jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Li Feng | Jianwen Zhao | Wei Wu | Hongyue Wang | Yu-Qing Jiang | Jin-Xun Liu | Wei-Xue Li
Source: Journal of the American Chemical Society (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning Oxide-supported metal clusters are central to the reverse water-gas shift (RWGS) reaction, which converts CO<sub>2</sub> to CO; however, the optimal interfacial properties governing activity remain unresolved. Although the oxygen vacancy formation energy (<i>E</i><sub>V</sub>) is known to influence CO<sub>2</sub> activation, its quantitative role and ideal value for catalysis have not been defined owing to the complexity of metal-oxide combinations and reaction pathways. Here, we integrate first-principles microkinetic modeling with interpretable machine learning across nine transition metal clusters on eight oxide supports to identify two key descriptors─<i>E</i><sub>OV</sub> of the support and the atomic radius (<i>r</i>) of the metal cluster─that together control the RWGS reactivity. We reveal a volcano-type relationship between the turnover frequency (TOF) and <i>E</i><sub>V</sub>, with optimal activity emerging at moderate vacancy formation energies (∼3.4 eV). A high <i>E</i><sub>V</sub> suppresses vacancy formation, whereas a low <i>E</i><sub>V</sub> limits CO<sub>2</sub> activation. Additionally, larger metal radii systematically lower the barrier for lattice oxygen reduction, stabilizing the transition state and promoting vacancy regeneration. The reaction mechanism shifts from carboxylate-mediated to direct CO<sub>2</sub> dissociation as <i>E</i><sub>V</sub> increases. Our framework captures experimental trends across reported catalysts and provides a physically grounded, predictive strategy for designing efficient RWGS catalysts by engineering metal-oxide interfaces. Catalysis ; Vacancy defect ; Dissociation (chemistry) ; Transition metal ; Lattice (music) ; Atomic radius ; Oxygen Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Yanquan Geng | Yunli Gao | Yongda Yan | Jaya Verma | Ching Yern Chee | Songyuan Zhang | Jiqiang Wang
Source: Surfaces and Interfaces (article, 2026-01-01)
Field: Materials Science — Biomaterials

Prediction of surface structure and optimization of corrosion resistance in magnesium alloys via machine learning for nanosecond laser processing Corrosion ; Magnesium ; Magnesium alloy ; Support vector machine ; Surface modification ; Superhydrophilicity ; Multilayer perceptron ; Titanium alloy Magnesium Alloys: Properties and Applications ; Laser Material Processing Techniques ; Corrosion Behavior and Inhibition
Authors: Yulin Shengcao | Xu Qin | Qinghang Wang | Huabao Yang | Yanfu Chai | Dabiao Xia | Bo Jiang | Hyoung Seop Kim
Source: Journal of Material Science and Technology (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Deep learning-driven innovation in metallic materials: A comprehensive review on microstructure analysis, property prediction, and inverse design Property (philosophy) ; Microstructure ; Inverse ; Tracing ; Generative grammar ; Characterization (materials science) ; Key (lock) ; Inverse problem Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Vahe Gharakhanyan | Luis Barroso-Luque | Yi Yang | Muhammed Shuaibi | Kyle Michel | Daniel S. Levine | Misko Dzamba | Xiang Fu | Meng Gao | Xingyu Liu | Haoran Ni | Keian Noori | Brandon M. Wood | Matt Uyttendaele | Arman Boromand | C. Lawrence Zitnick | Noa Marom | Zachary W. Ulissi | Anuroop Sriram
Source: Scientific Data (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Open Molecular Crystals 2025 (OMC25) dataset and models The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets with property labels. To address this challenge, we introduce the Open Molecular Crystals 2025 (OMC25) dataset, a collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was created by relaxing over 230,000 randomly constructed molecular crystal structures-representing approximately 50,000 organic molecules-using dispersion-inclusive density functional theory (DFT) with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional combined with Grimme's D3 dispersion correction (PBE+D3). OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide information on the dataset's construction, composition, and properties. To demonstrate the quality and use cases of OMC25, we trained and evaluated state-of-the-art open-source machine learning interatomic potentials. By making this dataset publicly available, we aim to accelerate the development of accurate and efficient machine learning models for molecular crystals. Crystal (programming language) ; Intermolecular force ; Crystal structure prediction ; Density functional theory ; Range (aeronautics) ; Property (philosophy) ; Dispersion (optics) ; Quality (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Donghyun Ra | Jaeryun Lee | Minwoo Lee | Seongmin Kwak | Seungchul Lee | Sooyoung Lee
Source: Journal of Manufacturing Systems (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions Key (lock) ; Computer-integrated manufacturing ; Advanced manufacturing ; Artificial neural network ; Foundation (evidence) ; Manufacturing Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: Masaru Tanaka | Shigeaki MORITA | Tomohiro Hayashi
Source: Polymer Journal (article, 2026-01-16)
Field: Materials Science — Surfaces, Coatings and Films

Interfacial water states and the biocompatibility of biomaterials: The role of intermediate water Biocompatibility ; Polymer ; Self-healing hydrogels ; Biocompatible material ; Monolayer ; Infrared spectroscopy ; Attenuated total reflection Polymer Surface Interaction Studies ; Collagen: Extraction and Characterization ; Surface Modification and Superhydrophobicity
Authors: Sidharth S. Menon | Trishit Mondal | Shuvayan Brahmachary | Aniruddha Panda | Subodh M. Joshi | Kaushic Kalyanaraman | Ameya D. Jagtap
Source: Neural Networks (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey Scientific Foundation Models (SciFMs) represent a transformative paradigm for addressing complex scientific and engineering problems by leveraging large-scale pretraining and deep learning architectures. Unlike traditional numerical solvers, which require problem-specific discretization and extensive parameter tuning, SciFMs aim to learn generalizable representations of physical laws, thereby enabling broad applicability with minimal retraining. In the absence of a rigorous definition, this work categorizes their capabilities into four key dimensions-domain adaptation, domain generalization, problem adaptation, and problem generalization; thereby providing rigorous definitions for SciFMs, which we refer to as the sufficient conditions. To operationalize these conditions in practice, we further introduce the necessary conditions. We also propose a taxonomy of SciFMs based on model architecture, learning objectives, and training strategies. Although SciFMs demonstrate remarkable potential in computational science and engineering, several challenges persist. Ensuring physical consistency, interpretability, and robustness under extrapolation to extreme regimes remains a critical hurdle. Furthermore, the significant computational demands and the lack of standardized benchmarks present substantial barriers to widespread adoption. This work surveys existing SciFMs across diverse domains-including chemistry, materials science, biology, climate and weather, Earth observation, geophysics, chaotic dynamics, robotics and control, nuclear science, etc., while categorizing them under the proposed definitions. We further outline open research directions, emphasizing the integration of domain knowledge with data- and physics-driven approaches and the development of efficient architectures to enhance generalization. Addressing these challenges will be critical for SciFMs to accelerate scientific discovery and deliver real-world impact. The GitHub repository for this work is available at https://github.com/ParamIntelligence/Awesome-Scientific-Foundation-Models. Operationalization ; Key (lock) ; Robustness (evolution) ; Transformative learning ; Domain (mathematical analysis) ; Scientific discovery ; Robotics ; Deep learning ; Open research Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Scientific Computing and Data Management
Authors: Ruotong Gao | Muhammad Khatib | Zhenan Bao
Source: npj Biosensing (article, 2026-01-09)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted polymer-based electrochemical sensors for amino acid detection: towards wearable sensing Continuous, non-invasive monitoring of physiological health is a significant objective in modern medicine and personalized wellness. To efficiently capture physiological data and diagnostic metrics, the ideal biofluid/medium for health monitoring should be non-invasive, easy to access, and contains a variety of physiologically significant analytes. Sweat is a particularly promising biofluid for real-time monitoring due to its ease of usage and rich milieu of metabolic and potentially diagnostic biomarkers. Within sweat, amino acids are of particular diagnostic interest due to their direct correlation with a spectrum of metabolic disorders, chronic illnesses, and overall health. Molecularly Imprinted Polymers (MIPs) have emerged as a promising class of synthetic, antibody-mimetic receptors that offer a robust, stable, and cost-effective alternative to traditional biological recognition elements. This review provides a comprehensive examination of MIP-based sensors for the detection of amino acids in sweat. It commences with an overview of the current literature around wearable sensing and real-time health monitoring. Then it presents a dissection of sweat composition and its utility as a diagnostic fluid with a following exploration of the role of amino acids as pivotal biomarkers in sweat. The review then discusses the fundamental principles of molecular imprinting, detailing various synthesis methodologies and the underlying physicochemical binding mechanisms governing MIP-analyte interactions. Subsequently, the specifics of MIP-based sensor construction for sweat analysis are investigated such as substrate selection, various signal transduction modalities with an emphasis on electrochemical techniques, nanomaterial integration, and current methods for sweat induction in wearables. This review concludes with analyzing current research trajectories, addressing the scientific and technical challenges such as sensor regeneration, physiological differences in amino acid concentrations, and explores the opportunities for future applications like point-of-care diagnostics, management of chronic diseases, and integration with artificial intelligence for personalized health analytics and health monitoring. Amino acid ; Wearable computer ; Human health ; Human physiology ; Modalities ; Biosensor ; Homogeneous Analytical chemistry methods development ; Advanced Sensor and Energy Harvesting Materials ; Advanced Chemical Sensor Technologies
Authors: Сачин Кумар Шарма | Lokesh Kumar Sharma | Reshab Pradhan | Yogesh Sharma | Mohit Sharma | Sandra Gajević | Lozica Ivanović | Blaža Ž. Stojanović
Source: Polymers (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Interphase-Centric and Mechanism-Driven Advances in Polymer Composites Reinforced with Nano-, Synthetic, and Inorganic Fillers Polymer composites reinforced with nanofillers, synthetic fibers, and inorganic fillers have progressed rapidly, yet recent advances remain fragmented across filler-specific studies and often lack unified mechanistic interpretation. This review addresses this gap by presenting an interphase-centric, mechanism-driven framework linking processing routes, dispersion and functionalization requirements, interphase formation, and the resulting structure-property relationships. Representative quantitative datasets and mechanistic schematics are integrated to rationalize nonlinear mechanical reinforcement, percolation-controlled electrical/thermal transport, and thermal stabilization and barrier effects across major filler families. The review highlights how reinforcement efficiency is governed primarily by interfacial adhesion, filler connectivity, and processing-induced microstructural evolution rather than filler loading alone. Key challenges limiting scalability are critically discussed, including dispersion reproducibility, viscosity and processability constraints, interphase durability, and recycling compatibility. Finally, mechanism-based design rules and future outlook directions are provided to guide the development of high-performance, multifunctional, and sustainability-oriented polymer composite systems. Interphase ; Filler (materials) ; Limiting ; Dispersion (optics) ; Composite number ; Polymer Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Composite Material Mechanics
Authors: Chuin Wei Tan | Marc Descoteaux | Mit Kotak | Gabriel de Miranda Nascimento | Seán R. Kavanagh | Laura Zichi | Menghang Wang | Aadit Saluja | Yizhong R. Hu | Tess Smidt | Anders Johansson | William C. Witt | Boris Kozinsky | Albert Musaelian
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

High-performance training and inference for deep equivariant interatomic potentials The NequIP framework is redesigned for scalable distributed training and PyTorch 2.0 compilation. AOT Inductor inference and optimized Allegro kernels accelerate molecular dynamics by factors of 5–18 on practical system sizes. Inference ; Training (meteorology) ; Scalability ; Training set ; Equivariant map ; Artificial neural network ; Dynamics (music) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: H. Cheng | Gaojie Han | Congqi Liu | Yang Zhou | Jie Wang | Bing Zhou | Yuezhan Feng | Chuntai Liu | Hao‐Bin Zhang | Changyu Shen
Source: Advanced Science (article, 2026-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineered Interpenetrating MXene Networks in Aramid Layered Films for Antioxidant and Broadband Electromagnetic Interference Shielding MXene-based layered films featuring a "brick-and-mortar" architecture show significant potential for electromagnetic interference (EMI) shielding applications; however, the insulating polymer "mortar" disrupts the connectivity of the MXene network, ultimately compromising both electrical conductivity and shielding effectiveness (SE). To address this limitation, this study introduces an innovative interpenetrating layered structure, comprising independent aramid nanofiber (ANF) layers and MXene layers, fabricated through a directional freeze-thaw intercalation-gel-film formation strategy. Unlike traditional homogeneous layered films, this unique layered structure features mutually embedded conductive and insulating layers, facilitating efficient electron transport, generating numerous heterogeneous interfaces for multiple reflections and scattering, and enhancing oxidation resistance. The interpenetrating ANF/MXene film exhibits exceptional electrical conductivity (5630.8 S/m) and EMI SE (43.3 dB) at ∼40 wt.% MXene loading, significantly outperforming the homogeneous ANF/MXene film (26.9 dB). Importantly, the interpenetrating ANF layers fully encapsulate the MXene layers, providing remarkable long-term stability with only a 10% decline in EMI performance after 80 days of aging. Furthermore, the interpenetrating ANF layers form a resilient mechanical framework, resulting in the A@M film boasting outstanding mechanical properties (tensile strength of 121.0 MPa, fracture strain of 13%). Consequently, this work presents a novel design and approach for fabricating high-performance MXene-based layered EMI shielding films. Electromagnetic shielding ; EMI ; Aramid ; Electromagnetic interference ; Electrical conductor ; Electrical resistivity and conductivity ; Conductivity ; Polymer Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Flame retardant materials and properties
Authors: Federico Stucchi | M. Li | Giuseppe Castellano | Francesco Cellesi
Source: Frontiers in Bioengineering and Biotechnology (article, 2026-01-21)
Field: Materials Science — Biomaterials

Regulatory framework for polymer-based nanotherapeutics in clinical translation Polymer-based nanotherapeutics (PNs) have emerged as versatile drug delivery platforms offering controlled release, targeted delivery, and improved pharmacokinetics across therapeutic areas. However, their structural complexity and nanoscale behavior pose significant regulatory challenges. Current frameworks still evaluate PNs under conventional pharmaceutical and biological regulations, overlooking nanospecific properties and leading to case-by-case assessments. This review examines critical quality attributes, analytical requirements, and gaps in standardization that hinder clinical translation. Key issues include inadequate characterization of the physicochemical properties, scalability of manufacturing, and limited predictability of <i>in vivo</i> performance. Highlighting the mismatch between nanoscale properties and existing regulatory expectations, this review underscores the need for a harmonized, science-driven framework to ensure safety, efficacy, and global acceptance of PNs. Standardization ; Scalability ; Key (lock) ; Predictability ; Quality (philosophy) ; Translational research ; Drug delivery Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Authors: Leiyang Wang | Ye Zhao | Jinteng Hu | Shah Fahad | Hao Wu
Source: Droplet (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Energy harvesting meets superhydrophobic surfaces: Recent advances Abstract Ambient energy harvesting from various renewable sources, including solar, thermal, wave, droplet, wind, and biomechanical energy, presents a promising solution for sustainable power generation and battery‐free Internet of Things networks. However, these technologies face significant challenges in energy conversion efficiency and device durability due to environmental factors such as surface contamination, moisture accumulation, and biofouling. Superhydrophobic surfaces address these limitations through their unique properties of self‐cleaning, water‐repellent, and anti‐bacterial, significantly enhancing energy harvesting performance and reliability. This review systematically summarizes recent advances in superhydrophobic surface‐enhanced energy harvesting devices based on various mechanisms, including photovoltaics, electromagnetism, piezoelectricity, triboelectricity, thermoelectricity, and electrical double‐layer dynamics. We first provide an updated overview of superhydrophobic surfaces, including their design strategies and fabrication methods. Then, we offer a comprehensive summary of their role in optimizing various energy harvesting devices. Finally, we discuss prospective challenges, potential solutions, and recommendations for future developments within this emerging interdisciplinary field. Energy harvesting ; Renewable energy ; Efficient energy use ; Sustainable energy ; Emerging technologies ; Solar energy Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Solar-Powered Water Purification Methods
Authors: Jennifer A. Korchak | S. Stephen Yi | Neil L. Kelleher | Nidhi Sahni | Gloria Sheynkman
Source: Nature Reviews Genetics (article, 2026-01-05)
Field: Chemistry — Spectroscopy

Proteoform medicine: characterizing and targeting protein forms in human disease Identification (biology) ; Human disease ; Alternative splicing ; Gene ; Systems biology ; Disease ; Transcriptome ; RNA splicing Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Genomics and Rare Diseases
Authors: Ulises H. Guzmán | Martin Rykar | Ivo A. Hendriks | H. Stewart | Eduard Denisov | Bernd Hagedorn | J. Petzoldt | Arne Kreutzmann | Yannick Mueller | Tabiwang N. Arrey | Immo Colonius | Ole Østergaard | Claire Koenig | Julia Kraegenbring | Kyle L. Fort | Erik P. A. Couzijn | Jan-Peter Hauschild | Daniel Hermanson | Vlad Zabrouskov | Christian Hock | Eugen Damoc | Jesper.V. Olsen
Source: Molecular & Cellular Proteomics (article, 2026-01-10)
Field: Chemistry — Spectroscopy

Higher-Throughput Proteome Profiling Enabled by Parallelized Pre-Accumulation and Optimized Ion Processing in the Orbitrap Astral Zoom Mass Spectrometer High-throughput proteomics is critical for understanding biological processes, enabling large-scale studies such as biomarker discovery and systems biology. However, current mass spectrometry technologies face limitations in speed, sensitivity, and scalability for analyzing large sample cohorts. The Thermo Scientific Orbitrap Astral Zoom mass spectrometer (MS) was developed to address these limitations by improving acquisition speed, ion utilization, and spectral processing, which are all essential for advancing proteome depth in high-throughput proteomics. The Orbitrap Astral Zoom MS achieves ultra-fast MS/MS scan rates of up to 270 Hz with enhanced ion utilization through pre-accumulation, enabling the identification of ∼100,000 unique peptides and >8400 proteins in a single 300 samples-per-day analysis of human cell lysate. The optimized system reduces analysis time by 40%, achieves near-complete proteome coverage (>12,000 proteins) in 2.7 h, and enables ultra-high-throughput workflows, identifying >7000 proteins in a 500 samples-per-day method with exceptional reproducibility (pairwise Pearson correlations >0.99). These advancements establish the Orbitrap Astral Zoom MS among the fastest and most sensitive instruments under the tested conditions, significantly enhancing speed, sensitivity, and scalability, paving the way for routine large-scale proteomics with applications in clinical research and systems biology. Orbitrap ; Zoom ; Proteome ; Mass spectrometry ; Proteomics ; Quantitative proteomics ; Human proteome project Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies