM:QM’)embedding techniques across systems with increasing coordination:diC4–BTBT (an organic molecular crystal), polythiophene(an organic polymer), and two MOFs (QMOF-d29cec2 and MOF-5). We analyzethe effects of using different electronic structure methods, includingTDHF, TDDFT, ADC(2), and CC(2). One of the main challenges is thatembedded cluster models are susceptible to overpolarization near theQM:QM’ boundary. To address this, we assess the impact of differentcharge redistribution schemes (Z-N (N = 0, 3), RC, and RCD) and implement them in fromage. Additionally,we compare cluster and periodic models. We find that localized modelseffectively reproduce excited states in both nonconnected systems(diC4–BTBT) and fully connected MOFs, whereas polythiopheneremains the most challenging due to band conduction. The accuracyof vertical excitations, oscillator strengths, and simulated spectrais strongly influenced by model size, boundary charges, redistributionschemes, and level of theory. We further analyze the effect of vibrationalbroadening using the nuclear ensemble approach to predict the absorptionand emission spectra of MOF-5. Our results provide a heuristic guidefor nonperiodic studies of crystalline excited states, highlightingthe remarkable relationship between molecular crystals and MOFs, whichwill be explored in the future work.

Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information.

In nanoporous materials, guest–host interactions affect the properties and function of both adsorbent and adsorbate molecules. Due to their structural and chemical diversity, metal-organic frameworks (MOFs), a common class of nanoporous materials, have been shown to be able to efficiently and, often, selectively adsorb various types of guest molecules. In this study, we characterize the structure and dynamics of water confined in ZIF-90. Through the integration of experimental and computational infrared (IR) spectroscopy, we probe the structure of heavy water (D2O) adsorbed in the pores, disentangling the fundamental framework–water and water–water interactions. The experimental IR spectrum of D2O in ZIF-90 displays a blue-shifted OD-stretch band compared to liquid D2O. The analysis of the IR spectra simulated at both classical and quantum levels indicates that the D2O molecules preferentially interact with the carbonyl groups of the framework and highlights the importance of including nuclear quantum effects and taking into account Fermi resonances for a correct interpretation of the OD-stretch band in terms of the underlying hydrogen-bonding motifs. Through a systematic comparison with the experimental spectra, we demonstrate that computational spectroscopy can be used to gain quantitative, molecular-level insights into framework–water interactions that determine the water adsorption capacity of MOFs as well as the spatial arrangements of the water molecules inside the MOF pores which, in turn, are key to the design of MOF-based materials for water harvesting.