jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guindo Mahamed L, Sokhar Samb, Mor Gueye
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

THE POWER OF IA AND SPECTROSCOPY FOR FOOD Ensuring the quality, safety, and authenticity of food is a critical issue that remains among the paramount globalpriorities because of the challenges of food fraud, contamination, and complex supply chains. In recent years, theconvergence of Artificial Intelligence and spectroscopic technologies has emerged as a transformative solution forfast, accurate, and non-destructive food analysis. Spectroscopy techniques, such as infrared, near-infrared, Raman,and fluorescence spectroscopy, give information on the molecular composition of food, while AI will improve theirinterpretative strength with more advanced data processing, pattern recognition, and predictive modeling. This paperdiscusses how AI-driven chemometrics and machine learning algorithms interface with spectroscopic data to realizereal-time detection of adulteration, prediction of nutritional value, and freshness and safety monitoring. Case studiesfrom various food industries showed significant improvements in the accuracy, speed, and automation of detection.Despite promising advances, challenges related to data standardization, model generalization, and equipment costremain persistent. The full paper concludes that synergistic use of AI and spectroscopy will be one of the key driverstoward establishing a transparent, smart, and sustainable global food system. Transformative learning ; Chemometrics ; Automation ; Food supply ; Fluorescence spectroscopy ; Spectroscopy ; Power (physics) ; Key (lock) Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Identification and Quantification in Food
Authors: Nutcha Taneepanichskul | Helen Hailes | Mark Miodownik
Source:  (preprint, 2025-02-18)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging and machine learning to identify food contaminated compostable and recyclable plastics With the increasing public legislation aimed at reducing plastic pollution, compostable plastics have emerged as an alternative to conventional plastics for some food packaging and food service items. However, the true value of compostable plastics can only be realized if they do not enter the environment as contaminants but instead are processed along with food and garden waste using industrial composting facilities. Distinguishing compostable plastics from conventional plastics in this waste stream is an outstanding problem. Currently, Near Infrared (NIR) technology is widely used to identify polymers, but it falls short in distinguishing plastics contaminated with food waste. This study investigates the application of hyperspectral imaging (HSI) to address this challenge, enhancing the detection and sorting of contaminated compostable plastics. By combining HSI with various machine learning algorithms we show it is possible to accurately identify and classify plastic packaging with food waste contamination, achieving up to 99% accuracy. The study also measures the impact of plastic features such as darkness, size, and level of contamination on model performance, with darkness having the most significant impact. Implementing HSI in waste management systems can significantly increase composting and recycling rates. This advanced sorting approach supports the circular economy by ensuring that both compostable and recyclable plastics are effectively processed and recycled, minimizing environmental impact Hyperspectral imaging ; Contamination ; Environmental science ; Waste management ; Computer science ; Artificial intelligence ; Engineering ; Biology Spectroscopy and Chemometric Analyses
Authors: Hongshuo Pan | Xinyun Chen | Ruyue Han | Xiaoxi He | Jiayi Xu | Y. ZHANG | Shuhao He | Ma Xu | Mengke Wang | Xiangyu Xu | Hao Wang
Source: Sensors and Actuators B Chemical (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Machine learning-assisted regulator-mediated single nanozyme sensor array rapid detection of proteins and nutritional supplements Nanoclusters ; Analyte ; Sensor array ; Identification (biology) ; Nanomaterials ; Food safety ; Metal ; Set (abstract data type) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Qinyang Li | Rongzhi Dong | Nicholas Miklaucic | Jeffrey Hu | Sadman Sadeed Omee | Lai Wei | Sourin Dey | Ming Hu | Jianjun Hu
Source: ArXiv.org (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

In context learning Foundation models for Materials Property Prediction with Small datasets Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context learning foundation model (ICL-FM) framework for materials property prediction that integrates both composition-based and structure-aware representations. The proposed approach couples the pretrained TabPFN transformer with graph neural network (GNN)-derived embeddings and our novel MagpieEX descriptors. MagpieEX augments traditional features with cation-anion interaction data to explicitly measure bond ionicity and charge-transfer asymmetry, capturing interatomic bonding characteristics that influence vibrational and thermal transport properties. Comprehensive experiments on the MatBench benchmark suite and a standalone lattice thermal conductivity (LTC) dataset demonstrate that ICL-FM achieves competitive or superior performance to state-of-the-art (SOTA) models with significantly reduced training costs. Remarkably, the training-free ICL-FM outperformed sophisticated SOTA GNN models in five out of six representative composition-based tasks, including a significant 9.93\% improvement in phonon frequency prediction. On the LTC dataset, the FM effectively models complex phenomena such as phonon-phonon scattering and atomic mass contrast. t-SNE analysis reveals that the FM acts as a physics-aware feature refiner, transforming raw, disjoint feature clusters into continuous manifolds with gradual property transitions. This restructured latent space enhances interpolative prediction accuracy while aligning learned representations with underlying physical laws. This study establishes ICL-FM as a generalizable, data-efficient paradigm for materials informatics. Suite ; Feature learning ; Property (philosophy) ; Artificial neural network ; Feature (linguistics) ; Benchmark (surveying) ; Disjoint sets ; Context (archaeology) ; Deep learning Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Duraisami Dhamodharan | Mamdouh A. Al‐Harthi
Source: Diamond and Related Materials (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

PbO-graphene nanohybrids: A versatile tool for enhancing RLR1110 composites supported by ANN modeling Graphene ; Composite material ; Materials science ; Nanotechnology ; Composite number Thermal properties of materials ; Graphene research and applications ; Composite Material Mechanics
Authors: Adithya Madduri gmail.com | Randall J. Ellis | Chirag J. Patel
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-12-01)
Field: Chemistry — Spectroscopy

Protein Compositional Ratio Representation (PCRR) Systematically Improves Human Disease Prediction Abstract Plasma proteomics captures a functional snapshot of human physiology; yet, most machine learning models treat protein abundances as independent variables, ignoring the fact that biological systems and proteomic measurements are inherently compositional. Many molecular processes depend not on absolute concentrations but on relative balances: receptor–ligand stoichiometry, enzyme–substrate ratios, and homeostatic feedbacks that govern signaling and metabolism. We propose that these relationships are best captured through pairwise protein ratios, which more faithfully reflect underlying biochemical constraints than raw expression values. We evaluate a machine learning framework that models pairwise log-ratios of proteins ( log ( A ) − log ( B )) as features, thereby encoding compositional structure directly into the learning space. Applied to the ROSMAP plasma proteomics cohort (n = 871), this approach substantially improved the classification of Alzheimer’s subtypes (NCI, MCI, AD, AD+) with an average AUROC gain of +0.1274 over a strong baseline that incorporated raw proteomics and demographics. The top-ranked ratios (e.g., SEMA3C:TMEM70, IDUA:NPTXR) captured converging pathogenic pillars of Alzheimer’s disease, including microglial activation, proteostasis dysregulation, and lipid-clearance imbalance, highlighting that ratio-based features recover biologically coherent axes of disease. To assess generality, we scaled the method to the UK Biobank proteomic dataset (n &gt; 53,000; 587 phenotypes). The ratio-based model outperformed raw-level models in 95.1 % of diseases, with statistically significant (FDR &lt; 0.05) gains in 56.7 %. Together, these results suggest that proteomic data should be viewed and modeled as compositional systems, where relative protein abundances carry the accurate functional signal. This insight supports the broader utility of ratio-based representations for disease prediction and biomarker discovery. Proteomics ; Proteostasis ; Pairwise comparison ; Representation (politics) ; Proteome ; Quantitative proteomics ; Human proteome project ; Snapshot (computer storage) Advanced Proteomics Techniques and Applications ; Alzheimer's disease research and treatments ; Bioinformatics and Genomic Networks
Authors: Zezhu Zeng (11612446) | Xia Liang (179535) | Zheyong Fan (1287324) | Yue Chen (73753) | Michele Simoncelli (21626027) | Bingqing Cheng (2810599)
Source: Figshare (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

ThermalTransport of Amorphous Hafnia Across the GlassTransition Heat transport in glasses over a wide temperature range is critical for applications in gate dielectrics and thermal insulators but remains poorly understood due to the challenges in modeling vibrational anharmonicity and configurational dynamics across the glass transition. Recent predictions show an unusual decrease in thermal conductivity (κ) with temperature in amorphous hafnia (a-HfO<sub>2</sub>), contrasting with the typical trend in glasses. Using molecular dynamics with a machine-learning-based neuroevolution potential, we compute κ of a-HfO<sub>2</sub> from 50 K to 2000 K. At low temperatures, the Wigner transport equation captures both anharmonicity and quantum statistics. Above 1200 K, atomic diffusion invalidates the quasiparticle picture, and we resort to the Green–Kubo method to capture convective transport. We further extend the Wigner transport equation to supercooled a-HfO<sub>2</sub>, revealing the crucial role of low-frequency modes in facilitating heat transport. The computed κ, based on both Green–Kubo and Wigner transport theories, increases continuously with temperature up to 2000 K. Anharmonicity ; Thermal conductivity ; Amorphous solid ; Glass transition ; Supercooling ; Thermal diffusivity ; Molecular dynamics ; Hafnia Thermal properties of materials ; Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies
Authors: Seifert, Lukas
Source: RWTH Publications (RWTH Aachen) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Modellierungsansatz zur Vorhersage der mechanischen und rheologischen Eigenschaften von Polypropylen-Compounds In industrial practice, the methodology used to develop new plastic compounds is commonly characterised by an empirical and iterative approach. Existing model-based approaches such as mixture models or machine learning methods are either only applicable to simple formulations with few different formulation components or require a large database. The aim of this work is to develop an alternative modelling approach that is also suitable for complex formulations and requires comparatively little experimental data. Using an exemplary PP formulation for a packaging application, a novel analytical modelling approach for predicting the modulus of elasticity, shear viscosity, MFR and notched impact strength is investigated. For this modelling approach, the individual effects of the blending of polymers and the addition of the other formulation components are considered in isolation from each other, modelled and then combined to form an overall model. Interactions between the individual formulation components are not taken into account. In addition to validating the developed models using formulations produced on a laboratory plant, the transferability to an industrial plant is analysed. This illustrates that the models achieve similar prediction accuracies even when scaled up. The models also proved to be suitable for more complex formulations where all components are used simultaneously and which differ from the original training data set. It is only when predicting notched impact strength that the unconsidered interactions between the additives used lead to a deterioration in prediction accuracy. Finally, the developed model is used for targeted formulation development based on an industrial case study. In combination with genetic algorithms, new PP formulations can be predicted that meet the desired mechanical and rheological properties. Experimental validation of the identified formulations confirmed the practical applicability of the approach. Future work should investigate the extension of the methodology to take into account interactions between formulation components and the applicability to other polymer types and compound properties. Transferability ; Work (physics) ; Experimental data ; Robustness (evolution) ; Empirical modelling ; Design of experiments Polymer crystallization and properties ; Engineering and Materials Science Studies ; Rheology and Fluid Dynamics Studies
Authors: Jong Hyun Jung | Tom Schächtel | Yongliang Ou | Selina Itzigehl | Marc Högler | Niels Hansen | Johanna R. Bruckner | Blazej Grabowski
Source: ArXiv.org (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

End-to-end machine-learned interatomic potentials for modeling functionalized mesoporous aluminosilicates The structural hierarchy and chemical flexibility of metallosilicates enable broad technological applications, yet they also make it challenging to uncover structure--property relations. Previous large-scale atomistic simulations have provided mechanistic insight, but their accuracy and achievable model complexity remain constrained by the available interatomic potentials. Here, we present an end-to-end workflow for developing accurate and efficient machine-learning potentials, specifically moment tensor potentials (MTPs), tailored for structurally and chemically complex systems such as metallosilicates. The workflow integrates de novo structure generation, surface functionalization, and property evaluation. A domain-specific training strategy is employed: Configurations associated with melt--quench generation and subsequent functionalization train the syn-MTP, whereas configurations near equilibrium train the eq-MTP. We apply the workflow to prototypical metallosilicates, i.e., aluminosilicates, which we also experimentally synthesize and characterize for benchmarking the simulations. The syn-MTP reliably generates amorphous aluminosilicates that match experimental density and pair distribution functions measured with synchrotron X-ray diffraction. The eq-MTP reproduces experimental infrared spectra and surface hydroxyl densities, along with density-functional-theory-derived dehydrogenation energies, demonstrating meta-GGA-level accuracy and validating the end-to-end workflow. Finally, we showcase the applicability of the developed potentials by predicting infrared spectra of functionalized porous aluminosilicates. This study establishes a robust path toward accurate modeling of realistic metallosilicates under operando-relevant conditions. Workflow ; Molecular dynamics ; Surface (topology) ; Mesoporous material ; Amorphous solid ; Granularity ; Interatomic potential ; Path (computing) ; Flexibility (engineering) Machine Learning in Materials Science ; Mesoporous Materials and Catalysis ; Block Copolymer Self-Assembly
Authors: Ulderico Di Caprio | M. Enis Leblebici
Source: Chemical Engineering Science (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

SINDybrid: automatic generation of hybrid models for dynamic systems Robustness (evolution) ; Python (programming language) ; Uncertainty quantification ; Hybrid system ; Sampling (signal processing) ; Dynamic programming ; Noise (video) ; Training set Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Control Systems Optimization
Authors: Tong Zhai (19480723) | Wen-Tao Gu (21385190) | Miao Yu (199182) | Yu-Di Shen (21385193) | Jing-Min Liu (1979428) | Shuo Wang (143908)
Source: Figshare (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Machine Learning-AssistedMulticolor FluorescenceAssay for Visual Data Acquisition and Intelligent Inspection of MultipleFood Hazards Regardless of Matrix Interference Regarding the significant health risks of pesticide residue in foods, while current sensors still suffer from limited efficiency and stability, as well as difficulties in qualitative identification and quantitative detection of mixtures, development of innovative detection techniques combined with advanced methodology holds great research value. Herein, a highly efficient intelligent food risk evaluation system was proposed by integrating a multicolor fluorescent responsive assay with machine learning (ML) algorithms for the identification and quantification of multiple pesticides, carbendazim (CBZ), heptachlor (HEP), chlordimeform (CDF), and their mixtures. This method leveraged the color changes generated from the interaction between multicolor carbon dots (CDs) and target pesticide molecules. By extracting color signal feature values from these reactions and integrating the visual data acquisition with ML models, this method enables efficient qualitative identification and quantitative detection of multiple pesticides, regardless of matrix interference through a dual-source data acquisition strategy without large instruments. The developed evaluation system via a ″stepwise prediction″ strategy automatically demonstrated robust qualitative identification capability with a discrimination accuracy of 99.3% for pesticide categorization while achieving robust quantitative prediction accuracy (<i>R</i><sup>2</sup> ≥ 0.8946) for pesticide concentration detection, verified in six kinds of food matrix. This method significantly improves the detection stability and efficiency, providing a promising tool for food safety monitoring. Pesticide residue ; Identification (biology) ; Data acquisition ; Pattern recognition (psychology) ; Categorization ; Interference (communication) ; Food safety ; Visual inspection Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Electrochemical sensors and biosensors
Authors: Peiqi Yu (21298966) | Shikun Duan (18609415) | Qixiang Jia (17819743) | Yuanyu Zhu (14309841) | Xueyu Guan (21298969) | Ruonan Ji (3370310) | Hailing Wang (1388346) | Zhongshi Huang (5816258) | Lin Li (28817) | Xiaolong Zhu (266562) | Xuechu Shen (17505432) | Shaowei Wang (490684)
Source: Figshare (article, 2025-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-ControlledMetasurface Design for Full-StokesPolarization Detector Driven by SPPN Full-Stokes real-time detection, an important polarization detection method, can capture the polarization, spectrum, and intensity information on vector light field simultaneously and improve the ability to accurately identify complex targets significantly. However, in existing systems, the components required for obtaining full-Stokes polarization and circular polarization are often difficult to integrate, relying on multiple parameter adjustments, resulting in time-consuming and heavily dependent on computational resources. In this article, we combine a phase-controlled metasurface with a two-dimensional material detector to design a multidimensional detector with wavelength and full-Stokes polarization selection characteristics. We proposed a spectral phase prediction network based on a convolutional neural network-gated recurrent unit, which effectively addressed the time-consuming and labor-intensive problem of searching for appropriate phase-corresponding structures in phase-regulated metasurface design. This innovation can reduce the design time by a factor of 10<sup>6</sup>. The experimental results show that the circular dichroism of the design achieves 0.8 at the design wavelength, with transmission over 87%. According to the characteristics, the spectra can be improved by combining the designed device with the Fabry–Pérot cavity. Finally, we combined van der Waals materials with the metasurface to achieve a highly integrated full-Stokes polarization spectroscopic photodetector and circular polarization imaging. This study provides a reliable technological approach for the development of next-generation spatial sensing technology and holds promise for future high-dimensional sensing devices to achieve multidimensional detection and recognition of targets. Polarization (electrochemistry) ; Detector ; Photodetector ; Circular polarization ; Polarimetry ; Convolutional neural network Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Advanced Wireless Communication Technologies
Authors: Zi-Ran Guo | Sen Xue | Lu He | Ziyi Xie | Tian-Hao Yang | Qiang Fu
Source: Chinese Journal of Polymer Science (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites Curse of dimensionality ; Inverse ; Modular design ; Component (thermodynamics) ; Representation (politics) ; Feature (linguistics) ; Composite number ; Process (computing) ; Thermal conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Composite Material Mechanics