jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinmin Wang | Chang‐Hu Chu | Mingming Fang | Yongxin Tao | Yong Qin | Zheng Liang
Source: ACS Applied Nano Materials (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Functional Covalent Organic Framework Nanosheets for Ratiometric Monitoring of <i>Pseudomonas aeruginosa</i> Biomarker with Deep Learning-Assisted Assay The structure of covalent organic framework nanosheets (COF NSs) effectively mitigates fluorescence quenching caused by π–π stacking. Designing functionalized COF NSs as luminescent materials represents an innovative approach to expanding research on COF-based fluorescence sensing. Given that COF NSs expose a greater number of binding sites, they can be functionalized with appropriate luminescent elements to form a COF NS composite. The interaction between analytes and COF NS composites results in significant changes in luminescence, making these composites promising candidates for chemosensing applications. In this study, we introduce functionalized dual luminescent centers (silicon quantum dots (SiQDs) and Eu3+ complexes) into COF skeletons via a modification-facilitated exfoliation strategy. This process transforms the bulk COF (MPCOF) into a COF NS composite (1) with an enhanced luminescent performance. As a fluorescent sensor, 1 detects the biomarker 2-aminoacetophenone (2-AA) from Pseudomonas aeruginosa using ratiometric signals, facilitating visual sensing analysis. Leveraging its high sensitivity, an algorithmic method based on deep learning has been established to predict the fluorescence response of sensor 1 to specific concentrations of 2-AA. This facile determination of 2-AA enables effective monitoring of human or animal health and evaluation of water environments. Pseudomonas aeruginosa ; Biomarker ; Covalent bond ; Chemistry ; Biology ; Biochemistry ; Organic chemistry ; Bacteria ; Genetics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Surya Sarkar | Sanjukta Zamindar | Sayani Chowdhury | Sukdeb Mandal | Samik Dutta | Priyabrata Banerjee
Source: Journal of the Indian Chemical Society (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Quantification and prediction of corrosion mitigation for green corrosion inhibitor leveraging electrochemical, theoretical and SVR machine learning approach: A comprehensive AI-Driven approach Corrosion ; Corrosion inhibitor ; Support vector machine ; Surface roughness ; Molecular dynamics ; Density functional theory ; Adsorption ; Surface finish ; Quenching (fluorescence) Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Vikranth Reddy Madhirala | Naveen Kumar Maddu | Arun Shukla | Carl‐Ernst Rousseau
Source: Journal of Cellular Plastics (article, 2025-06-27)
Field: Materials Science — Polymers and Plastics

Deep learning for predicting stress and instantaneous modulus in closed-cell PVC foams Closed-cell polyvinyl chloride (PVC) foam offers several benefits, including its lightweight nature, high stiffness and specific strength, moisture resistance, and low thermal conductivity. These features make it an excellent core material for composite sandwich structures. This study aims to develop a deep neural network (DNN) model to accurately predict both stress and instantaneous modulus up to the densification phase based on density, strain, strain rate, and anisotropy ratio for all commercially available closed-cell H series PVC foams. To achieve this, the DNN model has been trained on stress-strain data from various studies published in the existing literature, encompassing different strain rates, densities, and anisotropy ratios of commercially available closed-cell H series PVC foams. The developed DNN model demonstrates exceptional accuracy in predicting stress and effectively captures the general trend of instantaneous modulus despite inherent noise in the actual data. This is evidenced by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> scores of 0.99 for stress and 0.77 for instantaneous modulus. These results underscore the model’s robustness, reliability, and potential as a powerful tool for accurately modeling the mechanical properties of closed-cell H series PVC foams. Materials science ; Composite material ; Modulus ; Stress (linguistics) ; Elastic modulus Polymer Foaming and Composites ; Polymer composites and self-healing ; Asphalt Pavement Performance Evaluation
Authors: Zipeng An | JingChun Wang | Yapeng Zhang | Zhiyu Li | Jiang Wu | Yalun Zheng | GuanHua Chen | Xiao Zheng
Source: The Journal of Chemical Physics (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Mitigating error cancellation in density functional approximations via machine learning correction The integration of machine learning (ML) with density functional theory has emerged as a promising strategy to enhance the accuracy of density functional methods. While practical implementations of density functional approximations (DFAs) often exploit error cancellation between chemical species to achieve high accuracy in thermochemical and kinetic energy predictions, this approach is inherently system-dependent, which severely limits the transferability of DFAs. To address this challenge, we developed a novel ML-based correction to the widely used B3LYP functional, directly targeting its deviations from the exact exchange-correlation functional. By utilizing highly accurate absolute energies as exclusive reference data, our approach eliminates the reliance on error cancellation. To optimize the ML model, we attribute errors to real-space pointwise contributions and design a double-cycle protocol that incorporates self-consistent field calculations into the training workflow. Numerical tests demonstrate that the ML model, trained solely on absolute energies, improves the accuracy of calculated relative energies, demonstrating that robust DFAs can be constructed without resorting to error cancellation. Comprehensive benchmarks further show that our ML-corrected B3LYP functional significantly outperforms the original B3LYP across diverse thermochemical and kinetic energy calculations, offering a versatile and superior alternative for practical applications. Computer science ; Pointwise ; Density functional theory ; Approximation error ; Hybrid functional ; Algorithm ; Workflow ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Xin Li | WU Sheng-qi | Shichao Ding | Zhao Wang | Jing He | Mingxia Sun | Haitham Saad Al-mashriqi | Selene Tang | Jia Chen | Hongdeng Qiu
Source: Analytical Chemistry (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Fe–N–C Single-Atom Nanozyme Rapid Screening Platform for Acetylcholinesterase Inhibitors Traditional screening methods for acetylcholinesterase inhibitors (AChEIs) encounter significant challenges due to two primary factors: subjective errors in colorimetric analysis and reliance on laboratory instruments. To overcome these limitations, this study introduces a rapid screening platform, which integrates a Fe-N-C single-atom nanozyme (SAzyme) biosensor, an anti-interference portable hydrogel kit, and an intelligent analysis platform. The system capitalizes on the remarkable enzyme-like catalytic properties of SAzyme, which effectively transform changes in AChE activity into visible colorimetric signals. In addition, the encapsulation of SAzyme within an agarose acidic hydrogel, combined with the physical light-blocking features of a 3D printing setup, ensures high stability during detection. Moreover, the system is enhanced by a customized smartphone application equipped with machine learning algorithms. This application identifies color signals, processes data, corrects for interference, and provides quantification of AChE activity via the smartphone interface. In practical application, this platform was successfully utilized to screen 10 alkaloids for AChE inhibitory activity, of which only berberine chloride demonstrated a significant inhibitory effect (IC<sub>50</sub> 4.61 μM). This study developed a three-unit integrated system (enzyme-like catalytic material, hydrogel kit, and AI algorithm) that overcomes traditional limitations through synergistic innovation, enabling a portable, precise, instrument-free AChEIs screening platform for resource-limited regions. Acetylcholinesterase ; Agarose ; Aché ; Inhibitory postsynaptic potential ; Colorimetric analysis Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Enzyme Catalysis and Immobilization
Authors: Jacob Wekalao | Hussein A. Elsayed | Ahmed Mehaney | Haifa E. Alfassam | Mostafa R. Abukhadra | Wail Al Zoubi | Amuthakkannan Rajakannu | K. Vijayalakshmi
Source: Sensing and Bio-Sensing Research (article, 2025-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Label-free graphene-based surface plasmon resonance sensor for advanced male fertility evaluation with behavior prediction via polynomial regression Male infertility affects approximately 15 % of reproductive-age couples globally, with male factors contributing to roughly 50 % of infertility cases, creating an urgent need for advanced, accessible diagnostic technologies for semen analysis. Current sperm assessment protocols rely predominantly on conventional light microscopy and Computer-Assisted Sperm Analysis (CASA) systems, which suffer from subjective interpretation, high costs, and limited accessibility in resource-constrained settings. This study presents a simple graphene-based Surface Plasmon Resonance (SPR) biosensor featuring a simple resonator architecture optimized for ultrasensitive sperm detection through label-free, real-time analysis. The electromagnetic analysis using COMSOL Multiphysics 6.3 demonstrates exceptional sensitivity ranging from 118 GHzRIU −1 to 5000 GHzRIU −1 across refractive indices of 1.33–1.3461 RIU, with a maximum figure of merit of 68.493 RIU −1 and detection limits as low as 0.028 RIU. Machine learning optimization using polynomial regression achieved prediction accuracies of 87–91 % (R 2 values of 94–100 %) across critical operational parameters including graphene chemical potential (0.1–0.9 eV), geometric variations, and angular dependencies (0–80°), validating the sensor's robust performance for clinical sperm analysis applications. Surface plasmon resonance ; Polynomial regression ; Regression ; Graphene ; Fertility ; Resonance (particle physics) ; Regression analysis ; Polynomial ; Materials science ; Mathematics ; Statistics ; Nanotechnology ; Medicine ; Mathematical analysis ; Physics ; Population ; Atomic physics ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Gaijinliu Gangmei | Bernard Rolfe | Santu Rana | Kishalay Mitra | S. Bhattacharyya
Source: Materials science forum (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Fourier Neural Operator for Predicting the Growth of Precipitates in a Binary Alloy Materials microstructural evolution can be effectively investigated with physics-based mod els, such as phase-field modeling. Nevertheless, the need to generate fine mesh systems in order to obtain numerical solutions of complex partial differential equations(PDEs) systems makes it compu tationally expensive. Therefore, the focus of this work is on Fourier Neural Operators (FNO), a quick and generalizable machine learning model that serves as a surrogate model. In this study, we have demonstrated the capability of FNO to learn the dynamics of precipitate growth. For interpolation settings, FNO could accurately predict the two coupled phase-field variables( c and η ) which represent the evolutionary state of the precipitate growth. It could also predict microstructure evolutions based on unseen initial conditions in extrapolation settings that is, outside the training set’s distribution of initial conditions. However, the error increases as we deviate further away from the distribution of the initial conditions used during training. For the case of precipitate growth in 1D with a system size of (X,T)=(4096*101), the Fourier neural operator has an inference time of only 0.027s compared to 0.21s of the pseudo-spectral method. We have also shown the capability of FNO in predicting the coupled phase-field variables at a higher resolution(4096*101) using the same model trained with low resolution data(64*101). Materials science ; Alloy ; Binary number ; Fourier transform ; Binary alloy ; Metallurgy ; Mathematical analysis ; Mathematics ; Arithmetic Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Metallurgy and Material Forming
Authors: Yassmin El morabit | Mohammed El Maadoudi | Naoual Alahlah | Amin Salhi | Abdellah Elyoussfi | Hassan Amhamdi | M’hamed Ahari
Source: Microchemical Journal (article, 2025-07-15)
Field: Chemistry — Analytical Chemistry

Charting a decade of olive oil authentication: A bibliometric survey of spectroscopy and chemometrics (2014–2024) Chemometrics ; Olive oil ; Authentication (law) ; Analytical Chemistry (journal) ; Chemistry ; Computer science ; Chromatography ; Food science Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Identification and Quantification in Food
Authors: Jincheng Qin | Faqiang Zhang | Mingsheng Ma | Yongxiang Li | Zhifu Liu
Source: Materials Genome Engineering Advances (article, 2025-03-24)
Field: Materials Science — Ceramics and Composites

Multiobjective optimization of dielectric, thermal, and mechanical properties of inorganic glasses utilizing explainable machine learning and genetic algorithm Abstract To meet the demands of advanced electronic devices, inorganic glasses are required to have comprehensive dielectric, thermal, and mechanical properties. However, the complex composition–property relationship and vast compositional diversity hinder optimization. This study developed machine learning models to predict permittivity, dielectric loss, thermal conductivity, coefficient of thermal expansion, and Young’s modulus based on the composition features of inorganic glasses. The optimal models achieve R 2 values of 0.9614, 0.7411, 0.9454, 0.9684, and 0.8164, respectively. By integrating domain knowledge with model‐agnostic interpretation methods, feature contributions and interactions were analyzed. The mixed alkali effect is crucial for property regulation, especially Na‐K for dielectric loss and Na‐Li for thermal conductivity. Boron anomaly shifts the high‐λ region to a balanced composition of alkali metals with rising B%. The multiobjective optimization of properties was realized using a genetic algorithm framework. After 23 iterations, the optimal material in the MgO‐Al 2 O 3 ‐B 2 O 3 ‐SiO 2 system exhibits ε r = 4.78, tanδ = 0.00063, λ = 2.59 W/(m·K), α = 50.27×10 −7 K −1 , and E = 82.41 GPa, outperforming all materials in the dataset. The computational effort was reduced to 1/19 of that required using exhaustive search methods. This study provides a model interpretation framework and an effective multiobjective optimization strategy for glass design. Dielectric ; Genetic algorithm ; Thermal ; Optimization algorithm ; Materials science ; Computer science ; Algorithm ; Composite material ; Mathematical optimization ; Artificial intelligence ; Machine learning ; Mathematics ; Optoelectronics ; Physics ; Thermodynamics Glass properties and applications ; Electrical and Thermal Properties of Materials ; Pigment Synthesis and Properties
Authors: Feranmi V. Olowookere | C. Heath Turner
Source: The Journal of Physical Chemistry B (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Improving Aqueous Metal Salt Interactions Using Machine-Learned Interatomic Potentials Accurate modeling of aqueous metal salt solutions is essential for understanding processes relevant to environmental safety, energy storage, and separation technologies. Trace metals such as As<sup>3+</sup> at low concentrations pose significant health and environmental risks. They are challenging to simulate due to limitations in both classical force fields (CFFs), which lack accuracy, and <i>ab initio</i> methods, which are restricted to short trajectories. In this study, we develop machine-learned interatomic potentials (MLIPs) to model aqueous AsCl<sub>3</sub> and MgCl<sub>2</sub> using the NequIP/Allegro equivariant graph neural network architecture trained on <i>ab initio</i> molecular dynamics (AIMD) and density functional theory data. Our MLIP models accurately reproduce <i>ab initio</i> energies and forces while capturing solvation structure, ion diffusion, and hydration dynamics more effectively than CFFs (AMBER and UFF models). Our MLIPs achieve energy MAEs < 1 meV/atom and force RMSEs < 40 meV/Å, while providing an O(10<sup>4</sup>) speedup over AIMD. These MLIPs offer a reliable and efficient alternative for modeling trace metal speciation and transport, with implications for improved separation and environmental processes. Molecular dynamics ; Solvation ; Force field (fiction) ; Aqueous solution ; TRACE (psycholinguistics) ; Potential of mean force ; Metal ; Salt (chemistry) ; Potential energy Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Anuradha Upadhyaya | Anurag Panda | Somagni Roy | Ramesh Kumar | Marta Otero | Prasenjit Chakraborty | Santoshi Mohanta | Shirsendu Banerjee | Moonis Ali Khan | Sashikant Nayak | Somnath Chowdhury | Byong‐Hun Jeon | Sankha Chakrabortty | Suraj K. Tripathy
Source: Cellulose (article, 2025-07-25)
Field: Materials Science — Biomaterials

Parametric optimizations of genotoxic dye sorption using zinc-activated rice straw-derived cellulose: a study aligned with circular economy goals (SDG 12) Sorption ; Cellulose ; Zinc ; Parametric statistics ; Rice straw ; Straw ; Circular economy ; Materials science ; Chemical engineering ; Composite material ; Chemistry ; Adsorption ; Mathematics ; Organic chemistry ; Engineering ; Inorganic chemistry ; Biology ; Metallurgy ; Statistics Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Nanomaterials for catalytic reactions
Authors: Yu Wang | Yue Wang | Guohui Yang | Kuang Zhang | Xing Yang | Chunhui Wang | Yu Zhang
Source: Optics Express (article, 2025-04-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of multifunctional tunable dual-layer metalens based on deep learning To address the challenges of complex control in individually tunable meta-atoms and the limited modulation performance of single-layer holistic tunable metalenses, we propose a dual-layer tunable multifunctional metalens that utilizes holistic modulation based on the tunable BaTiO<sub>3</sub> (BTO) material. This design enables rapid switching between different functionalities to be achieved by varying the voltage across the top and bottom layers. To accelerate the device design process, we employ a deep learning algorithm based on the transformer structure and incorporate a band embedding method that aids in predicting the high-frequency response. This approach enables accurate and rapid prediction of the transmittance and phase of meta-atoms in different voltage states, and can generate 100,000 arbitrary geometric meta-atoms in less than 1 s. Detailed simulation results demonstrate that the designed metalens can achieve near-perfect switching between dual-functionality and quadruple-functionality within the visible light range. The switchable functionalities include focal length, focal position, beam deflection angle, beam splitting, and the transitions between these different functionalities. Furthermore, to enhance the manufacturability of the designed metalens, we replace arbitrary geometric meta-atoms with simple geometric meta-atoms to design the structure of the overall metalens, which can also achieve the dual-function switchable effect with minimal performance degradation. Optics ; Layer (electronics) ; Dual layer ; Materials science ; Dual (grammatical number) ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Pedro Sampaio | Bruna Carbas | Andreia Soares | Inês Sousa | Carla Brites
Source: Food Chemistry (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution. Partial Least Squares (PLS) models accurately predicted parameters such as whiteness (R<sup>2</sup> = 0.94), width (R<sup>2</sup> = 0.94), resilience (R<sup>2</sup> = 0.96), and springiness (R<sup>2</sup> = 0.98), highlighting key wavelength regions. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a 17 % error rate in external predictions. Spectral markers at A6032/4457 cm<sup>-1</sup>, A7004/5241 cm<sup>-1</sup>, and A7004/4749 cm<sup>-1</sup> reflected biomolecular differences among varieties. This innovative approach enables precise quantification, classification, and differentiation of rice types, enhancing quality control, improving consumer satisfaction, and optimizing breeding selection processes efficiently. Infrared spectroscopy ; Infrared ; Spectroscopy ; Computer science ; Near-infrared spectroscopy ; Chemistry ; Artificial intelligence ; Materials science ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Yongbo Shi | Yu Bao | Lei Zhang | Ye Su | Ping Qian
Source: Diamond and Related Materials (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Insights into the mechanical and thermal transport properties of 2D BxCyNz from machine-learned interatomic potentials Thermal conductivity ; Molecular dynamics ; Interatomic potential ; Phonon ; Thermal ; Extrapolation ; Thermal stability ; Interfacial thermal resistance Thermal properties of materials ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Yu Zheng | Pan Shi | Ziyue Sun | Yin Tan | Songping Yang | Qin Wu
Source: Journal of Stored Products Research (article, 2025-09-03)
Field: Chemistry — Analytical Chemistry

Hybrid EXP-FEA-ANN modeling for citrus ripeness detection via modal analysis Ripeness ; Finite element method ; Modal ; Engineering ; Structural engineering ; Horticulture ; Biology ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Junwei Yang | Xueqi Yang | Zhibo Zhang | Haoli Qin | Changyuan Wang | Hao Shen | Xin Zhou
Source: Journal of Cleaner Production (article, 2025-07-07)
Field: Chemistry — Analytical Chemistry

Optimizing engineering strategies for delayed coking applications: A machine learning-based multi-objective framework Manufacturing engineering ; Computer science ; Process engineering ; Engineering ; Industrial engineering Petroleum Processing and Analysis ; Coal and Coke Industries Research ; Process Optimization and Integration