AI 驱动科学发现 · 全部实体（第 13 / 100 页）

本页收录第 1201–1300 条，共 10000 条 · 返回 AI 驱动科学发现 首页

• RNApedia: a database of structural protein-RNA interactions.
  The interaction between RNAs and RNA-binding proteins (RBPs) is fundamental for gene expression and regulation of cellular homeostasis. The growing interest in understanding protein-RNA complexes and their use in developing biotechnological solutions has highl…
  europepmc 2026 置信度 0.80
• Understanding the density maximum of water with machine-learned potentials.
  After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4°C. For nearly a century, this phenomenon has been qualitatively attributed to a mixture of ordered and disordered structures. He…
  europepmc 2026 置信度 0.80
• MXtalTools: A Toolkit for Machine Learning on Molecular Crystals.
  We present MXtalTools, a flexible Python package for the data-driven modeling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes of utilities: (1) synthesis, collation, and curation o…
  europepmc 2026 置信度 0.80
• fix pimd/langevin: An efficient implementation of path integral molecular dynamics in LAMMPS.
  Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture nuclear quantum effects …
  europepmc 2026 置信度 0.80
• Charge accumulation and solvation in β-NiOOH: Surface chemistry of an OER catalyst from ML-aided simulations.
  Electrochemical water splitting is a key technology for a sustainable energy transition, providing a route to store surplus electricity from renewable sources. A central bottleneck is the sluggish oxygen evolution reaction (OER), which drives the search for ca…
  europepmc 2026 置信度 0.80
• Tailored Vapor Deposition Unlocks Large-Grain, Wafer-Scale Epitaxial Growth of 2D Magnetic CrCl<sub>3</sub>.
  Two-dimensional magnetic materials (2D-MM) are an exciting playground for fundamental research, and for spintronics and quantum sensing. However, their large-grain, wafer-scale synthesis using scalable vapor deposition methods is still an unsolved challenge. H…
  europepmc 2026 置信度 0.80
• Phases and Architectures in Metal/Metal Oxide Systems Driven by Strong Metal-Support Interactions.
  Metal oxide supported metal catalysts are widely applied in industrial processes. Many of these materials dynamically evolve under reducing atmospheres, leading to metal nanoparticles partially or fully encapsulated by metal oxide shells, impacting catalytic p…
  europepmc 2026 置信度 0.80
• Precessional Dynamics of Octahedra in CsPbBr<sub>3</sub>.
  The crystal structure of ABX 3 halide perovskites consists of corner-sharing BX 6 octahedra, whose collective distortions define the different crystallographic phases. Because these materials are mechanically soft, with a shallow energy landscape, the octahedr…
  europepmc 2026 置信度 0.80
• Planar structures of medium-sized gold clusters become ground states upon ionization.
  This study investigates the structural stability of ionized gold clusters of sizes ranging from 22 to 100 atoms, contrasting compact, cage and planar structures. While it is well known that neutral clusters in the upper part of this size range predominantly fa…
  europepmc 2026 置信度 0.80
• First-principles and machine learning investigation of the structural and optoelectronic properties of dodecaphenylyne: a novel carbon allotrope.
  We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a unique geometric structure. The structural, dynamic, mechanical, electronic, and optical properties of DP were evaluated using density functiona…
  europepmc 2026 置信度 0.80
• Experimental-data-driven thermal conductivity prediction and inverse composition design for alloys.
  This work develops a data-driven framework for predicting the thermal conductivity of metals and multi-component alloys and for inversely proposing compositions that meet a target conductivity. We collect, to our knowledge, the largest experimental dataset con…
  europepmc 2026 置信度 0.80
• A Machine Learning-Guided Search of Cu-Based Bimetallic Alloys That Favor CO Dimerization in CO $_2$ RR.
  The rational design of Cu-based bimetallic catalysts for the electrochemical reduction of CO 2 into multicarbon (C 2 /C 2+ ) products critically depends on tuning the CO adsorption strength, which governs C-C coupling selectivity. However, systematically explo…
  europepmc 2026 置信度 0.80
• From DFT to MLFF: Accurate and Efficient Modeling of Strain-Tuned Lattice Thermal Conductivity in MoS<sub>2</sub> Monolayer.
  On-the-fly machine learning force fields (MLFFs), trained using ab initio molecular dynamics, offer a fast and accurate alternative to density functional theory (DFT) for predicting lattice thermal conductivity (κ L ) in two-dimensional materials. This study f…
  europepmc 2026 置信度 0.80
• Performance of Meta's Universal Model for Atoms across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts.
  Machine Learning Interatomic Potentials (MLIPs) promise to transform computational catalysis by delivering near-density functional theory (DFT) accuracy at a fraction of the computational cost. Here, we evaluate the Universal Machine Learning Potential for Ato…
  europepmc 2026 置信度 0.80
• Benchmarking Foundation Potentials against Quantum Chemistry Methods for Predicting Molecular Redox Potentials.
  Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications, such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accura…
  europepmc 2026 置信度 0.80
• Machine learning driven discovery of low modulus biomedical titanium alloys for additive manufacturing.
  Designing alloys for additive manufacturing opens opportunities for next-generation biomedical orthopaedic implants. However, most biomedical alloys currently in use are legacy compositions that cannot fully harness the potential of additive manufacturing. Her…
  europepmc 2026 置信度 0.80
• Machine-Learning Interatomic Potentials Achieving CCSD(T) Accuracy for Systems with Extended Covalent Networks and van der Waals Interactions.
  Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. In recent years, MLIPs trained on coupled-cluster data─particularly CCSD(T), which includes single, doub…
  europepmc 2026 置信度 0.80
• Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials.
  With the growing availability of machine-learned interatomic potential (MLIP) models for materials simulations, there is an increasing demand for robust, automated, and chemically informed benchmarking methodologies. In response, we here introduce LiPS-25, a c…
  europepmc 2026 置信度 0.80
• Fast and accurate prediction of adsorption energy of AgPd nanoalloys by deep learning potentials and neural networks.
  The accurate prediction of adsorption energy plays a crucial role in the design of efficient catalysts. However, the high-throughput prediction of adsorption energies based on first-principles methods presents significant challenges, hindering the rapid accele…
  europepmc 2026 置信度 0.80
• Exploring chemistry and catalysis by biasing skewed distributions via deep learning.
  The automated discovery of chemical and catalytic reactions remains a major challenge in computational chemistry, particularly in complex systems where conventional methods struggle to identify optimal searching directions. Here, we propose Loxodynamics, a mac…
  europepmc 2026 置信度 0.80
• Score matching the descriptor density of states for model-agnostic free energy estimation.
  Vibrational free energy estimation is a cornerstone of atomic simulation, essential to predict finite-temperature material properties. Expressing the free energy as a function of interatomic potential parameters is actively sought in modern workflows for uncer…
  europepmc 2025 置信度 0.80
• Learning Latent Representations to Bridge Coarse-Grained and Atomistic Resolutions in Polymer Simulations.
  We present a machine-learning-based framework for learning reduced-order representations of polymer chain conformations across coarse-grained (CG) and united-atom (UA) fidelities. By employing linear singular value decomposition and nonlinear autoencoders, we …
  europepmc 2026 置信度 0.80
• Efficient quantum algorithm for the design of complex materials: quantum circuit learning.
  Validity of the quantum circuit learning (QCL) method for the prediction of physical properties of complex materials has been explored by comparing the prediction results of Vickers hardness of the high entropy alloys with those predicted by the conventional l…
  europepmc 2026 置信度 0.80
• Theoretical Perspectives of Precision Chemistry.
  Precise prediction and control of chemical reactions and material propertiesthe central goal of precision chemistrytruly rely on accurate and efficient theoretical modeling of chemical systems. In recent years, significant progress has been made in theoretic…
  europepmc 2026 置信度 0.80
• A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids.
  Thermodynamic integration (TI) is the state-of-the-art computational technique for accurate Gibbs free energy predictions of solids. Conventional TI schemes start from an NVT harmonic reference and require three successive corrections to recover the Gibbs free…
  europepmc 2026 置信度 0.80
• Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.
  Machine-learning potentials (MLPs) promise near-first-principles fidelity at scales relevant to heterogeneous catalysis, yet the key determinant of their reliability remains the quality of the training data. A paramount challenge lies in constructing training …
  europepmc 2026 置信度 0.80
• Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.
  Machine learning potentials have achieved great success in accelerating atomistic simulations, among which message passing neural networks (MPNNs) have become increasingly prevalent thanks to their superior accuracy. However, MPNN potentials are difficult to b…
  europepmc 2026 置信度 0.80
• A Machine Learning-Based Computational Architecture for Unlocking Water Dynamics in Saturated Calcium Silicate Hydrate.
  The durability of reinforced concrete is closely related to the transport behavior of water and aggressive ions within the complex nanoporous network of calcium silicate hydrate. While molecular dynamics simulations provide critical atomistic insights into the…
  europepmc 2026 置信度 0.80
• Subsurface hydrogen as a hidden driver of copper surface reconstruction in CO<sub>2</sub> electroreduction.
  Copper (Cu) undergoes significant surface reconstruction during CO 2 electroreduction, which is strongly modulated and accelerated by reaction intermediates, yet the atomic-scale mechanism remains far behind the experimental observations. By integrating machin…
  europepmc 2026 置信度 0.80
• AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.
  Open-shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges. Most of the current…
  europepmc 2026 置信度 0.80
• Revealing the hidden third dimension of point defects in two-dimensional MXenes.
  Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engin…
  europepmc 2026 置信度 0.80
• Using experimental results of protein design to guide biomolecular energy-function development.
  Computational models of macromolecules have many applications in biochemistry, but physical inaccuracies limit their utility. One class of models uses energy functions rooted in classical mechanics. The standard datasets used to train these models are limited …
  europepmc 2026 置信度 0.80
• Search for Low-Periodic Substructures in Crystalline Solids: A Novel Approach.
  The discovery of new 2D materials is vital for advancing electronics and quantum technologies. As most 2D materials originate from layered bulk structures, identifying exfoliable crystals and estimating the energy required to isolate a single layer are critica…
  europepmc 2026 置信度 0.80
• Advancing the Frontiers: Contemporary Achievements and Future Directions in Density Functional Theory Calculations.
  Density Functional Theory (DFT) has been a cornerstone of computational chemistry and physics for several decades [...].
  europepmc 2026 置信度 0.80
• Deep learning generative model for conditional crystal structure prediction of sodium amide.
  Understanding the high-pressure behavior of sodium amide (NaNH 2 ) is essential for its applications in hydrogen storage and chemical synthesis. Conventional structure prediction methods often struggle to accurately capture its pressure-induced phase transitio…
  europepmc 2026 置信度 0.80
• Compact Kernel/Neural Network Representation for Accurate, Fast, and Global Reactive Molecular Potential Energy Surfaces.
  Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictive power depend directly on the accuracy of the underlying potential energy surface (PES). T…
  europepmc 2026 置信度 0.80
• Computational Investigation of GNMT-Catalyzed Methyl Transfer Reaction: Integrating MD, QM, and ML Approaches.
  The glycine N-methyltransferase (GNMT) reaction was examined using an integrated workflow combining molecular dynamics (MD), quantum mechanical (QM) cluster calculations, and machine learning (ML) analysis. Instead of relying on a single crystal-like conformat…
  europepmc 2026 置信度 0.80
• Equivariant electronic Hamiltonian prediction with many-body message passing.
  Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale appli…
  europepmc 2026 置信度 0.80
• Generalization of long-range machine learning potentials in complex chemical spaces.
  The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limi…
  europepmc 2026 置信度 0.80
• A Molecular "Thermometer" for Measuring Effective Non-Local Exchange.
  Non-local exchange (NLX) is a key ingredient for accurate density functional calculations, but its effective strength is often hard to quantify beyond simple global hybrids. To this end, we introduce a molecular probe based on the isomerization of hexaethynylb…
  europepmc 2026 置信度 0.80
• A statistical understanding of oxygen vacancies in distorted high-entropy perovskite oxides.
  High-entropy perovskite oxides have emerged as promising electrode materials for solid oxide electrolyzers. However, their compositional complexity makes the formation of oxygen vacancies, which influence properties such as oxygen ionic conductivity and therma…
  europepmc 2026 置信度 0.80
• Atomistic Simulations of Thermal and Chemical Expansions of PrNi<sub>x</sub>Co<sub>1-x</sub>O<sub>3-δ</sub> Accelerated by Machine Learning Potentials.
  The electrodes and solid-state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machin…
  europepmc 2026 置信度 0.80
• Search for active and inactive ion insertion sites in organic crystalline materials.
  The position of mobile active and inactive ions, specifically ion insertion sites, within organic crystals, significantly affects the properties of organic materials used for energy storage and ionic transport. Identifying the positions of these atomic (and io…
  europepmc 2026 置信度 0.80
• Data-Driven Quantum Simulation of Artificial Quantum Materials with Rydberg Atoms.
  Programmable quantum simulators based on Rydberg atom arrays provide a versatile platform for data-driven quantum simulation of strongly correlated systems, combinatorial optimization problems, and artificial quantum materials. In this review, we present a uni…
  europepmc 2026 置信度 0.80
• Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.
  The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial-and-error approaches, AI-empowered data-driven technologies, particularly large AI models such as un…
  europepmc 2026 置信度 0.80
• Weighted active space protocol for multireference machine-learned potentials.
  Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods…
  europepmc 2025 置信度 0.80
• Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.
  Machine-learning potentials (MLPs) extend the time and length scales of atomistic simulations, enabling the study of complex systems, such as electrolyte solutions. Yet most models face a tradeoff between accuracy, computational cost, and the ability to captur…
  europepmc 2026 置信度 0.80
• Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol-Water Mixtures.
  Mixtures of water and ethanol are an important solvent for biochemical reactions, as well as being a model system for hydrophobic hydration, since the ethanol molecule consists of polar and nonpolar regions. Experiments carried out over the last several decade…
  europepmc 2026 置信度 0.80
• Unveiling sodium storage mechanisms in hard carbon <i>via</i> machine learning-driven simulations integrated with accurate site occupation identification.
  Hard carbon (HC) has attracted considerable interest as a promising anode material for sodium-ion batteries (SIBs) due to its high specific capacity, excellent cycling stability, and cost-effectiveness. Nevertheless, the sodium storage mechanism in HC remains …
  europepmc 2026 置信度 0.80
• Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.
  Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping the landscape of computational chemistry, offering new opportunities for accelerating catalyst discovery and deepening our understanding of chemical reactivity. This perspective highl…
  europepmc 2026 置信度 0.80
• Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations.
  The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on …
  europepmc 2026 置信度 0.80
• Property prediction of energetic materials based on directional-aware graph attention network.
  Energetic materials have widespread applications in military, aerospace, and other high-stakes domains. Accurate prediction of their explosive properties is critical for both material development and safe deployment. This paper proposes a Directional-Aware Gra…
  europepmc 2026 置信度 0.80
• A universal machine learning model for the electronic density of states.
  In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the…
  europepmc 2026 置信度 0.80
• Defects and defect-mediated engineering of two-dimensional materials: challenges and open questions.
  Compared to bulk solids, defects in low-dimensional materials and, specifically, 2D systems are expected to have a stronger effect, detrimental or beneficial, on their properties. Owing to their geometry, defects in 2D materials can easily be formed due to the…
  europepmc 2026 置信度 0.80
• Optimizing cross-domain transfer for universal machine learning interatomic potentials.
  Accurate yet transferable machine-learning interatomic potentials are essential for accelerating materials and chemical discovery. However, many existing universal models are overfitted to narrow chemical spaces or computational protocols, limiting their relia…
  europepmc 2026 置信度 0.80
• From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
  Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can…
  datacite Liu, Ryan, Qu, Eric, Kreiman, Tobias, Blau, Samuel M. 等 2026 置信度 0.66 Machine Learning (cs.LG)Materials Science (cond-mat.mtrl-sci)Artificial Intelligence (cs.AI)Chemical Physics (physics.chem-ph)FOS: Computer and information sciences
• Z/6Z Modular Superselection and Mixed-Mass Decoupling: Inverse Design of the Non-Ergodic Topological Chalcogenide Ta3PbS6
  Computational Materials Data Package: Inverse Design of Non-Ergodic Topological Quantum Materials ($\mathrm{Ta_3PbS_6}$) Associated Manuscript "Modular Superselection $\mathbb{Z}/6\mathbb{Z}$ and Mixed-Mass Decoupling: Inverse Design and Validation of the Topo…
  datacite Peinador Sala, José Ignacio 2026 置信度 0.66 SuperconductivityTa3PbS6Phonon density of states (DOS)CHGNetSupervised Machine Learning
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations using a machine learning interatomic po…
  datacite Cao, Chuntian, Li, Boyang, Rodriguez Campos, Armando, Pace, Alexis 等 2026 置信度 0.66
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations using a machine learning interatomic po…
  datacite Cao, Chuntian, Li, Boyang, Rodriguez Campos, Armando, Pace, Alexis 等 2026 置信度 0.66
• Domain-bridging datasets and DFT results for "Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials"
  Updated (2026. 02. 06) SevenNet-Omni is a universal machine learning interatomic potential (uMLIP) trained based on the SevenNet-MF architecture, using 15 different open datasets across material domains of molecules, crystals, and surface systems. This item in…
  datacite Kim, Jaesun, You, Jinmu, Park, Yutack, Lim, Yunsung 等 2026 置信度 0.66 Computational chemistry
• HE26
  HE26 is a first-principles heavy-element dataset including eight rarely covered heavy elements: Am, Cm, Cf, Fr, At, Ra, Po, and Rn. It provides data for elemental solids, oxides, multicomponent compounds, and complex fluorite oxides to support machine learning…
  datacite ishihara, kenji 2026 置信度 0.66 Computational chemistryNeural networksTheory and design of materials
• HE26
  HE26 is a first-principles heavy-element dataset including eight rarely covered heavy elements: Am, Cm, Cf, Fr, At, Ra, Po, and Rn. It provides data for elemental solids, oxides, multicomponent compounds, and complex fluorite oxides to support machine learning…
  datacite ishihara, kenji 2026 置信度 0.66 Computational chemistryNeural networksTheory and design of materials
• Domain-bridging datasets and DFT results for "Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials"
  Updated (2026. 02. 06) SevenNet-Omni is a universal machine learning interatomic potential (uMLIP) trained based on the SevenNet-MF architecture, using 15 different open datasets across material domains of molecules, crystals, and surface systems. This item in…
  datacite Kim, Jaesun, You, Jinmu, Park, Yutack, Lim, Yunsung 等 2026 置信度 0.66 Computational chemistry
• Dataset for "Machine learning interatomic potentials for NaPSO glasses: the critical role of training data"
  Training (and test) dataset and machine learning potential model in ASE and LAMMPS format for the FT-MP0 model published in: Bertani M., Pedone A. "Machine learning interatomic potentials for NaPSO glasses: the critical role of training data" Solid State Scien…
  datacite Bertani, Marco, Pedone, Alfonso 2025 置信度 0.66
• Dataset for "Machine learning interatomic potentials for NaPSO glasses: the critical role of training data"
  Training (and test) dataset and machine learning potential model in ASE and LAMMPS format for the FT-MP0 model published in: Bertani M., Pedone A. "Machine learning interatomic potentials for NaPSO glasses: the critical role of training data" Solid State Scien…
  datacite Bertani, Marco, Pedone, Alfonso 2025 置信度 0.66
• Survival Benefit of ARSI Therapy in Japanese mCSPC: A Systematic Review and Meta-Analysis.
  Randomized clinical trials have shown that second-generation androgen receptor signaling inhibitors (ARSIs) improve overall survival (OS) in metastatic castration-sensitive prostate cancer (mCSPC). In Japan, combined androgen blockade (CAB) has been widely use…
  pubmed Iwamoto H, Izumi K, Hori T, Inaba T 等 2026 Aug 置信度 0.82
• Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS): Autoimmune Mechanisms, Diagnostic Controversies, and Therapeutic Implications.
  Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is a proposed postinfectious neuroimmune syndrome characterized by the abrupt onset of obsessive-compulsive disorder, tic disorders, and associated neuropsychiatr…
  pubmed Lynne V, Agrawal DK 2026 Sep 置信度 0.82
• Chronic obstructive pulmonary disease and risk of incident cardiovascular disease, chronic kidney disease, and death: a systematic review and meta-analysis.
  Chronic obstructive pulmonary disease (COPD) is associated with cardiovascular disease and chronic kidney disease, but with conflicting estimates. We aimed to quantify the association of COPD and incident cardiovascular diseases, chronic kidney disease and dea…
  pubmed Joseph T, Gao C, Nadarajah R, Al-Lehebi R 等 2026 Aug 置信度 0.82
• Personalizing Modern Heart Failure Therapy in HFrEF: Bridging Evidence, Clinical Practice, and Future Perspectives.
  Heart failure with reduced ejection fraction (HFrEF) remains a major cause of morbidity, mortality, and healthcare utilization worldwide despite substantial therapeutic advances. Over the past three decades, randomized clinical trials have established four fou…
  pubmed Shahverdi E, Shahverdi A, Shahi S, Schneider C 等 2026 置信度 0.82
• A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.
  Flavonoids are multifunctional phenylpropanoid-derived metabolites that occupy a central position in plant adaptation to environmental stress. Beyond their established roles in antioxidant protection, they contribute to defense against pathogens and herbivores…
  pubmed Hina A, Abbasi A, Chaudhry A, Sanaullah T 等 2026 置信度 0.82
• Toward engineered microbial chassis for growth-production balancing.
  The inherent conflict between cellular growth and product synthesis, arising from limited resource allocation and metabolic burden, fundamentally constrains the performance of microbial chassis. Traditional strategies that focus solely on flux intensification …
  pubmed Xia H, Wang B, Qi F, Huang J 2027 Mar 置信度 0.82
• Gut microbiota as a driver of variable antihypertensive drug response: A narrative review.
  Hypertension is a major global burden to human health. Despite the availability of multiple classes of antihypertensive medications, a considerable number of patients still have uncontrolled or treatment-resistant hypertension. The gut microbiota is increasing…
  pubmed Zheng V, Patel D, Yang T 2026 置信度 0.82
• Role of the Antioxidant Lycopene in Bone Health: Emphasis on the RANKL/RANK/OPG Signaling Pathway.
  Bone health is maintained by a balance between bone formation and bone resorption. Lycopene, a lipophilic carotenoid and an acyclic isomer of beta-carotene, functions as an antioxidant and may improve bone health via multiple pathways, including the suppressio…
  pubmed Esmaili H, Asgari N, Mosayeb N, Jafarnejad S 2026 Aug 置信度 0.82
• Research on artificial intelligence in stress and obesity: A bibliometric analysis using scopus, web of science, PubMed.
  Stress and obesity are major health concerns affecting individuals worldwide. Artificial Intelligence (AI) is being designed to identify and address these health challenges more precisely. Therefore, this study aimed to conduct a bibliometric analysis to inves…
  pubmed Pandit PR, Dharmagadda S, Goyal AK, Mahadeva R 等 2026 Jan-Dec 置信度 0.82
• Treatment prioritization of chemotherapy-anchored regimens after EGFR-TKI failure in EGFR-mutant NSCLC: a systematic review, pairwise meta-analysis and Bayesian network meta-analysis with probabilistic multi-criteria decision analysis.
  In advanced EGFR-mutant non-small cell lung cancer (NSCLC) after EGFR-TKI failure, chemotherapy alone offers limited benefit, while heterogeneous efficacy and safety across combination regimens complicate treatment selection. We systematically compared chemoth…
  pubmed Jing Y, Xing D, Jia A, Xu S 等 2026 置信度 0.82
• Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.
  Osteoporosis often remains undetected until fracture. Dual-energy X-ray absorptiometry (DXA) screening is limited by poor accessibility. Opportunistic screening of routine medical images, powered by artificial intelligence (AI), may enable automated, large-sca…
  pubmed Chen X, Wu W, Shen J, Chen Y 2026 置信度 0.82
• Diagnosing soil bioremediation failure: evidence-weighted pathways from mechanism to field closure.
  Soil bioremediation is often presented as a sustainable alternative to physicochemical remediation, yet its field performance remains less predictable than laboratory and microcosm evidence suggests. This critical review argues that the central problem is not …
  pubmed Mrozik A, Piotrowska-Seget Z, Cycoń M 2026 置信度 0.82
• Comprehensive and reproducible grading of the Updated Sydney System in gastric biopsies using artificial intelligence: multi-pathologist validation and a reader study.
  The Updated Sydney System is the most widely used framework for histological grading of gastritis, but complete grading in routine gastric biopsies is time-consuming and subject to interobserver variability. We developed an artificial intelligence (AI) system …
  pubmed Kim HH, Jeong WC, Hwang Y, Hwang G 等 2026 Aug 15 置信度 0.82
• From pixels to patterns: the AI revolution in stem cell-derived models.
  Artificial intelligence (AI) is rapidly transforming stem cell and developmental biology, offering new strategies to analyze, interpret and optimize complex, dynamic systems such as organoids and stem cell-derived embryo models. In this Perspective, we chart t…
  pubmed Deininger L, Caldarelli P, Zernicka-Goetz M, Mikut R 2026 Aug 14 置信度 0.82
• Novel triglyceride-lowering therapies to reduce the risk of acute pancreatitis in hypertriglyceridaemia: a protocol for systematic review and meta-analysis.
  Hypertriglyceridaemia is a major risk factor and rising aetiology for acute pancreatitis (AP), a condition associated with significant morbidity and mortality. Novel triglyceride-lowering therapies targeting key regulators of lipoprotein metabolism, specifical…
  pubmed Wu Y, Liu S, Luo W, Rao J 等 2026 Aug 14 置信度 0.82
• Incidence, Predictors, and Clinical Impact of Early Readmission After Complex Percutaneous Coronary Intervention.
  Early (≤30-day) rehospitalization after percutaneous coronary intervention (PCI) is associated with increased morbidity and mortality and is used to measure quality performance. We evaluated the impact and predictors of early readmission following compl…
  pubmed Kalaba F, Garg Y, Odukwe C, Oliva A 等 2026 Aug 14 置信度 0.82
• Progresses in viral detection technologies for wastewater-based epidemiology.
  Wastewater-based epidemiology (WBE) has emerged as a powerful surveillance tool used to assess public health by tracking infectious materials shed into sewage systems by infected hosts. To enable a widespread implementation of WBE, advanced analytical technolo…
  pubmed Tanaka Y, Salleh NABM, Ow SY, Zheng XT 等 2026 Aug 8 置信度 0.82
• Functional ionic liquids for enhanced transdermal drug delivery: Recent advances and future perspectives.
  Ionic liquids (ILs) have emerged as a versatile and transformative class of materials in pharmaceutical sciences due to their unique physicochemical tunability, high solubilization capacity, and exceptional ability to modulate biological barriers. In recent ye…
  pubmed Tan Y, Qin X, Chen Q, Zhou W 等 2026 Aug 12 置信度 0.82
• Access to palliative care in rural settings: A mixed-methods systematic review.
  Rural populations experience persistent inequities in access to palliative care. Existing evidence often describes individual barriers separately, with less attention to how access breaks down across the care pathway or how different service configurations sha…
  pubmed Yan C, Ai J, Cai J, Feng S 2026 Aug 6 置信度 0.82
• Operationalising the subjective: Coding visual art with AI.
  The use of Artificial Intelligence (AI) to study socio-cultural phenomena in visual art is a rapidly growing field, yet it introduces fundamental methodological challenges around measurement. Drawing on recent debates surrounding measurement theory frameworks,…
  pubmed Noord NV 2026 Aug 6 置信度 0.82
• Technology-Themed Persecutory and Related Presentations of Delusions in the Digital Age: Narrative Review.
  Persecutory delusions have long mirrored prevailing cultural and technological concerns. Beliefs involving implanted devices, internet surveillance, hacked smartphones, algorithmic targeting, and AI-mediated control are increasingly visible in contemporary psy…
  pubmed Bokhari SA, Osman AA, Elnoor M, Alnor MA 等 2026 Aug 14 置信度 0.82
• On the History of Hepatic Vein Catheterization.
  Hepatic vein catheterization (HVC) in man was first performed 80 years ago in the wake of right-sided heart catheterization. Several new methods, recognitions and concepts followed, especially the indirect Fick-method for determination of splanchnic blo…
  pubmed Henriksen JH, Bendtsen F, Møller S 2026 Sep 置信度 0.82
• Strategies for the Integration of Multifunctional Thermal Management Materials: A Review.
  The rapid development of high-power electronics, flexible devices, and advanced energy systems has created a growing demand for thermal management materials that combine heat dissipation with additional functions such as electrical insulation, electromagnetic …
  pubmed Song J, Huang G, Wei F, Sun L 等 2026 Aug 14 置信度 0.82
• From Inside to Outside: A Scoping Review of Connections in Care Buildings and Their Impact on End-Users' Experiences.
  AimThis scoping review aims to provide insight into how various connections between indoor and outdoor environments in care buildings affect end-user experiences and use of space.BackgroundNumerous studies highlight the beneficial effects of outdoor interactio…
  pubmed Watthy C, De Vos E, Annemans M 2026 Aug 14 置信度 0.82
• Advances in Cyclic Peptides Targeting G Protein-Coupled Receptors.
  Peptide-responsive G protein-coupled receptors (GPCRs) often recognize endogenous peptide ligands through extended receptor interfaces, providing opportunities for peptide-based ligands to engage receptor contacts that conventional small molecules struggle to …
  pubmed Zhou Y, Li N, Zheng JS 2026 Aug 14 置信度 0.82
• The LKB1-AMPK pathway in NAFLD: molecular mechanisms and therapeutic implications.
  The LKB1-AMPK signaling pathway is a central regulator of hepatic energy homeostasis and is increasingly implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). LKB1-mediated AMPK activation promotes fatty acid β-oxidation, autophagy…
  pubmed Goleij P, Tabari MAK, Naser Y, Alataa R 等 2026 Aug 14 置信度 0.82
• Artificial intelligence in anesthesiology residency education: a scoping review
  This project hosts materials for a completed scoping review investigating the global body of literature applying artificial‑intelligence technologies to anesthesiology residency (postgraduate) medical educationOSF. Background Anesthesiology residency training …
  datacite Wei Wang 2026 置信度 0.66 Medicine and Health SciencesEducation
• Advanced Topics in AI & Data Science
  Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative …
  datacite M. G. Shrigan, S. D. Bhourgunde 2026 置信度 0.66
• Advanced Topics in AI & Data Science
  Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative …
  datacite M. G. Shrigan, S. D. Bhourgunde 2026 置信度 0.66
• Applications of Artificial Intelligence in Physiotherapy Practice: A Scoping Review
  This scoping review maps the current landscape of artificial intelligence (AI) applications in physiotherapy practice, examining which technologies are clinically embedded versus still exploratory. Following the Arksey and O'Malley (2005) framework and reporte…
  datacite Aliza Mirza, Rabab Rahib, Melvyn Manoj, Lalitha venkatraman 等 2026 置信度 0.66 Other Rehabilitation and TherapyPhysical Sciences and MathematicsPhysiotherapyMedicine and Health SciencesRehabilitation and Therapy
• Pharmacological Management of Attention-Deficit/Hyperactivity Disorder in Adolescents and Adults with Methamphetamine or Amphetamine-Type Stimulant Use: A Scoping Review Protocol
  This project is a scoping review that will map and descriptively synthesize the available scientific evidence on the effectiveness, safety, and acceptability of pharmacological treatments for attention-deficit/hyperactivity disorder (ADHD) in adolescents and a…
  datacite Juan Rodrigo Gomez Bernal, Marisol Orocio Contreras 2026 置信度 0.66 Mental DisordersPsychiatry and PsychologyMedicine and Health Sciences
• DIGITAL TWIN TRAINING AND LEGAL GOVERNANCE FOR HUMANOID POLICE ROBOTS IN PATROL AND ARREST-SUPPORT
  Humanoid police robots are emerging as Physical AI platforms because they can operate in human-centered infrastructure such as stairs, doors, corridors, vehicles, and control panels. This study is important because police robots do not merely perform technical…
  datacite SEUNGKOOK ROH 2026 置信度 0.66 Physical AI, Digital Twin, Humanoid Police Robot, Sim-to-Real Transfer, AI Governance
• DIGITAL TWIN TRAINING AND LEGAL GOVERNANCE FOR HUMANOID POLICE ROBOTS IN PATROL AND ARREST-SUPPORT
  Humanoid police robots are emerging as Physical AI platforms because they can operate in human-centered infrastructure such as stairs, doors, corridors, vehicles, and control panels. This study is important because police robots do not merely perform technical…
  datacite SEUNGKOOK ROH 2026 置信度 0.66 Physical AI, Digital Twin, Humanoid Police Robot, Sim-to-Real Transfer, AI Governance
• jansky-research: A CPU-first, reproducible toolkit for amateur radio-astronomy analyses
  A tested, CPU-first Python toolkit for amateur, reproducible radio-astronomy analyses, built on the jansky course library, with optional opt-in GPU (ROCm/CUDA-portable, pure-PyTorch) acceleration for its signal-processing and machine-learning components. It bu…
  datacite Barbere, Joseph 2026 置信度 0.66 radio astronomyfast radio burstsHI 21 cmSETIreproducibility
• jansky-research: A CPU-first, reproducible toolkit for amateur radio-astronomy analyses
  A tested, CPU-first Python toolkit for amateur, reproducible radio-astronomy analyses, built on the jansky course library, with optional opt-in GPU (ROCm/CUDA-portable, pure-PyTorch) acceleration for its signal-processing and machine-learning components. It bu…
  datacite Barbere, Joseph 2026 置信度 0.66 radio astronomyfast radio burstsHI 21 cmSETIreproducibility