jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Congli Mei | Shuai Lu | Xiaolin Zhou | Fanzhen Meng | Hui Jiang
Source: Foods (article, 2026-02-15)
Field: Chemistry — Organic Chemistry

Rapid and Accurate Quantification Detection of BHT in Edible Oils Using Raman Spectroscopy Combined with Chemometric Models The chemical composition of vegetable cooking oils is a key parameter in determining the quality of their products. Antioxidants are widely used in these products to extend their shelf life. In this study, the concentration of butylated hydroxytoluene (BHT) in edible oil was quantitatively determined by Raman spectroscopy combined with chemometrics. Initially, Raman spectra of edible oil samples with varying concentrations of BHT were obtained. Subsequently, three variable selection methods were applied to the pre-processed spectra. Optimised characteristic wavelengths were then used to establish a Radial Basis Function (RBF) neural network and partial least squares (PLS) models. The impact of variable selection on feature wavelengths was evaluated for both models in both independent and combined cases. The results demonstrate that the features identified through multiple variable selection methods correlate highly with the BHT content and can be utilised to develop high-precision detection models. The findings indicate that the PLS model, optimised using competitive adaptive reweighting (CARS), achieved the best prediction performance, with an average RP2 of 0.9687, and RMSEP of 3.1211. These results demonstrate the feasibility of using Raman spectroscopy combined with chemometrics for the rapid screening of BHT in edible oils. While the current study focuses on a broad concentration range to validate the method's linearity, further optimisation is required for trace-level detection to meet strict regulatory limits. Chemometrics ; Partial least squares regression ; Feature selection ; Butylated hydroxytoluene ; Edible oil ; Raman spectroscopy ; Analytical Chemistry (journal) Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Eleonora Bianca | Ghasem Beiginalou | Ada Ferri | Gianluca Boccardo
Source: Textiles (article, 2026-01-08)
Field: Materials Science — Polymers and Plastics

CFD and Machine Learning Approaches for Predicting Air Permeability in Technical Textiles Predicting the thermo-physiological comfort of technical clothing requires an understanding of how microscopic textile structures influence macroscopic properties such as air, heat, and moisture permeability. This work represents the first step towards a multi-scale predictive tool capable of estimating key comfort-related properties from the geometrical features of woven fabrics. Focusing on air permeability, the effect of structural and design parameters was investigated while keeping the fibre material (cotton) constant. A computational framework that combines validated Computational Fluid Dynamics (CFD) simulations with a Fully Connected Neural Network (FCNN) was developed, enabling fast and accurate predictions before production. The CFD model accounts for both intra- and inter-yarn porosity, ensuring reliability across a wide range of fabric configurations. The FCNN, trained on simulation and literature data, achieved a mean absolute relative error of 2.01% and a maximum error of 7.72%, demonstrating excellent agreement with experimental results. The analysis highlights how weave type and yarn density govern airflow resistance, offering an efficient tool for the design and optimisation of breathable technical textiles. Computational fluid dynamics ; Artificial neural network ; Airflow ; Air permeability specific surface ; Yarn ; Range (aeronautics) ; Woven fabric ; Work (physics) Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Epoxy Resin Curing Processes
Authors: Assia Aboubakar Mahamat | Moussa Mahamat Boukar | Nordine Leklou | Philbert Nshimiyimana | Ifeyinwa Ijeoma Obianyo | Blasius Ngayakamo
Source: Results in Engineering (article, 2026-02-22)
Field: Materials Science — Polymers and Plastics

Predicting hygro-mechanical properties of natural Borassus fiber reinforced bio-based alkali activated matrix: an ensemble learning approach • This study pioneered the development of a Borassus fruit fiber reinforced earthen matrix bio-composite with the fiber’s impact on the bio-composite’s crucial hygro-mechanical characteristics. • A novel primary dataset of experimentally build hygro-mechanical properties was established and expertly leveraged to train and validate a suite of ensemble learning models • A comprehensive comparative analysis of seven machine learning models, encompassing both ensemble and individual approaches, was conducted to accurately predict the bio-composite's hygro-mechanical behaviour. • Ensemble learning (EL) methodologies and linear regression exhibited superior predictive accuracy for both hygroscopic and mechanical properties compared to other evaluated models. This study uses machine learning (ML) to predict the effect of natural Borassus fruit fiber reinforcement on the hygro-mechanical properties of a bio-based earth-based bio-composite, enhancing sustainable construction. The study used experimental results of the hygro-mechanical properties to develop a primary dataset for training and testing ML models (validation of 80/20). Various models including stacking and voting ensemble learning (EL), linear regression (LR), support vector machine (SVM), gradient boosting (GBoost), XGBoost, and random forest (RF) were tested, with EL methods showing superior predictive performance for hygro-mechanical properties compared to individual models. Ensemble methods generally outperformed individual models. Specifically, gradient boosting and stacking EL achieved the highest R² of 99.2% for hygroscopic properties. For flexural strength, linear regression and stacking ensemble learning achieved perfect R² values of 100%. The developed models have the potential to optimize bio-composite design, predict material performance in various environments, and accelerate the development of sustainable construction materials. Further research is needed to enhance the performance of less effective models through hyperparameter tuning and exploring different kernel functions. Ensemble learning ; Random forest ; Support vector machine ; Boosting (machine learning) ; Regression ; Ensemble forecasting ; Gradient boosting ; Linear regression Natural Fiber Reinforced Composites ; Aluminum Alloys Composites Properties ; Scientific and Engineering Research Topics
Authors: Tathamay Basu | Gourrab Roy | Mohit Kumar | Jhuma Sannigrahi | Vincent Caignaert | Devashibhai Adroja | Ekta Kushwaha | Sayan Ghosh | Wilfrid Prellier | Vincent Hardy
Source: Research Square (preprint, 2026-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum Bi-stability and Robust Room-Temperature Spin Excitation in a Single-Ion Magnet Magnetism ; Magnon ; Inelastic neutron scattering ; Spin (aerodynamics) ; Neutron diffraction ; Excitation ; Coupling (piping) ; Neutron scattering ; Quantum Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Theoretical and Computational Physics
Authors: Rafael Martínez-Palou
Source: Geoenergy Science and Engineering (article, 2026-02-13)
Field: Chemistry — Analytical Chemistry

Transportation of heavy and extra-heavy crude oil by pipelines: An updated review Asphaltene ; Crude oil ; Pipeline (software) ; Pipeline transport ; Raw material ; Petroleum ; Energy consumption ; Asphalt ; Production (economics) Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Zili Tang | Xiaoxin Xie | Xinqi Liu | Xing Zhang | Fei Liu
Source: Journal of Applied Physics (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Hash-matching-based local density matrix reuse method for efficient <i>ab initio</i> electronic structure construction in large-scale complex systems First-principles electronic structure calculations for large-scale material systems with defects or dopants remain a major computational bottleneck in atomistic simulations. Here, we propose a target-driven, non-learning-based method termed HaMLR (Hash-Matching-based Local density-matrix Reuse) to efficiently construct the density matrix of periodic atomic structures containing local defects or dopants. Leveraging the nearsightedness principle of electronic matter, the method systematically scans all atoms in the target system to extract local substructures within a defined nearsightedness radius, thereby covering the full sample space of local environments. Each substructure is encoded based on its geometric and chemical features, hashed, and deduplicated. Distinct substructures are then evaluated using self-consistent density functional theory (DFT) calculations to obtain the density-matrix blocks between the central atom and its neighbors within the cutoff radius. During reconstruction, the full-system density matrix is assembled by matching local environments via hash values and reusing the precomputed local density blocks—thereby avoiding full-scale DFT calculations. Unlike machine learning-based approaches, HaMLR does not require model training, offering improved physical consistency and computational efficiency. Validation on defective graphene, MoS2, and doped silicon demonstrates that HaMLR achieves high accuracy while significantly accelerating density-matrix construction, providing an efficient and robust alternative for large-scale electronic structure modeling. Electronic structure ; Density functional theory ; Bottleneck ; Substructure ; Hash function ; Atom (system on chip) ; Matrix (chemical analysis) ; Reuse Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; 2D Materials and Applications
Authors: Haowei Zheng | Shuo Xu | Kexiang Wang | Lei Zhao
Source: Symmetry (article, 2026-03-07)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Soluble Solids Content in Multiple Varieties of Hami Melon Based on Hyperspectral Imaging and Machine Learning Hami melon is a widely consumed fruit worldwide, and its sweetness, characterized by soluble solids content (SSC), is a key indicator of fruit quality and commercial value. In this study, hyperspectral imaging combined with machine learning was systematically applied to develop non-destructive models for SSC prediction in multiple Hami melon varieties. Four varieties, namely ‘Xizhoumi’, ‘Jiashigua’, ‘Jinfenghuang’, and ‘Heimeimao’, with a total of 160 samples, were used as the test materials. Hyperspectral images were collected, and SSC was measured at two pulp positions for each sample (denoted as BRIX1 and BRIX2). After applying preprocessing methods including Standard Normal Variate (SNV) transformation and Savitzky–Golay smoothing, five machine learning models were compared: XGBoost, LightGBM, Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). Furthermore, an ensemble modeling strategy based on residual predictive deviation (RPD) weighting from the validation set was proposed. The results show that all models could effectively predict SSC, with the ensemble model achieving the best performance: the coefficients of determination (R2) for BRIX1 and BRIX2 were 0.848 and 0.833, the root mean square errors (RMSEs) were 0.992 and 0.899, the Mean Absolute Percentage Errors (MAPEs) were 6.90% and 6.76%, and the RPD values were 2.57 and 2.45, respectively, demonstrating its strong quantitative analysis capability. This performance benefited from three core optimized designs adopted in this study: (1) a multi-cultivar experimental design that verified the stable correlation between sugar-related spectral features and internal SSC across different Hami melon varieties; (2) an RPD-weighted ensemble modeling strategy that balanced the fitting ability and generalization performance of linear and nonlinear models; and (3) a dual-position SSC measurement design that validated the robustness of the model for SSC prediction at different spatial positions in the pulp. This study confirms the feasibility of hyperspectral imaging technology for non-destructive SSC detection in the four tested Hami melon varieties under laboratory-controlled conditions. The proposed ensemble model achieved a marginal but stable improvement in overall prediction accuracy across the tested varieties compared with the optimal single model, providing a preliminary methodological reference and data support for the development of cross-cultivar non-destructive SSC detection models for Hami melon. Hyperspectral imaging ; Partial least squares regression ; Support vector machine ; Random forest ; Pattern recognition (psychology) ; Residual ; Test set ; Least squares support vector machine ; Weighting Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Yatramohan Jana | Czesław promotor Rudowicz | Muhammed Açıkgöz
Source: Computational Condensed Matter (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Machine learning-enabled prediction of lattice constants of A2B2X7 pyrochlores via cross-validated multiple linear regression model Electronegativity ; Lattice constant ; Ionic radius ; Linear regression ; Lattice (music) ; Lattice energy ; Kriging ; Regression Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Inorganic Chemistry and Materials
Authors: Han Chen | Yufeng Ning | Y.B. Chen | Jinjian Yan | Jie Chen | Liang Liu | Yinfeng Long | Siwen Zhang | Kai Liu | Mao Chen | Jinxiu Zhao | Mengwei Si | Xinwei Zhang | Yishu Zhang | Du Xiang | Lin Wang
Source: ACS Applied Materials & Interfaces (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Electrically Tunable WSe <sub>2</sub> /α-In <sub>2</sub> Se <sub>3</sub> van der Waals Ferroelectric Heterostructure for Reconfigurable Photodetection and Neuromorphic Computing The growing demand for compact and energy-efficient hardware in artificial intelligence and edge computing calls for multifunctional electronic systems beyond conventional architectures. van der Waals (vdW) ferroelectric semiconductors, which couple switchable polarization with tunable carrier transport, offer a promising avenue for reconfigurable electronics. Here, we demonstrate a WSe<sub>2</sub>/α-In<sub>2</sub>Se<sub>3</sub> vdW heterostructure that enables dynamic and nonvolatile reconfiguration of electrical functionality within a single device. Through bias-mediated control of ferroelectric polarization and the associated interfacial carrier redistribution, the heterostructure exhibits dual-mode operation: as a high-performance photodetector with a detectivity exceeding 10<sup>12</sup> Jones and as a nonvolatile memory element. Furthermore, the device emulates biological synaptic plasticity, supporting the implementation of artificial neural networks (ANNs) that achieve 96.76% recognition accuracy on the MNIST data set. This work establishes a versatile and electrically reconfigurable platform based on vdW ferroelectric heterojunctions, enabling dual-mode operation for photodetection and neuromorphic computing. Neuromorphic engineering ; Photodetection ; Ferroelectricity ; Heterojunction ; Non-volatile memory ; Photodetector ; Control reconfiguration ; Photodiode 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Yaohui Gu | Binbo Li | Lingyang Jiang | Yuhui Hu | Wenqiang Liu | Lijun Xu | Pengfei Zhai | Jie Liu | JINGLAI DUAN
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation Gallium oxide (Ga2O3) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in understanding phase-transformation behaviors under nonequilibrium conditions. Here, we develop a robust, accurate, and computationally efficient machine-learning interatomic potential (MLIP) for Ga2O3 based on the neuroevolution potential (NEP) framework combined with an energy-dependent weighting strategy. The resulting NEP potential demonstrates clear advantages over the state-of-the-art tabGAP potential with respect to both accuracy and computational efficiency. Furthermore, we introduce a physically process-oriented sampling strategy to systematically augment the training dataset, thereby enhancing the MLIP performance for targeted physical phenomena. As a representative application, a dedicated NEP potential is constructed for swift heavy-ion (SHI) irradiation simulations of \b{eta}-Ga2O3. The simulated results are in quantitative agreement with experimental observations and provide a consistent physical explanation for the reported experimental discrepancies regarding phase transformations in the ion track of \b{eta}-Ga2O3. Gallium ; Neuroevolution ; Weighting ; Observable ; Molecular dynamics ; Multiscale modeling ; Physical system ; Irradiation Machine Learning in Materials Science ; Ga2O3 and related materials ; GaN-based semiconductor devices and materials
Authors: Jacob Wekalao | Hussein A. Elsayed | Haifa A. Alqhtani | Abdulkarem H. M. Almawgani | Hussein S. Gumaih | Yousif S. Adam | Ahmed Mehaney | William Ochen
Source: AIP Advances (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced graphene–MXene–black phosphorus multilayered metasurface sensor for high-sensitivity terahertz brain tumor detection In this research, we present a multilayer metasurface sensor design integrating graphene, MXene, black phosphorus, and gold for the ultrasensitive detection of brain tumor biomarkers in liquid biopsy samples. The hierarchical structure consists of a MXene-coated rectangular resonator, a black phosphorus-coated square resonator, a gold-coated circular ring, and a graphene-based circular substrate. This architecture was systematically optimized through comprehensive numerical simulations using COMSOL Multiphysics 6.3, integrated with machine learning frameworks. The proposed sensor demonstrates an outstanding sensitivity of 2308 GHz/RIU across a physiologically relevant refractive index range (1.3333–1.4833), significantly outperforming current state-of-the-art devices. Performance analysis identifies an optimal sensing regime at RI = 1.3425, achieving a figure of merit of 20.79 RIU−1 and a detection limit as low as 0.079 RIU. Detailed investigations of the transmission spectra under varying graphene chemical potentials (0.1–0.9 eV), incident angles (0°–80°), and geometric modifications of the resonators reveal highly tunable sensing behavior. Furthermore, Random Forest Regression models achieve predictive accuracies of 85%–100%, enabling reliable estimation of sensor performance across diverse operating conditions. Collectively, these results establish a solid foundation for employing advanced 2D material–based metasurfaces in minimally invasive and early-stage brain tumor diagnostics, thereby advancing the capabilities of next-generation liquid biopsy technologies. Terahertz radiation ; Figure of merit ; Black phosphorus ; Multiphysics ; Graphene ; Sensitivity (control systems) ; Resonator Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Adil Moutaouaffiq | Najat El Gaidi | Abdelghafour El Mekkaouy | Fatima Zohra Moutaouaffiq | Ahmed Sali | Abdellah Tahiri | Rodouan Touti
Source: Computational Condensed Matter (article, 2026-04-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-principles study of structural stability and physical properties of RaThX3 (X = O, S, Se) perovskites Structural stability ; Stability (learning theory) ; Structural change ; Perovskite (structure) ; Thermal stability Magnetic and transport properties of perovskites and related materials ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Antonius v. Strachwitz | Karim Kacper Kacper Alaa El-Din | Ana C. C. Dutra | S. M. Vinko
Source: Machine Learning Science and Technology (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Data-efficient learning of exchange-correlation functionals with differentiable DFT Abstract Machine learning (ML) density functional approximations (DFAs) have seen a lot of interest in recent years, often being touted as the replacement for well-established non-empirical DFAs, which still dominate the field. Although highly accurate, ML-DFAs typically rely on large amounts of data, are computationally expensive, and fail to generalize beyond their training domain. In this work we show that differentiable DFT with Kohn–Sham regularization can be used to accurately capture the behavior of known local density approximations from small sets of synthetic data without using localized density information. At the same time our analysis shows a strong dependence of the learning on both the amount and type of data as well as on model initialization. By enabling accurate learning from sparse energy data, this approach paves the way towards the development of custom ML-DFAs trained directly on limited experimental or high-level quantum chemistry datasets. Differentiable function ; Regularization (linguistics) ; Density functional theory ; Work (physics) ; Energy (signal processing) ; Energy density ; Training set ; Local-density approximation Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Yuzhi Liu | Duo Zhang | Anyang Peng | Weinan E | Linfeng Zhang | Han Wang
Source: Open MIND (preprint, 2026-03-09)
Field: Materials Science — Materials Chemistry

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we systematically develop Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for MLIPs and analyze the effects of routing strategies and expert designs. We show that sparse activation combined with shared experts yields substantial performance gains, and that nonlinear MoE formulations outperform MoLE when shared experts are present, underscoring the importance of nonlinear expert specialization. Furthermore, element-wise routing consistently surpasses configuration-level routing, while global MoE routing often leads to numerical instability. The resulting element-wise MoE model achieves state-of-the-art accuracy across the OMol25, OMat24, and OC20M benchmarks. Analysis of routing patterns reveals chemically interpretable expert specialization aligned with periodic-table trends, indicating that the model effectively captures element-specific chemical characteristics for precise interatomic modeling. Scaling ; Routing (electronic design automation) ; Nonlinear system ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: S. Elakkiya | Kumaravel Kaliaperumal | P. Renukadevi | Gopu Venugopal | Manjunathan Alagarsamy
Source: Sensing and Imaging (article, 2026-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene-Enhanced V-Shaped Metasurface Resonators for High-Sensitivity Refractive Index Sensing: Electromagnetic Modeling and Machine-Learning Validation Refractive index ; Figure of merit ; Resonator ; Sensitivity (control systems) ; Transmittance ; Parametric statistics ; Terahertz radiation ; Metamaterial ; Plasmon Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications