jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guan-Meng Zhang | Mutian Yang | Jie Zang | Xiaoli Li | Qiyong Zhong | Yuxiang Hu | Yiting Xie | Ludan Zhang | Yajie Qian | Jinna Ren | Yang Peng | Haitao Chen | Jiandong Gao | Ji Wu | Yuguang Wang
Source: Cancer Nexus (article, 2025-04-12)
Field: Materials Science — Materials Chemistry

Machine Learning–Driven Screening of High‐Activity Antitumor Nanozymes Using an Ensemble Enzymatic Oracle System ABSTRACT Nanozymes, by mimicking the catalytic sites of natural enzymes, have emerged as effective substitutes for traditional natural enzymes. However, the relationship between the physicochemical properties and activity of nanozymes is complex and nonlinear. Traditional laboratory screening and theoretical calculations struggle to handle large‐scale samples and multidimensional parameters, resulting in inefficiency and high costs in the pursuit of high performance nanozymes. Efficiently screening and designing nanozymes with desirable properties remain a significant challenge. Machine learning (ML) technology can capture the complex nonlinear relationship between the physicochemical properties and activity of nanozymes, allowing for the simultaneous processing of multidimensional input variables, thereby achieving more accurate and efficient performance prediction. This study adopts a data‐driven approach to propose an ensemble enzymatic activity prediction model, utilizing ML algorithms to understand the particle–activity relationship, thereby enabling the efficient screening of nanozymes with high peroxidase (POD)‐like activity. The ensemble model integrates five sub‐models, significantly improving prediction accuracy and enhancing generalization compared to single models, achieving an accuracy of 82.4%. External experimental validation results indicate that the model's activity screening outcomes for four nanozymes outside the dataset are consistent with the results of enzymatic activity assays. Further in vitro and in vivo experiments substantiate the effectiveness of the model‐selected nanozymes with POD activity in tumor treatment. This study offers a promising strategy for screening antitumor nanozymes with the desired POD activity and demonstrates the potential of ML in the field of materials science. Oracle ; Ensemble learning ; Computer science ; Chemistry ; Machine learning ; Artificial intelligence ; Software engineering Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Alireza Golgouneh | Maryam Zebarjadi | Brad Holschuh | Lucy E. Dunne
Source: Smart Materials and Structures (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Fatigue-aware force estimation for NiTi SMA actuators using LSTM recurrent networks Abstract This study focuses on the force estimation of NiTi shape memory alloy (SMA) actuators, particularly addressing the challenges posed by material fatigue and force degradation over repeated actuation cycles. Traditional modeling approaches for SMAs, which typically include constitutive, phase transformation, and heat transfer sub-models, often fail to account for these degradation phenomena, leading to inaccuracies in long-term predictions. To overcome these limitations, we developed and implemented multivariate long short-term memory (LSTM) recurrent neural networks for accurate force estimation using measured parameters such as electrical power (current, voltage), strain, and temperature. An extensive experimental setup was designed to cyclically and electrothermally (Joule heating) activate the SMA actuators at varying strain levels, and the degradation in force production was quantified, with a reduction of up to 20.62% observed after 1000 cycles. The study introduced both single-stage and two-stage LSTM architectures, with the SLF model, which incorporates historical force data along with other measurements, achieving the highest accuracy (RMSE of 0.003 N). More sensor-limited and deployment-oriented variants also performed well: SLWF and SLWTF achieved RMSEs of 0.089 N and 0.110 N, respectively, and the two-stage MTSL (with learned temperature as a hidden state) achieved 0.096 N. The proposed LSTM-based models demonstrated their efficacy in providing reliable force estimations under conditions of actuator degradation, making them suitable for real-time applications where traditional analytical models fall short. These findings offer valuable insights for applications requiring stabilized forces and consistent actuation performance in the presence of material fatigue. Actuator ; Shape-memory alloy ; SMA* ; Nickel titanium ; Degradation (telecommunications) ; Artificial neural network Shape Memory Alloy Transformations ; Prosthetics and Rehabilitation Robotics ; Aeroelasticity and Vibration Control
Authors: Stephanie Walsh | Hailly Degagne | J. Carr | Dan Festarini | David J. Rowan | Jules M. Blais | Matthew J. Bond
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Environmental Occurrence and Predictive Understanding of Polonium‑210 in Freshwater Environments Polonium ; Environmental science ; Chemistry ; Radiochemistry Radioactive element chemistry and processing ; Radioactivity and Radon Measurements ; Geochemistry and Elemental Analysis
Authors: Zhenxiang Wang | Taizheng Wu | Liang Zeng | Jiaxing Peng | Yu Ding | Ming Gao | Guang Feng
Source: ArXiv.org (preprint, 2025-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Relationships between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors The development of supercapacitors is impeded by the unclear relationships between nanoporous electrode structures and electrochemical performance, primarily due to challenges in decoupling the complex interdependencies of various structural descriptors. While machine learning (ML) techniques offer a promising solution, their application is hindered by the lack of large, unified databases. Herein, we use constant-potential molecular simulation to construct a unified supercapacitor database with hundreds of metal-organic framework (MOF) electrodes. Leveraging this database, well-trained decision-tree-based ML models achieve fast, accurate, and interpretable predictions of capacitance and charging rate, experimentally validated by a representative case. SHAP analyses reveal that specific surface area (SSA) governs gravimetric capacitance while pore size effects are minimal, attributed to the strong dependence of electrode-ion coordination on SSA rather than pore size. SSA and porosity, respectively, dominate volumetric capacitance in 1D-pore and 3D-pore MOFs, pinnacling the indispensable effects of pore dimensionality. Meanwhile, porosity is found to be the most decisive factor in the charging rate for both 1D-pore and 3D-pore MOFs. Especially for 3D-pore MOFs, an exponential increase with porosity is observed in both ionic conductance and in-pore ion diffusion coefficient, ascribed to loosened ion packing. These findings provide profound insights for the design of high-performance supercapacitor electrodes. Nanoporous ; Supercapacitor ; Electrochemistry ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Electrode Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Nanoporous metals and alloys
Authors: Eugenio Ragazzi
Source: Journal of Phytomolecules and Pharmacology (article, 2025-10-18)
Field: Chemistry — Analytical Chemistry

Fractals in pharmacognosy: Linking morphology, metabolites, and pharmacological relevance Fractals are self-similar and hierarchically organized patterns found throughout nature that can offer a framework to describe complexity in both plants and humans. Fractal architectures are evident in many aspects of plant structures, such as roots, leaves and venation networks, suggesting adaptive strategies for efficient resource acquisition and resilience to environmental stress. In addition to providing a quantitative measure for distinguishing taxa and monitoring plant health, fractal dimension can help outline the pattern of secondary metabolite biosynthetic pathways and chemical fingerprints, supporting chemometric analyses and quality control. In this context, fractalomics, a proposed discipline analogous to genomics or metabolomics, may extend fractal principles across biological systems and could be applied to pharmacognosy by linking plant morphology, metabolite networks, and systemic functions. Under a holistic view, it is possible to suggest a hypothesis-generating concept of 'fractal resonance' between medicinal plants and human systems, inviting a modern reinterpretation of the historical 'theory of signatures'. Integrating fractal analysis with machine learning and high-throughput techniques may not only advance predictive phytochemistry and personalized phytotherapy but also promote a more sustainable use of medicinal resources by bridging ethnopharmacological knowledge with contemporary systems biology. Relevance (law) ; Fractal ; Holism ; Modular design ; Bridging (networking) ; Phytochemistry ; Quality (philosophy) ; Fractal dimension Spectroscopy and Chemometric Analyses ; Chromatography in Natural Products ; Metabolomics and Mass Spectrometry Studies
Authors: Tim Houben | Coen van der Grinten | Maxim Pisarenco | T. J. Huisman | Jacek Kustra | Fons van der Sommen | Peter de With
Source:  (article, 2025-04-24)
Field: Materials Science — Surfaces, Coatings and Films

Optimizing surface reconstruction using a segmented scanning electron microscope detector and deep learning Accurate depth measurements of on-chip structures are increasingly important in semiconductor metrology. Given that Scanning Electron Microscopes (SEMs) are already widely used for fast, localized 2D imaging, exploring their potential for 3D capabilities is highly interesting. This paper presents an optimization study focused on 3D reconstruction performance when angle-resolved detection is achieved through a partitioned back-scattered electron (BSE) detector. The conducted research begins with an analysis of electron distribution projections on the detector surface, simulated using a Monte Carlo scattering model for relevant structures. Then a post-processing module is implemented which enables the simulation of various detector configurations that serve as input to an optimized machine-learning model. Using this framework, we evaluate and compare multiple detector setups, demonstrating a 56% depth prediction accuracy improvement over the single-detector baseline. Additionally, improvements ranging from 32% to 49% on typical critical dimension metrology are achieved using a multi-detector setup. This work highlights the benefits of a data-driven, angle-resolved detection approach to improve metrology precision for future semiconductor nodes. Scanning electron microscope ; Detector ; Surface reconstruction ; Artificial intelligence ; Computer science ; Materials science ; Surface (topology) ; Electron microscope ; Computer vision ; Optics ; Physics ; Mathematics ; Geometry Electron and X-Ray Spectroscopy Techniques
Authors: Renata Andrade | Lucas Benedet | Marcelo Mancini | Sérgio Henrique Godinho Silva | C. Freitas | Marco Aurélio Carbone Carneiro | Nilton Curi
Source: Ciência e Agrotecnologia (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes ABSTRACT Brazilian steel industries require high-quality charcoal to produce pig iron. Desirable charcoal attributes include high elemental carbon content, large mean particle size (MPS), and high density, while producing low contents of ash and volatile matter, and presenting low contents of water and contaminants (e.g., phosphorous). These attributes are commonly determined by standardized laboratory analyses, which are time consuming and costly, besides generating chemical effluents. Portable X-ray fluorescence (pXRF) spectrometry can be used to avoid the downsides of laboratory analyses. The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. Auxiliary explanatory variables (drying time, wood age, fine particle content, and friability) were included in the models. Models were trained to predict the following charcoal attributes: fixed C (%), ash content (%), volatile matter (%), MPS (mm), water content (%), density (kg/m3), and P contents (%). Satisfactory predictions were obtained for volatile matter, MPS, moisture, and density (R2 &gt; 0.6), and very accurate predictions were obtained for ash and P contents (R2 &gt; 0.75). The inclusion of auxiliary explanatory variables increased the prediction accuracy of MPS (R2 increased from 0.61 to 0.82), bulk density (from 0.56 to 0.73), and P contents (from 0.86 to 0.94). These results indicate that pXRF can be used as an ecofriendly alternative to assess the quality of eucalypt charcoal utilized in metallurgy. Charcoal ; Fluorescence ; Environmental science ; Environmental chemistry ; Computer science ; Materials science ; Remote sensing ; Chemistry ; Physics ; Geology ; Optics ; Metallurgy Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Cultural Heritage Materials Analysis
Authors: Wenxing Lv (6853457) | Yuxuan Zeng (10835813) | Xiaobo Wang (652710) | Weiming Lv (558976) | Xiaojing Wu (337665) | Hongmei Tian (18440891) | Baoshun Zhang (4759428) | Li Huang (107408) | Zhongming Zeng (2061985)
Source: Figshare (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Artificial SynapseBased on Black Phosphorus/SnS<sub>2</sub> Heterostructure Transistorfor Neuromorphic Computing withHigh Accuracy Artificial synaptic devices have garnered significant attention for overcoming the limitations of conventional von Neumann architecture in neuromorphic computing. However, the reported nonlinear weight update of artificial synapses prevents a hardware neural network from achieving the same high training and learning accuracy levels as those attained by a soft neural work. Despite the large-area fabrication constraints, two-dimensional (2D) materials that have tunable electronic band alignment and optoelectronic properties have emerged as promising candidates for artificial synapses. Here, we demonstrate an artificial synaptic device based on a black phosphorus (BP)/SnS<sub>2</sub> van der Waals heterostructure. This device achieves a large memory window and successfully emulates various synaptic functionalities. Notably, the nonlinearity in the synaptic weight update of this device can be electrically modulated, and the underlying mechanism is analyzed by density functional theory (DFT) calculations. Furthermore, we successfully integrate BP/SnS<sub>2</sub>-based artificial synapses into an artificial neural network, achieving a high recognition accuracy of 96.3% on the MNIST data set for handwritten digit recognition. These findings introduce an effective method for high-accuracy neuromorphic applications through the band structure engineering of 2D materials. Neuromorphic engineering ; MNIST database ; Artificial neural network ; Von Neumann architecture ; Nonlinear system ; Transistor ; Synapse ; Synaptic weight ; Crossbar switch 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Xuetao Li | Liang‐Shih Fan | Chenxi Xiong | Wei Nie | Yujiao Dong | Bo Zhu | Wei Guan
Source: Angewandte Chemie (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Prediction of Ground‐ and Excited‐State Redox Potentials in Iridium(III) Photocatalysts Abstract This study introduces a data‐driven framework that combines DFT calculations with machine learning to facilitate accurate and scalable predictions of ground‐ and excited‐state redox potentials for iridium(III) photocatalysts. We first constructed independent models to identify key geometric and electronic descriptors governing redox behavior. Shapley additive explanations‐based analyses revealed clear structure–activity relationships, offering mechanistic insights and rational guidance for tuning redox potentials. Based on these insights, we developed unified multi‐output models—Model G for ground‐state and Model E for excited‐state redox potentials—to enable rapid, cost‐effective, and high‐throughput predictions. By modeling oxidation and reduction processes within a shared descriptor space, we can reduce computational overhead while maintaining high predictive accuracy. To assess cross‐metal generalizability, residual transfer learning was applied to osmium (Os) photocatalysts. Using feature‐similar complexes, the resulting transfer models (G‐T, E‐T) achieved performance comparable to Os‐only baselines, demonstrating efficient few‐shot cross‐metal transfer. Collectively, this study establishes an interpretable and transferable machine‐learning framework for photocatalyst discovery. This framework provides a foundation for large‐scale screening and rational design across diverse transition‐metal platforms, accelerating advancements in photoredox catalysis, solar fuel production, and broader sustainable energy technologies. Redox ; Scalability ; Overhead (engineering) ; Rational design ; Reduction (mathematics) ; Electron transfer ; Oxidation reduction ; Transfer of learning Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Xu, Zhengtao | Wöll, Christof | Bräse, Stefan
Source: KITopen (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs) The layer-by-layer (LbL) assembly of coordination solids, enabled by the surface-mounted metal–organic framework (SURMOF) platform, yields thin films with well-defined orientation, tunable thickness, low density of defects, and editable crystalline heteroepitaxy. Such high-quality thin films are suited for integrating metal-organic framework (MOF) materials into devices used in electronics and optoelectronics technologies. However, the potential of the SURMOF platform has not been fully realized due to its instability, poor electronic interaction/transport, and limited intercalation/heteroepitaxy functions. Leveraging the longstanding efforts in processing and functionalizing coordination networks, four directions are highlighted for fully unleashing the technological potential of the SURMOF platform: 1) cascade cyclization to form polycyclic aromatic, nanographene-like scaffolds with strong electron polarizability and electroactivity; 2) crosslinking by fused-aromatic and metal–thiolate bridges for improved charge transport and structural stability; 3) covalent-ionic heteroepitaxy of conductive metal–thiolate layers alternating with metal-aqua-hydroxide layers to emulate the transport layer and the charge storage layer in high-temperature superconductors of cuprates and iron pnictides; and 4) machine learning (ML)-based methods to optimize synthesis conditions. Electronics ; Layer (electronics) ; Polarizability ; Electrical conductor ; Charge (physics) ; Electron transport chain ; Cascade ; Flexible electronics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: Lingjun Zeng | Yu Zhang | Xiaoping Mai | Ai Peng | Lan Xie | Xue Bai | Qiang Zheng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Excellent Absorption-Dominant Electromagnetic Interference Shielding Performances of Asymmetric Gradient Layered Composite Films Exploited with Assistance of Machine Learning Electromagnetic shielding ; Interference (communication) ; Composite number ; Electromagnetic interference ; Materials science ; Absorption (acoustics) ; Electromagnetic radiation ; Composite material ; Optoelectronics ; Optics ; Computer science ; Telecommunications ; Physics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Compatibility and Measurements
Authors: Hongling Guo | Yinghua Feng | Yiting Guo | Xiuli Zhang | Ping Wang | Can Hu | Hongcheng Mei | Yajun Li | Jun Zhu
Source: Research Square (preprint, 2025-12-18)
Field: Chemistry — Analytical Chemistry

A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic purposes Propellant ; Ammunition ; Chemometrics ; Unexploded ordnance ; Spectroscopy ; Forensic identification ; Characterization (materials science) ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser-induced spectroscopy and plasma
Authors: Mouna Gharbi | Silvia Villa | Émilie Chouzenoux | Jean‐Christophe Pesquet | Laurent Duval
Source: ArXiv.org (preprint, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux Data restoration from degraded observations, of sparsity hypotheses, is an active field of study. Traditional iterative optimization methods are now complemented by deep learning techniques. The development of unfolded methods benefits from both families. We carry out a comparative study of three architectures on parameterized chromatographic signal databases, highlighting the performance of these approaches, especially when employing metrics adapted to physico-chemical peak signal characterization. Field (mathematics) ; SIGNAL (programming language) ; Parameterized complexity ; Signal processing ; Iterative method Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Remote-Sensing Image Classification
Authors: Xiaohou Shao | Yuqi Zhang | Di Zhang | Dong, Zhaoyan | Tianxiang Gao | Li, Mingzhe | Xinyuan Liu | Zhihua Gan | Fanshun Meng | Kong, Lingcai | Gao, Zhengyang | Lic, Hao | Yangd, Weijie
Source: ArXiv.org (preprint, 2025-01-27)
Field: Materials Science — Materials Chemistry

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Constructing efficient and diverse datasets is essential for the development of accurate machine learning potentials (MLPs) in atomistic simulations. However, existing approaches often suffer from data redundancy and high computational costs. Herein, we propose a new method--Difference Vectors based on Local Atomic Environments (DV-LAE)--that encodes structural differences via histogram-based descriptors and enables visual analysis through t-SNE dimensionality reduction. This approach facilitates redundancy detection and dataset optimization while preserving structural diversity. We demonstrate that DV-LAE significantly reduces dataset size and training time across various materials systems, including high-pressure hydrogen, iron-hydrogen binaries, magnesium hydrides, and carbon allotropes, with minimal compromise in prediction accuracy. For instance, in the $α$-Fe/H system, maintaining a highly similar MLP accuracy, the dataset size was reduced by 56%, and the training time per iteration dropped by over 50%. Moreover, we show how visualizing the DV-LAE representation aids in identifying out-of-distribution data by examining the spatial distribution of high-error prediction points, providing a robust reliability metric for new structures during simulations. Our results highlight the utility of local environment visualization not only as an interpretability tool but also as a practical means for accelerating MLP development and ensuring data efficiency in large-scale atomistic modeling. Interatomic potential ; Computer science ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science