jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Niu | Xingjing Wang | Shaoping Wang | Di Liu | Xinyuan Zhang | Alexander I. Gavrilov
Source:  (article, 2025-08-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on nonlinear modeling and precision displacement control of giant magnetostrictive actuator To address the nonlinear issues in the precision displacement control of the giant magnetostrictive actuator(GMA), a nonlinear mathematical model was developed based on the J-A hysteresis model. This model takes into account the dynamic loss characteristics of giant magnetostrictive materials (GMM) as well as the structural nonlinearity of the actuator. A long short-term memory neural network (LSTM) feedforward compensation PID control strategy is proposed to achieve precise control of GMA by adjusting controller parameters in real-time. The control model is constructed using simulation software, and a GMA system test platform is developed. Simulation analysis and experimental verification are conducted to evaluate the performance of the GMA system. The results show that the LSTM feedforward compensation PID control has good position tracking and anti-interference capabilities, and the dynamic tracking error is kept within 1μm, which greatly improves the control accuracy. Control theory (sociology) ; Feed forward ; PID controller ; Displacement (psychology) ; Nonlinear system ; Compensation (psychology) ; Actuator ; Feedforward neural network Magnetic Properties and Applications ; Shape Memory Alloy Transformations ; Magnetic Bearings and Levitation Dynamics
Authors: Xueqing Wang (210284) | Lan Wei (244030) | Fan Li (49953) | Zhangmei Hu (12055941) | Meikun Fan (1699705)
Source: Figshare (article, 2025-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data FusionEnhanced High-Dimensional SERS FingerprintsConstruction via Dual-Wavelength and Multisubstrate Strategy for PreciseWastewater Identification It is well-known that traditional label-free surface-enhanced Raman spectroscopy (SERS) can capture fingerprint information on analyte, providing a foundation for target identification and differentiation. However, the conventional one-dimensional spectral data obtained through traditional SERS methods is insufficient for characterizing samples with complex chemical compositions, such as wastewater, or for tackling more intricate challenges, including tracing pollution sources, where a more comprehensive analytical profile is necessary. Herein, we introduce “SERSynergy”, a data-fusion-driven machine learning approach that integrates dual-wavelength and multisubstrate data to generate a holistic SERS fingerprint, which allows for precise and robust wastewater identification. This method leverages complementary spectral features of wastewater samples by collecting a total of 12,000 spectra using four types of noble metal nanoparticles under two excitation wavelengths. A hybrid feature-decision fusion strategy cross-combined spectral features from various conditions to form high-dimensional fingerprints, which were then evaluated using optimized machine learning models and consolidated via probability-level fusion. The “SERSynergy” method demonstrated an identification accuracy of up to 99.67% for wastewater samples. Furthermore, when validated with blind sample testing, the method maintained an accuracy of 96.67%. Overall, the developed approach shows great promise for efficiently and accurately identifying wastewater samples, and it has potential applications in the precise acquisition of spectral features and identity discrimination in complex matrix samples. Wastewater ; Sensor fusion ; Identification (biology) ; Spectral signature ; Complex matrix ; Fusion ; Tracing ; Pattern recognition (psychology) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced oxidation water treatment
Authors: Ameer Malik Shaik (21263019) | Veera Siva Reddy B. (21263022) | Durga Prabhas S. (21263025) | Chandrasekhara Sastry C (21263028) | J. Krishnaiah (21263031)
Source: Figshare (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Supporting Information_Complete Dataset. <div> The development of lightweight, corrosion-resistant metallic lattice structures has gained significant attention in aerospace, defense, and structural applications, where material durability and weight optimization are critical. This study investigates the corrosion behavior of Laser Powder Bed Fusion (LPBF)-fabricated A286 steel honeycomb, Body-Centered Cubic (BCC), and gyroid lattices, comparing their performance against conventional materials such as Rolled Homogeneous Armor (RHA), Maraging High Strength Steel (MHA), and High-Nitrogen Steel (HNS). Corrosion testing was conducted using accelerated salt spray exposure, and the results were validated through computed tomography (CT)-based structural integrity analysis and machine learning-based predictive modeling. The experimental findings revealed that lattice structures exhibited significantly lower corrosion rates than conventional bulk materials, with the honeycomb lattice demonstrating the highest corrosion resistance (1.218 mm/year), followed by BCC (1.311 mm/year) and gyroid (1.671 mm/year). Compared to RHA, the honeycomb lattice exhibited a 57.23% reduction in corrosion rate, confirming its superior electrochemical stability. CT scan evaluations further highlighted differences in density distribution and geometric fidelity, with honeycomb lattices showing the most uniform porosity, while BCC structures displayed localized density variations at nodal intersections. To enhance predictive capabilities, various machine learning (ML) algorithms were employed to model corrosion behavior based on weight-loss measurements and lattice topology. Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. Linear Regression also performed well, while ensemble models such as Random Forest and XGBoost exhibited higher error margins due to dataset linearity constraints. Residual analysis and graphical interpretations further validated the stability and predictive reliability of ML-based corrosion assessments, demonstrating their feasibility as an alternative to traditional experimental methods. This study presents a comprehensive framework for integrating experimental corrosion testing, computational modeling, and CT-based defect analysis, offering a scalable approach to optimizing micro-lattice designs for corrosion-sensitive applications. The findings highlight the potential of LPBF-fabricated metallic lattices for aerospace, defense, and marine structures, where enhanced corrosion resistance, reduced material degradation, and predictive maintenance strategies are essential for long-term operational performance. </div> Corrosion ; Lattice (music) ; Durability ; Lattice constant ; Residual Machine Learning in Materials Science ; Big Data and Digital Economy ; Machine Learning in Healthcare
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Enhanced Dielectric Strength Prediction and Real-Time Monitoring via Multi-modal Data Fusion and Bayesian Neural Networks **Abstract:** This paper introduces a novel framework for accurate dielectric strength prediction and real-time monitoring in high-voltage insulation systems. The approach combines statistical analysis of material properties, imaging data from X-ray computed tomography (CT) scans, and environmental sensor data (temperature, humidity, voltage stress) using a Bayesian Neural Network (BNN) architecture. This allows for significantly improved prediction accuracy compared to traditional empirical models, while incorporating uncertainty quantification capabilities applicable to failure risk assessment and preventative maintenance strategies. The proposed system allows for an estimated 30% improvement in predictive accuracy over current finite element analysis (FEA) methods, leading to reductions in equipment downtime and improved safety standards within power transmission and distribution infrastructure, representing a multi-billion dollar market opportunity. Downtime ; Artificial neural network ; Bayesian probability ; Prediction interval ; Sensor fusion ; Bayesian network ; Reliability (semiconductor) ; Transmission (telecommunications) ; Experimental data ; Weibull distribution High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: M. Lavanya | A.Prasad | C.Ramesh Kumar | G B Santhi | R.Dilli Babu | R.Nandhakumar
Source:  (article, 2025-11-05)
Field: Materials Science — Polymers and Plastics

Wear Rate Modelling in Bamboo Fibre Polymer Composites by Pin on Pin-on-Disc Method using Tree-based Regression Algorithms This study focuses on modeling the wear rate of bamboo fibre reinforced polymer composites machined using the pin-on-disc method. Wear behavior was predicted with the use of machine learning algorithms, including Decision Tree (DT), Random Forest (RF), and XGBoost (XGBoost). The models were trained and validated with experimental data. XGBoost algorithm performed the best with the highest R 2 value of 92.22 percent and the best RMSE. Decision Tree also demonstrated competitive accuracy, having the least MAE of 0.1152. The findings establish that tree-based models, especially XGBoost, are effective in predicting wear rate in natural fibre composites accurately. The results of the three regression models of trees, Decision Tree (DT), Random Forest (RF), and XG Boost, on predicting the wear rate of bamboo fibre polymer composites using the pin-on-disc technique. XG Boost had the best RMSE (0.2054), which means that it has better predictive accuracy than the other models. The DT also scored fairly well with RMSE of 0.2731 and a minimum MAE of 0.1152. RF and XGBoost had the same value of MAE (0.2263). Regarding R-squared, XG Boost was the best (92.22%), DT (86.25%), and RF (84.16%). In general, XG Boost turned out to be the most efficient model to predict wear rates in the current study. Random forest ; Bamboo ; Decision tree ; Regression analysis ; Linear regression ; Mean squared error ; Predictive modelling Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Scientific and Engineering Research Topics
Authors: Manasa Kaniselvan | Alexander Maeder | Chen Hao Xia | Alexandros Nikolaos Ziogas | Mathieu Luisier
Source: ArXiv.org (preprint, 2025-07-04)
Field: Materials Science — Materials Chemistry

Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales, enabling investigation of the electronic properties of materials with extended defects, interfaces, or exhibiting disordered phases. However, as interactions between atomic orbitals typically extend over 10+ angstroms, the graph representations required for this task tend to be densely connected, and the memory requirements to perform training and inference on these large structures can exceed the limits of modern GPUs. Here we present a distributed eGNN implementation which leverages direct GPU communication and introduce a partitioning strategy of the input graph to reduce the number of embedding exchanges between GPUs. Our implementation shows strong scaling up to 128 GPUs, and weak scaling up to 512 GPUs with 87% parallel efficiency for structures with 3,000 to 190,000 atoms on the Alps supercomputer. Equivariant map ; Embedding ; Scaling ; Graph ; Artificial neural network ; Inference ; Graph embedding ; Graph theory Machine Learning in Materials Science
Authors: Alfonso; id_orcid 0009-0002-3711-4231 Annarelli | Igor Poltavsky | C. Filippi
Source: University of Twente Research Information (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Toward excited-state molecular dynamics at quantum Monte Carlo quality To simulate nonadiabatic molecular dynamics, an electronic structure method is needed that can accurately describe multiple electronic states and different regions of the potential energy surfaces, while remaining computationally feasible for large numbers of trajectories. We focus on azomethane as a test system, whose excited-state dynamics are particularly sensitive to the choice of wave function in multi-determinant methods. To assess the performance of different electronic descriptions, we compare energies and forces computed with quantum Monte Carlo (QMC) with those from more conventional methods, aiming to evaluate the suitability of QMC as a reference for excited-state dynamics. In parallel, we are training a machine learning model on ab initio data to support efficient trajectory-based simulations of photoinduced processes, with a focus on minimizing the training set size ahead of retraining on QMC data. Quantum Monte Carlo ; Monte Carlo method ; Wave function ; Focus (optics) ; Electronic structure ; Molecular dynamics ; Quantum ; Ab initio ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ashkan Maleki | Arash Mehdizad | Esmaeil Hassani | Mohammad Naveshki | Alimorad Rashidi | Abdolreza Farhadiand | Vahid Rezaverdinejad | Fariba Aghazadeh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Design of High-Performance Activated Carbons for Enhanced Co2 Capture Computer science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Liyun Fan | Kui Xu | Chen Chen | Chongchong Shen | Haijie Wang | Yunpeng Wei
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Multi-Criteria Evaluation and Operating Parameter Optimization of the SOFC-GT System under Variable Loads &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;At present, due to the complexity and nonlinearity, the thermal safety and economic feasibility assessment and optimization of the Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT) system under variable loads is important to extend the service life and reduce the cost. To solve these problems, this paper proposes a top-level cyclic SOFC-GT system, which considers the design of two-stage preheaters, as well as the impact of material reaction kinetics and thermoelectric coupling characteristics on system performance. Furthermore, the multi-criteria evaluation of the SOFC-GT system under variable loads has been studied, with evaluation indicators primarily including thermodynamic and economic indicators. Afterwards, a Spearman-based parametric sensitivity analysis is used to explore the response trends of performance indicators within the SOFC-GT system. Additionally, an intelligent learning method based on convolutional neural network is designed to determine the dynamic behavior between operational parameters and performance indicators. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced, aiming to get the optimal combination of operational parameters. The research results indicate that the reduction of fuel utilization, air excess ratio, and bypass valve opening can effectively improve the net power output of SOFC-GT system and significantly reduce the EPC. Meanwhile, the MOPSO algorithm has effectively improve the performance of the SOFC-GT system. Compared to before optimization, the net power and EPC have been optimized by 7.25% and 11.16%, respectively.&lt;/div&gt;&lt;/div&gt; Variable (mathematics) ; Computer science ; Reliability engineering ; Engineering ; Mathematics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Yuhao Zheng (836715) | Guohailin Xu (21684517) | Jingwen Chen (489529) | Xinyi Wang (559030) | Dou Xu (11001390) | Yapeng Li (124282) | Mengwei Chen (3128997) | Wenbo Jiang (4725909) | Jie Kong (702788) | Shan Chen (232477) | Yimin Fang (115705)
Source: Figshare (article, 2025-07-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical Resolutionof the Morphological Change ofSingle Nanoparticles at Native Conditions: A Nonsuper-Resolution Approach Label-free optical imaging of the morphology of single nanoparticles under native conditions remains a challenging task despite its importance in the synthesis, properties, and applications of nanomaterials. Here, we developed an angular scanning dark-field scattering microscopy (ASDFSM) that can identify the particle morphologies by the features of their angle-dependent scattering intensities without increasing spatial resolution. ASDFSM can unambiguously resolve the morphology of single nanoparticles with sizes down to 1/5 wavelength of the incident light in a large field of view, facilitating the simultaneous imaging of a large number of nanoparticles with statistical significance. Coupling with a random forest machine learning algorithm, their morphologies can be well-identified with an average accuracy of 96%, facilitating <i>in situ</i> monitoring of the corrosion and growth kinetics of single nanoparticles in a high-temperature solution. The ASDFSM possesses the advantages of high-throughput, noninvasiveness, dynamic traceability, and simplicity over the present methods, promising its wide applications in nanoscience. Nanoparticle ; Scattering ; Light scattering ; Optical microscope ; Particle (ecology) ; Microscopy ; Coupling (piping) ; Morphology (biology) Gold and Silver Nanoparticles Synthesis and Applications ; Surface Roughness and Optical Measurements ; Optical Polarization and Ellipsometry
Authors: Yan Wu | Bin Wang | Jian Chen | Xiaolin Huang | Jun Xu | Wenguang Wei | Kefu Chen
Source: Industrial Crops and Products (article, 2025-04-06)
Field: Materials Science — Polymers and Plastics

Optimizing mechanical properties of sustainable industrial fiber composites with integrated neural networks and bio-inspired algorithms Sustainable natural fiber composites hold promising prospects in various industrial applications due to their eco-friendly attributes and renewable nature. In this study, we introduce an innovative dual-integrated artificial neural network framework enhanced by the sparrow search algorithm (SSA) and genetic algorithm (GA) to overcome the challenges posed by diverse raw materials and complex manufacturing processes. The SSA component improves global exploration and local optimization, resulting in significantly lower mean square error (MSE) and root mean square error (RMSE) compared to traditional backpropagation (BP) neural networks. Further fine-tuning of the initial weights and thresholds via GA substantially boosts model performance. Our experimental analysis demonstrates an impressive optimization rate of 99.88 %, with critical mechanical indices—such as a tearing index of 14.75 mN·m²/g, a tensile index of 68.30 N·m/g, an elasticity modulus of 4.02 GPa, and air permeability of 6.63 μm/(Pa·s)—being achieved. SHAP analysis further decodes feature-property relationships, enabling actionable insights for fiber-to-matrix ratio tuning. This comprehensive framework not only enhances prediction accuracy and model interpretability but also offers a scalable solution for the design and optimization of sustainable industrial fiber composites, advancing their application in green bio-composites. • SSA-BP improves sustainable industrial fiber composite property prediction accuracy. • The model shows superior robustness and reduces errors in mechanical predictions. • SHAP analysis highlights key factors, improving model interpretability and insight. • GA-BP optimizes fiber-to-matrix ratios, with experimental errors below 3 %. • The optimized industrial fiber composite exhibited superior mechanical performance. Artificial neural network ; Fiber ; Algorithm ; Composite material ; Materials science ; Computer science ; Artificial intelligence Textile materials and evaluations ; Advanced machining processes and optimization ; Natural Fiber Reinforced Composites
Authors: Jing Xue | Feijia Xu | Lijun Ge | Huixiang Wu | Weibo Lu | Shitong Wang | Qiaoling Zhao | Honghai Wang | Jingjing Liang | Zi-Jian Yang | Zhijian Wang | Qing Shen
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2025-07-01)
Field: Chemistry — Spectroscopy

From Laboratory Exploration to Practice: Applications, Challenges, and Development Trends of Rapid Evaporative Ionization Mass Spectrometry Technology in Food Detection The expansion of the food industry and the acceleration of global trade have underscored the importance of food authenticity identification (FAI) for ensuring food safety, maintaining market integrity, and protecting consumer interests. Rapid evaporative ionization mass spectrometry (REIMS), as an emerging food analysis technology, has demonstrated significant potential in FAI due to its real-time, accurate, and in-situ characteristics. This review article provides a comprehensive overview of REIMS technology, encompassing its developmental trajectory, experimental setup, operational procedures, ion sources, and ionization mechanisms. Then, it delves into the application of REIMS in data processing, particularly highlighting the advantages of chemometrics analysis, multivariate statistical analysis, and machine learning algorithms in FAI. The review delves into various application cases of REIMS in the food sector, highlighting its effectiveness in identifying food materials, monitoring processing procedures, and rapidly detecting adulteration and fraud. Despite its promise, REIMS faces challenges such as enhancing sensitivity and accuracy and broadening its applicability to diverse ionization targets. In the future, with ongoing technological advancements and in-depth research, REIMS technology will play a more significant role in food safety supervision. In addition, integrating REIMS with other analytical techniques and standardizing FAI processes based on REIMS will be pivotal areas for future research and development. These endeavors will not only bolster the technology's capabilities but also contribute to the sustainable growth of the food industry by ensuring higher standards of food safety and authenticity. Mass spectrometry ; Environmental science ; Chemistry ; Process engineering ; Environmental chemistry ; Chromatography ; Engineering Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical chemistry methods development