jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hussan Ibne Shoukani | Khudija tul Kubra | Sobia Nisa | Jafri Malin Abdullah | Sabahat Qumar | Sabayyel Hassan | Nimra Nasir | Muhammad Faheem Awan
Source: Biomedical Materials & Devices (review, 2025-08-22)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Toxicological Evaluations of NPs and Their Optimization for Biomedical Applications Biochemical engineering ; Management science ; Computer science ; Engineering ethics ; Risk analysis (engineering) ; Engineering ; Medicine Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Microplastics and Plastic Pollution
Authors: B. Muthuraj | M. Sivapragash | Rajesh Rajamoni | Michael Thomas Rex Francis
Source: Materials Research (article, 2025-01-01)
Field: Materials Science — Biomaterials

Bayesian ANN-Based Prediction and Multi-Objective Optimization of Tribological Behavior in Magnesium Alloy AZ91D at Elevated Temperature Using Pareto GA This study uses an artificial neural network (ANN) model to predict the wear rate and friction coefficient of the magnesium alloy AZ91D, based on experimental data from a pin-on-disc tribometer. The model includes three essential process parameters: sliding velocity (m/s), applied load (kg), and sliding distance (km), in addition to the chamber temperature (°C). A total of 27 experimental designs were devised using a Box-Behnken design. The ANN model was trained utilizing the Bayesian regularization approach with one hidden layer of 10 neurons. The developed ANN models for predicting wear rate and coefficient of friction were used as goal functions in a multi-objective Pareto-based genetic algorithm to maximize tribological performance. The ideal solution indicates a sliding velocity of 2 m/s, a load of 5 kg, a sliding distance of 1.5 km, and a chamber temperature of 143°C, yielding a minimal wear rate of 1.7891 mm3/kg·km and a coefficient of friction of 0.1435. Energy Dispersive Spectroscopy and Scanning Electron Microscopy analyses of worn surfaces show that the wear rate decreases with increasing load and sliding velocity at higher temperatures. The oxide layer that forms at high temperatures enhances wear resistance, even under high loads and sliding speeds. Tribology ; Particle swarm optimization ; Scanning electron microscope ; Delamination (geology) ; Bayesian optimization ; Magnesium alloy ; Alloy ; Artificial neural network ; Control theory (sociology) Magnesium Alloys: Properties and Applications ; Mechanical stress and fatigue analysis ; Aluminum Alloys Composites Properties
Authors: Dali Wei (11540194) | Mengfan Li (825430) | Yudi Yang (418600) | Chunmeng Deng (18137677) | Fang Zhu (185566) | Ming Li (91180) | Yibin Deng (410103) | Zhen Zhang (86004)
Source: Figshare (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Ligand Microenvironment-Regulated Nanozymes EnabledMachine Learning-Assisted Sensor Array for Simultaneous Identificationof Phenolic Pollutants Phenolic pollutants pose a great threat to human health due to high toxicity, whereas existing methods are difficult to achieve the rapid recognition of multiple phenolic pollutants. In this study, we developed a novel machine learning-assisted sensor array based on ligand microenvironment-regulated Pt nanozymes for the simultaneous differentiation of five phenolic pollutants (phenol, 2,4-DCP, <i>p</i>-chlorophenol, <i>o</i>-chlorophenol, and <i>m</i>-chlorophenol), wherein four cellulose ligands (carboxymethylcellulose, CMC; methylcellulose, MC; hydroxyethyl cellulose, HC; and hydroxypropyl methyl cellulose, HPMC)-regulated Pt nanozymes (Pt@CMC, Pt@MC, Pt@HC, and Pt@HPMC) with considerable laccase-mimicking activity were designed, and the Pt@CMC nanozyme exhibited the highest catalytic activity, which was about 7.5-folds than that of natural laccase. The calculation of density functional theory revealed that Pt@CMC had a stronger ability for capturing 2,4-DCP molecules, showing higher laccase-like activity. More importantly, the different cellulose ligands endowed four Pt nanozymes with laccase-like activity diverse recognition capability to phenolic compounds; thus, a nanozyme sensor array was developed for the differentiation of five phenolic pollutants. Moreover, the integration of a machine learning algorithm and the nanozyme sensor array successfully achieved accurate identification and prediction of the five phenolic pollutants in real water samples. Therefore, this study provided an emerging sensing strategy for the simultaneous identification of phenolic pollutants, carving a promising path for the application of sensor arrays and machine learning algorithms in environmental monitoring. Pollutant ; Ligand (biochemistry) ; Identification (biology) ; Human health ; Cellulose Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Snehal S. Datwase | Charansing Kayte | R. A. Deshmukh
Source:  (article, 2025-09-03)
Field: Chemistry — Analytical Chemistry

Machine Learning Based Detection of Mango Adulteration Using Spectral Signature Analysis This study focusses on natural, chemically treated, and artificially ripened mangoes and investigates the use of hyperspectral imaging and machine learning to categories mango adulteration. Second-order derivatives were used to preprocess hyperspectral data <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$350-2500 \text{nm}$</tex>, and Principal Component Analysis PCA was used to decrease it. The majority of the variation was captured by the first <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex> components. The performance of three classifiers Partial Least Squares Regression (PLSR), Random Forest, and Support Vector Machine SVM was assessed. With excellent precision and recall for natural mangoes and little misclassification, Random Forest had the highest accuracy (98.5%). Additionally, SVM showed 98.5% accuracy, but for chemically treated samples, it performed somewhat worse in recall. Despite its success in reducing dimensionality, PLSR only managed to classify fresh mangoes with 92.4% accuracy because of overlapping spectral features. The findings show the potential of hyperspectral imaging for accurate and non-destructive food quality inspection, highlighting Random Forest and SVM as reliable techniques for mango adulteration detection. Hyperspectral imaging ; Support vector machine ; Random forest ; Principal component analysis ; Pattern recognition (psychology) ; Partial least squares regression ; Spectral signature Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Yujun Chen | Xiuli Zhu | Peng Wang | Kuangrong Hao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Biomem-Gcn: A Biologically Inspired Memory-Enhanced Graph Convolutional Network for Interpretable Data-Driven Modeling of Complex Industrial Processes Computer science ; Graph ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Kamila Savvidi | Ludwig J. V. Ahrens-Iwers | Lucio Colombi Ciacchi | Dirk Zahn | Martin H. Müser | Robert H. Meißner
Source: The Journal of Chemical Physics (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Efficient treatment of long-range electrostatics in charge equilibration approaches A charge equilibration method based on real-space Gaussians as charge densities is presented. The implementation is part of the Electrode package available in the Large-scale Atomic/Molecular Massively Parallel Simulator and benefits from its efficient particle-mesh Ewald approach. A simple strategy required to switch from the previously used Slater-type orbital (STO) shielding to Gaussians is provided by fitting the Coulomb energy of two Gaussian charge distributions to the repulsion between two STOs. Their widths were optimized for O, Si, and Ti species, obtaining results consistent with previous studies using STOs in the case of SiO2 polymorphs. In the limit of sufficiently narrow Gaussians, it is shown that the implementation converges to electronegativity equalization method results for the case of Ti/TiOx interfaces. The method presented is implemented in a way that is potentially beneficial for the application of modern machine-learning force fields that include long-range electrostatic interactions. Electrostatics ; Coulomb ; Range (aeronautics) ; Charge (physics) ; Gaussian ; Limit (mathematics) ; Statistical physics ; Electromagnetic shielding ; Massively parallel ; Molecular dynamics ; Computational physics ; Computational science ; Materials science ; Computer science ; Physics ; Chemical physics ; Quantum mechanics ; Mathematics ; Electron ; Parallel computing ; Mathematical analysis Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques
Authors: P. Banerjee | Andre Schmeißer | Simone Gramsch
Source:  (article, 2025-12-07)
Field: Materials Science — Polymers and Plastics

Exploring and Predicting Fiber Laydown – A Dual Approach to Spunbond Process Optimization using Informed Machine Learning This study extends previous work on fiber laydown behavior in spunbond processes by combining simulation, scientific visualization, and machine learning (ML). The fiber laydown process is simulated using Fraunhofer ITWM’s FIDYST simulation tool across a Latin Hypercube of material and process parameters. Three output parameters – deposition spread in machine and cross-machine directions (σ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and σ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) and a stochasticity parameter A – are analyzed. Scientific visualization techniques are applied to uncover nonlinear relationships between input factors and deposition behavior. A multi-objective regression model is trained to predict the output parameters, with conformal prediction used to provide confidence intervals. The results demonstrate that ML informed by physical simulation supports faster and more interpretable optimization of complex nonwoven processes. Process (computing) ; Latin hypercube sampling ; Fiber ; Nonlinear system ; Visualization ; Nonlinear regression ; Dual (grammatical number) ; Regression ; Curse of dimensionality Textile materials and evaluations ; Photonic Crystal and Fiber Optics ; Electrospun Nanofibers in Biomedical Applications
Authors: Alexandra Boltasseva
Source:  (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Advancing quantum imaging and device testing with machine learning Single photon emitters represent a critical component for enabling several quantum technologies such as quantum communication, quantum photonic computing, sensing, and imaging. One example of quantum imaging that leverages the non-classical nature of light radiated by quantum emitters is the so-called antibunching super-resolution microscopy. We present a machine learning (ML)-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum on-chip devices hosting various types of quantum emitters as well as quantum material metrology. Additionally, for security applications, we develop a robust, ML-assisted on-chip device testing method that can be used to ensure the integrity of a chip avoiding risks of tampering and failure. Both ML approaches could pave the way to the realization of large-scale, secure quantum on-chip devices. Computer science Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhe Zhang | Zhiwei Sun | Haoming Zou | Xijuan Lv | Ziyang Guo | Shuai Zhao | Qinghai Shu
Source: Defence Technology (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

High-precision quantitative analysis of 3-nitro-1,2,4-triazol-5-one (NTO) concentration based on ATR-FTIR spectroscopy and machine learning 3-Nitro-1,2,4-triazol-5-one (NTO) is a typical high-energy, low-sensitivity explosive, and accurate concentration monitoring is critical for crystallization process control. In this study, a high-precision quantitative analytical model for NTO concentration in ethanol solutions was developed by integrating real-time ATR-FTIR spectroscopy with chemometric and machine learning techniques. Dynamic spectral data were obtained by designing multi-concentration gradient heating-cooling cycle experiments, abnormal samples were eliminated using the isolation forest algorithm, and the effects of various preprocessing methods on model performance were systematically evaluated. The results show that partial least squares regression (PLSR) exhibits superior generalization ability compared to other models. Vibrational bands corresponding to C=O and –NO 2 were identified as key predictors for concentration estimation. This work provides an efficient and reliable solution for real-time concentration monitoring during NTO crystallization and holds significant potential for process analytical applications in energetic material manufacturing. Fourier transform infrared spectroscopy ; Spectroscopy ; Nitro ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Chemical engineering ; Engineering ; Chromatography ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Lihua Zhu | Hao Ma | Jianying Hao | Zeye Lv | Shihao Li
Source: IEEE Transactions on Magnetics (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Research on the Wide-Frequency Loss Prediction Model and Dynamic Hysteresis Simulation Method for Nanocrystalline Materials 1K107B Nanocrystalline materials are widely utilized in high-frequency transformers due to their exceptional electromagnetic properties. Accurate loss prediction and effective hysteresis simulation for nanocrystalline materials are crucial for optimizing the design of high-frequency power electronic devices. This article presents a methodology for loss prediction and hysteresis simulation based on the loss separation theory and the Energetic hysteresis model. First, the hysteresis loss component is determined using the static Energetic hysteresis model. The real magnetic permeability is integrated into the calculation of the eddy current loss component, while accounting for the frequency-dependent behavior of conductivity. A skin effect coefficient is introduced to adjust the effective conductivity, which is subsequently employed to calculate both the eddy current and anomalous loss components. To model the frequency-dependent term of the anomalous loss, a cubic polynomial fitting approach is applied. Next, leveraging the principles of loss separation and field separation consistency, the total magnetic field intensity is expressed as the sum of the hysteresis field, eddy current field, and anomalous effect field. The impact of Energetic hysteresis model parameters on the hysteresis loop is thoroughly analyzed, and a shape factor is incorporated to accurately simulate the dynamic hysteresis loop. Shape factor parameters are further predicted using artificial neural networks. Finally, experimental measurements of nanocrystalline materials are conducted, and the prediction accuracy is assessed through error metrics such as relative error and root mean square error. The proposed wide-frequency loss prediction model demonstrates high accuracy, and the dynamic hysteresis simulation method proves effective. Nanocrystalline material ; Hysteresis ; Materials science ; Magnetic hysteresis ; Computer science ; Stoner–Wohlfarth model ; Condensed matter physics ; Magnetization ; Magnetic field ; Nanotechnology ; Physics Anodic Oxide Films and Nanostructures ; Piezoelectric Actuators and Control
Authors: M. Busch | Gregor Häfner | Jiayu Xie | M. Tacke | Marcus Müller | Christian J. Cyron | Roland C. Aydin
Source: ArXiv.org (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-level particle simulations, where high spatial and temporal resolution leads to rapidly increasing computational demands. To overcome these limitations, hybrid methods that combine simulations with different levels of resolution offer a promising solution. In this context, we present a machine learning-based decision model that dynamically selects between simulation methods at runtime. The model is built around a Multilayer perceptron (MLP) that predicts the expected discrepancy between particle and continuum simulation results, enabling the localized use of high-fidelity particle simulations only where they are expected to add value. This concurrent approach is applied to the simulation of membrane fabrication processes, where a particle simulation is coupled with a continuum model. This article describes the architecture of the decision model and its integration into the simulation workflow, enabling efficient, scalable, and adaptive multiscale simulations. Scalability ; Coupling (piping) ; Particle (ecology) ; Domain (mathematical analysis) ; Fabrication ; Resolution (logic) ; Molecular dynamics ; Particle filter Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Pei‐Chi Yang | C Lee | Yan‐Fu Kuo
Source:  (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Identifying Wood Species Using Macroscopic Images and Deep Learning <b><sc>Abstract.</sc></b> The area of forest in Taiwan exceeds 60%, positioning wood as a critical and economically valuable resource. Significant price disparities exist among different wood species. Thus, it is essential to accurately identify the species of the wood to ensure appropriate utilization of the materials. Conventionally, wood species identification has relied on manual methods. Typical methods include microscopic examinations with histological treatments and sensory evaluations based on smell, texture, and color. These approaches are, however, time-consuming, equipment-intensive, and highly dependent on expert experience, raising the issues of subjectivity and variability. Consequently, alternative methods for wood species identification are needed. This study proposes an automated system for identifying the species of wood logs using macroscopic cross-sectional images and deep learning. A total of 135,178 cross-sectional images from 41 wood species (27 hardwoods and 14 softwoods) were collected using flatbed scanners. A support vector machine (SVM) was first trained to exclude non-target species and anomalous images, such as those with severe blurring, scratches, or knots. Subsequently, a base version of Vision Transformer model (ViT-base) was trained on the filtered dataset to identify the species. The trained SVM and ViT-base model achieved accuracies of 97.6% and 98.4%, respectively. To enhance practical applicability, the ViT-base model was deployed on a cloud platform. Additionally, a chatbot was developed to facilitate user interaction with the model through an application programming interface (API). The results demonstrate that the proposed system provides a robust and efficient alternative to traditional methods, significantly reducing the time and effort required for wood species identification while maintaining a high level of accuracy. <b>Keywords.</b> Vision Transformer (ViT), Macroscopic Cross-Sectional Image, Wood Species Classification, Deep Learning Artificial intelligence ; Computer science ; Deep learning Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Debarati Dey | A. A. Hadiyeva | Aynur N. Jafarova | J.R. Sultanova | Vusala Nabi Jafarova
Source: Advances in Natural Sciences Nanoscience and Nanotechnology (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Framework for investigating the current–voltage topographies prediction model of quantum-confined metallic gate enclosed biomolecular nanoribbon based on DFT and machine learning Abstract This study presents the optimal prediction model for a quantum-confined adenine molecular nanoribbon, developed using a hybrid approach that integrates density functional theory with machine learning. The proposed framework enables precise prediction of electrical characteristics, including a visual representation of the current–voltage ( I – V ) response. Various electronic properties of the quantum-confined biomolecular nanoribbon are predicted through a regression learner model. To generate and supply training data for constructing future prediction models, the approach incorporates a learnable real-space Hamiltonian method alongside k -point sampling. The best-performing regression learner achieves an accuracy of approximately 89% for this quantum-confined adenine nanoribbon structure. Overall, this method delivers faster and more accurate electronic characterization of quantum-confined, bio-inspired nanoscale systems. Density functional theory ; Hamiltonian (control theory) ; Representation (politics) ; Nanoscopic scale ; Regression ; Characterization (materials science) ; Artificial neural network Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Quantum-Dot Cellular Automata
Authors: Alexander Aghili | A. M. Bruce | Dubravko Sabo | Razvan Marinescu
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamics Molecular dynamics (MD) simulations provide atomistic insight into biomolecular systems but are often limited by high computational costs required to access long timescales. Coarse-grained machine learning models offer a promising avenue for accelerating sampling, yet conventional force matching approaches often fail to capture the full thermodynamic landscape as fitting a model on the gradient may not fit the absolute differences between low-energy conformational states. In this work, we incorporate a complementary energy matching term into the loss function. We evaluate our framework on the Chignolin protein using the CGSchNet model, systematically varying the weight of the energy loss term. While energy matching did not yield statistically significant improvements in accuracy, it revealed distinct tendencies in how models generalize the free energy surface. Our results suggest future opportunities to enhance coarse-grained modeling through improved energy estimation techniques and multi-modal loss formulations. Matching (statistics) ; Energy (signal processing) ; Molecular dynamics ; Term (time) ; Yield (engineering) ; Energy landscape ; Potential energy ; Work (physics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications