Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.
BackgroundThe Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types.ResultsThe work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here.ConclusionsAvogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.
Graphene is a rapidly rising star on the horizon of materials science and condensed-matter physics. This strictly two-dimensional material exhibits exceptionally high crystal and electronic quality, and, despite its short history, has already revealed a cornucopia of new physics and potential applications, which are briefly discussed here. Whereas one can be certain of the realness of applications only when commercial products appear, graphene no longer requires any further proof of its importance in terms of fundamental physics. Owing to its unusual electronic spectrum, graphene has led to the emergence of a new paradigm of 'relativistic' condensed-matter physics, where quantum relativistic phenomena, some of which are unobservable in high-energy physics, can now be mimicked and tested in table-top experiments. More generally, graphene represents a conceptually new class of materials that are only one atom thick, and, on this basis, offers new inroads into low-dimensional physics that has never ceased to surprise and continues to provide a fertile ground for applications.
A frustrated system is one whose symmetry precludes the possibility that every pairwise interaction (“bond”) in the system can be satisfied at the same time. Such systems are common in all areas of physical and biological science. In the most extreme cases, they can have a disordered ground state with “macroscopic” degeneracy; that is, one that comprises a huge number of equivalent states of the same energy. Pauling's description of the low-temperature proton disorder in water ice was perhaps the first recognition of this phenomenon and remains the paradigm. In recent years, a new class of magnetic substance has been characterized, in which the disorder of the magnetic moments at low temperatures is precisely analogous to the proton disorder in water ice. These substances, known as spin ice materials, are perhaps the “cleanest” examples of such highly frustrated systems yet discovered. They offer an unparalleled opportunity for the study of frustration in magnetic systems at both an experimental and a theoretical level. This article describes the essential physics of spin ice, as it is currently understood, and identifies new avenues for future research on related materials and models.
Xuanzhu Zhao, Zhangrong Lou, Pir Tariq Shah
et al.
Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts.
Md. Shahazan Parves, Md. Abu Bakkar Siddique, Md. Tarekuzzaman
et al.
Considering the environmental concerns of lead Hazardousness and durability concerns in lead-based perovskite solar cells (PSCs), lead-free alternatives like X2NaIrCl6 (X = Rb, Cs) have gained significant attention. This investigation carries out an analysis of the structural and optoelectronic behaviour of X2NaIrCl6 (X = Rb, Cs) using DFT to assess its potential for absorber material for solar cells (SCs). Structural stability of X2NaIrCl6 (X = Rb, Cs) double perovskites was analysed using tolerance factors (τ1, μ, τ2), with dynamical stability ensured through phonon dispersion. Negative and binding energy (Eb) and formation energy (Ef) further validated their stability. Direct band gaps, determined utilizing the TB-mBJ (GGA-PBE) approach, the values were determined to be 2.02 eV (0.97 eV) for Rb2NaIrCl6 and 1.93 eV (0.92 eV) for Cs2NaIrCl6, placing them in the recommended range (0.8- 2.2 eV) for photoelectric conversion. X2NaIrCl6 (X = Rb, Cs) double perovskites exhibit remarkable potential for photovoltaic applications, driven by their high absorption coefficients (∼104) and favourable optical properties, including low energy loss and minimal reflectivity (<15 %). These attributes highlight their promise for high efficiency and low-cost materials for advanced optoelectronic and solar energy devices. SCAPS-1D software employed to identify the most efficient solar cell designs by incorporating various HTLs and ETLs. Among 40 tested configurations, the ITO/ZnO/Cs2NaIrCl6/Cu2O structure attains the maximum PCE of ∼20.39 %, while ITO/ZnO/Rb2NaIrCl6/Cu2O achieves ∼19.16 %. Additionally, the study examines the effects of varying ETL/absorber thicknesses and series and shunt resistances, and temperature on photovoltaic performance. A detailed investigation was conducted on the principal photovoltaic indicators, such as current-voltage characteristics, capacitance, quantum efficiency, Mott Schottky parameters, and the processes governing photocarrier generation and recombination. These findings highlight X2NaIrCl6 (X = Rb, Cs) as a suitable material for high-performance optoelectronic and photovoltaic real-world applications.
The electrochemical glycerol oxidation reaction (GOR) has emerged as a sustainable pathway for transforming biodiesel byproducts into valuable resources, while addressing the growing demand for renewable energy and green chemical production. This perspective provides a comprehensive examination of recent advances in GOR technology, with a particular emphasis on catalyst design, reaction mechanisms, and system integration strategies. It highlights key challenges related to selectivity, stability, and scalability, which are critical for advancing the technology toward industrial applications. We explore how both noble metals (e.g., Pt, Au, and Pd) and non-noble metal alternatives (e.g., Ni, Co, and Cu) can be engineered through various methods, such as facet control, single-atom incorporation, and dynamic potential modulation, to selectively preserve C–C bonds and direct selectivity toward valuable multi-carbon products. Beyond standalone GOR processes, the integration with cathodic reactions presents new opportunities for system-level optimization. We discuss the benefits of coupling GOR with cathodic reactions, such as hydrogen evolution reaction (HER), carbon dioxide reduction reaction (CO2RR), and nitrate reduction reaction (NO3RR), which not only reduce the energy consumption but also enable the co-production of high-value chemicals and clean fuels. Despite the significant progress in GOR technology, several critical challenges remain for its industrial implementation, including mass transfer limitations, tolerance to crude glycerol, and long-term stability. This perspective provides a roadmap for addressing these challenges, proposing targeted solutions ranging from advanced membrane-electrode assemblies to integrated techno-economic assessments. Ultimately, this work aims to guide the field beyond a focus on catalyst activity, toward a holistic paradigm that prioritizes system-level integration and economic viability, thereby accelerating the industrialization of GOR technology.
Lipid metabolism imbalance combined with over-activated inflammation are two key factors for hepatic stestosis. However, on-demand anchoring inflammation and lipid metabolism disorder for hepatic stestosis treatment has yet to be realized. Here we propose a charge reversal fullerene based nano-assembly to migrate hepatic steatosis via inhibiting macrophage-mediated inflammation and normalizing hepatocellular lipid metabolism in obesity mice. Our nano-assembly (abbreviated as FPPD) is comprised of electropositive polyetherimide (PEI), charge-shielded dimethylmaleic anhydride (DMA), and poly(lactic-co-glycolic acid) (PLGA), which provides hydrophobic chains for self-assembly with anti-oxidative dicarboxy fullerene poly(ethylene glycol) molecule (FP). The obtained FPPD nano-assembly owns a charge reversal ability that switches to a positive charge in an acidic environment that targets the electronegative mitochondria both in pro-inflammatory macrophages and steatosis hepatocytes. We demonstrate that the anti-oxidative and mitochondria-targeting FPPD notably reduces inflammation in macrophages and lipid accumulation in hepatocytes by quenching excessive reactive oxygen species (ROS) and improving mitochondrial function in vitro. Importantly, FPPD nano-assembly reveals a superior anti-hepatic steatosis effect via migrating inflammation and facilitating lipid transport in obesity mice. Overall, the charge reversal nano-assembly reduces over-activated inflammation and promotes lipid metabolism that provides an effectiveness of a multi-target strategy for hepatic steatosis treatment.
Materials of engineering and construction. Mechanics of materials, Biology (General)
Giuseppe Misia, Maurizio Prato, Alessandro Silvestri
Abstract Efficient hydrogen peroxide (H2O2) detection is crucial for electrochemical and colorimetric sensors, making the hydrogen peroxide reduction reaction (HPRR) a key area of catalysis. In this context, molybdenum disulfide (MoS2) has emerged as a valuable HPRR catalyst. Here, we report the first experimental investigation comparing the electrocatalytic properties of MoS2 enriched with different phases (2H and 1T) and showcasing diverse morphologies toward HPRR. We provide unprecedented parameters (Tafel slope, constant, and exchange current density) describing the materials’ HPRR performance through state-of-the-art electrochemical techniques, including Tafel plots and EIS. Our findings reveal that 1T-enriched MoS2 outcompetes 2H-MoS2. Moreover, we show that the distinct morphologies of 1T-MoS2, such as exfoliated nanosheets and hydrothermally synthesized nanoflowers, strongly influence the kinetics of the catalytic reaction. This study bridges the gap between MoS2 structural properties and its electrocatalytic activity for HPRR, facilitating the selection of optimal MoS2 materials for high-sensitivity hydrogen peroxide sensors.
Materials of engineering and construction. Mechanics of materials, Chemistry
Alireza Jahandoost, Razieh Dashti, Mahboobeh Houshmand
et al.
Abstract Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.