jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Roberta Palmieri | Silvia Serranti
Source: Recycling (article, 2026-03-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Coupled with Machine Learning for Accurate Color Classification of Glass Fragments in Recycling Processes Glass is a highly recyclable material that provides substantial environmental benefits, including savings in raw materials and energy as well as a reduction in CO2 emissions. To ensure the production of high-quality secondary raw materials, container glass from municipal waste separate collection must be accurately separated by color in recycling plants, where only minimal color mixing is tolerated. Color sorting is therefore a key step in glass recycling, as it directly affects both the quality and the market value of recycled cullet. Given the increasingly stringent color quality requirements for recycled glass and the high fraction of cullet used in container glass, advanced technological solutions are needed to improve sorting accuracy. In this study, a visible–near-infrared (VIS-NIR: 400–1000 nm) hyperspectral imaging (HSI) approach integrated with machine learning (ML) is proposed for the automated classification of post-consumer glass fragments from bottles and jars into five color categories: brown, dark green, light green, half-white and white. A hierarchical Partial Least Squares-Discriminant Analysis (PLS-DA) model combined with an object-based analysis strategy was developed to optimize color recognition. The proposed system achieved sensitivity and specificity values between 0.910 and 1.000, demonstrating excellent robustness and predictive capability. Validation on independent datasets confirmed the model’s reliability, with all color glass fragments correctly classified at the object level. The results highlight the potential of HSI-ML systems to enhance color sorting accuracy and process efficiency in recycling plants, contributing to improved material recovery and the advancement of sustainable, circular glass production. Glass recycling ; Sorting ; Hyperspectral imaging ; Robustness (evolution) ; Glass production ; Raw material ; Color analysis ; Glass industry Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Cultural Heritage Materials Analysis
Authors: ОЛЬГА СОЛОВЕЙ
Source: Herald of Khmelnytskyi National University Technical sciences (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

МОДЕЛІ ФІЗИЧНОГО СТАНУ ЕЛЕМЕНТІВ ДЛЯ ПРОГНОЗУВАННЯ НАДІЙНОСТІ БУДІВЕЛЬ ЗА ДОПОМОГОЮ ГРАФОВИХ НЕЙРОННИХ МЕРЕЖ Effective management of the life cycle of building assets requires reliable tools for predicting their technical condition. Modern approaches, in particular Building Information Modeling and the concept of digital twins, create opportunities for the application of graph neural networks. The aim of this work is to develop, through the formalization of existing theoretical provisions, validated models, and experimentally confirmed regularities, models of the physical state of structural elements of a building object as a function of time. To achieve this aim, the following tasks were accomplished: modern approaches to modeling structural degradation were analyzed in order to identify key physical laws and engineering models subject to formalization; models of the physical state were formulated for the main structural elements (columns, beams, slabs, and walls), describing the dynamics of deterioration of their characteristics over time under the influence of operational factors; and a methodology for verifying the proposed models was developed, including sensitivity analysis to key input parameters. The results of the sensitivity analysis confirmed the physical validity of the models. It was established that for load-bearing reinforced concrete elements (columns and beams), the dominant factors are corrosion intensity and degradation of material properties. For the foundation slab, the critical factor was found to be the chemical composition of the soil, while for the wall panel, the durability of the sealant under UV radiation—which triggers a cascading failure—proved to be decisive. A key result is the demonstrated ability of all models to clearly differentiate between two main scenarios: a “satisfactory state,” in which the structure retains a significant safety margin, and a “critical state,” in which this margin is exhausted. The practical significance of the work lies in the fact that the proposed models provide a sound computational basis for populating graph structures with physical attributes, which is a necessary prerequisite for the development and training of graph neural networks aimed at predicting the technical condition of building assets. Durability ; Key (lock) ; Sensitivity (control systems) ; Graph ; Work (physics) ; Component (thermodynamics) ; Function (biology) Structural mechanics and materials ; Environmental and Industrial Safety ; Scientific Research Methodologies and Applications
Authors: Alex Tai | Jason Ogbebor | Rodrigo Cassimiro de Freitas Freitas
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD. Stability (learning theory) ; Interatomic potential ; Molecular dynamics ; Icosahedral symmetry ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Ab initio ; Sample (material) ; Sampling (signal processing) Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: M. Sravani | K Srihari | S Karthik
Source: Applied Physics A (article, 2026-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic manipulation of Mid-IR Light with plasmonic metasurfaces through programmable nanostructures Plasmon ; Nanostructure ; Nanoparticle ; Plasmonic nanoparticles Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Penghua Ying | Ting Liang | Yun Chen | Yan Chen | Shiyun Xiong | Zheyong Fan | Jianbin Xu | Yilun Liu
Source: arXiv (Cornell University) (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport within a single framework remains a formidable challenge. Here, using extensive machine learning molecular dynamics, we demonstrate a fundamental thermal transport crossover in metal-organic frameworks. We reveal that grafting flexible side chains onto a pristine MOF backbone acts as a structural switch, strongly reducing the thermal conductivity by $\sim$70% (from $\sim 0.7$ to $\sim 0.2\ \text{W m}^{-1}\text{K}^{-1}$ at 300 K). Crucially, the functionalized derivatives exhibit a drastic transition from a classical Peierls $\sim 1/T$ decay to an anomalous, temperature-independent glass-like plateau. Reciprocal- and real-space analyses reveal the microscopic origins: the side chains act as built-in local resonators that trap acoustic energy via strong low-frequency resonant hybridization, while simultaneously inducing extreme steric crowding. Consequently, the heat-carrying phonon modes become critically damped, with their mean free paths strictly confined to the nanometer scale and their lifetimes collapsing to the Ioffe-Regel limit. This work establishes a highly programmable molecular engineering strategy to dismantle the phonon gas model, forcing crystalline frameworks into an extreme diffusive transport regime. Phonon ; Thermal conductivity ; Work (physics) ; Thermal ; Molecular dynamics ; Nanometre ; Steric effects ; Resonator Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science
Authors: Miao‐Jiong Tang | Tinghui Zhang | Qian Huang | Shuwen Li | Rui Liu | Hongye Li | Xiaofan Chen | Shunxi Dong | Xiaohua H. Liu | Xiaoming Feng | Xin Hong
Source: Angewandte Chemie International Edition (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Inside Front Cover: Data‐Driven Modeling of <i>N,N’</i> ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions (Angew. Chem. Int. Ed. 12/2026) In the Research Article (e18560), Xiaohua Liu, Xiaoming Feng, Xin Hong, and co-workers report an integrated data platform for N,N'-dioxide/metal-catalyzed asymmetric Michael additions. Featuring over 2,000 curated reactions, it combines statistical analysis with mechanistically informed machine learning. By integrating intermediate-based augmentation and similarity-weighted tuning, the model achieves accurate selectivity prediction, validated by the successful design of a new enantioselective transformation. Front (military) ; Front and back ends ; Statistical analysis ; Statistical model ; Enantioselective synthesis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Jihye Hur | Adam P. Generale | Michael K. Ballard | Vikas Varshney | Craig Przybyla | Surya Kalidindi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Flow-Based Generative Models for Estimating Extreme Value Distributions in Unidirectional Polymer Matrix Composites Microscale chemistry ; Parametric statistics ; Extreme value theory ; Probabilistic logic ; Resilience (materials science) ; Representative elementary volume ; Microstructure ; Statistical model ; Statistical inference Machine Learning in Materials Science ; Composite Material Mechanics ; Polymer crystallization and properties
Authors: Chongyuan Jiao
Source: Journal of Engineering Research and Reports (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

GAN-based Data Augmentation and Intelligent Fitting for Small-sample Regression: A Comprehensive Review The challenge of developing accurate regression models from limited data samples remains one of the most persistent obstacles in contemporary machine learning research and industrial applications. Small-sample regression problems arise across diverse domains, including materials science, chemical process engineering, biomedical research, and manufacturing quality control, where data acquisition is inherently expensive, time-consuming, or physically constrained. This comprehensive review examines the transformative role of Generative Adversarial Networks (GANs) in addressing small-sample regression challenges through synthetic data augmentation and intelligent model fitting strategies. The review systematically analyses the theoretical foundations of GAN architectures, beginning with the seminal work on adversarial training frameworks and progressing through significant variants including Wasserstein GANs, conditional GANs, and their hybrid formulations specifically designed for regression tasks. Particular attention is devoted to virtual sample generation methodologies that leverage GAN-based approaches to expand limited training datasets whilst preserving the underlying data distribution characteristics and feature correlations essential for accurate regression modelling. The review further explores intelligent fitting techniques that integrate GAN-generated samples with conventional machine learning algorithms, examining how these combined approaches enhance prediction accuracy, model generalisation, and robustness in data-scarce scenarios. Critical evaluation of training stability issues, including mode collapse and convergence challenges specific to regression applications, is presented alongside established mitigation strategies. Comparative analysis with alternative generative models, particularly Variational Autoencoders and emerging diffusion models, provides practitioners with guidance for method selection. The review concludes by identifying current limitations, highlighting promising research directions, and offering practical recommendations for implementing GAN-based augmentation strategies in real-world small-sample regression applications. Leverage (statistics) ; Robustness (evolution) ; Data-driven ; Regression ; Generative grammar ; Stability (learning theory) ; Process (computing) ; Regression analysis ; Data quality Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Ashwini Verma | Kavita Joshi
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

The Pathfinder: Adaptive Learning for Hydrogen Storage Material Optimization Progress in solid-state hydrogen storage is critically constrained by the time consuming experimental measurements. While machine learning offers a promising route to accelerate materials screening, its accuracy is fundamentally limited by the scarcity of quality experimental data. To overcome this bottleneck, we introduce an adaptive learning (AL) framework that integrates uncertainty quantification into a closed-loop workflow for targeted material optimization. Unlike static models, this framework actively guides the selection of high-value compositions to maximize information gain and improve model predictability. As a proof of concept, we apply the framework for the prediction of pressure-composition-temperature (PCT) isotherms of Mg-Ni-La compositions using literature-reported data. Starting with a baseline model trained solely on PCT isotherms of Mg-Ni compositions, the model is iteratively refined through ten adaptive learning cycles. The uncertainty-driven composition selection effectively 1 identifies informative data points spanning Mg fractions from 92% to 4% and temperatures from 300 K to 633 K, demonstrating strong exploration capability across the chemical space. For ten unseen Mg-Ni-La compositions evaluated sequentially, the model accuracy converges upto 80% within the first five cycles, with predictions aligning well with experimental observations. This work demonstrates adaptive learning as a robust, data efficient predictive framework for accelerating the screening and optimization of hydrogen storage materials while minimizing experimental efforts. Workflow ; Selection (genetic algorithm) ; Experimental data ; Baseline (sea) ; Quality (philosophy) ; Adaptive learning ; Active learning (machine learning) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: Gourav Kumbhojkar | Zihan Wang | Sinan Keten | Wei Chen
Source: Journal of Mechanical Design (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Multi-Objective Bayesian Optimization for Design Under Unknown Feasibility Constraints Abstract Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions and has found applications across engineering, materials science, and machine learning. However, in many practical tasks, feasibility of design is not readily quantifiable. Conventional BO methods often ignore or poorly model design feasibility, leading to impractical or invalid solutions. This work addresses the challenge of integrating feasibility information directly into the BO process. Here we show that modeling feasibility using Gaussian process (GP) classification and treating it as an objective together with other performance objectives in a multi-objective BO setup significantly improves solution quality across different benchmark design tasks. We develop a latent variable Gaussian process classifier for modeling feasibility over categorical design spaces, and use a Dirichlet-based GP classifier for continuous spaces. Our approach provides quantification of feasibility, offering clear optimization guidance. Comparative studies on analytical and real-world test problems demonstrate enhanced performance in terms of both feasibility and optimality. This approach could be extended to a wide range of applications where feasibility is implicit or difficult to define, such as materials discovery, drug design, and chemical process optimization. By re-framing feasibility as a learnable objective, our work opens new avenues for constrained optimization under uncertainty. Bayesian optimization ; Categorical variable ; Gaussian process ; Benchmark (surveying) ; Classifier (UML) ; Engineering design process ; Design of experiments ; Kriging ; Bayesian probability Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Khalid Abushahla | Halil Arslan | Bashar Al Bayoush
Source: Journal of Nuclear Engineering and Radiation Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Radiation Shielding Properties for Lanthanum-Cerium Oxide Doped 45S5 Bioactive Glass Using Machine Learning Algorithms Abstract This study investigates the incorporation of cerium oxide (CeO2) and lanthanum oxide (La2O3) into 45S5 bio-active glass to enhance its radiation shielding properties, critical for nuclear and biomedical applications. Traditional evaluation methods, including simulations and experimental testing, are often resource-intensive. To address this, a machine learning approach was employed to predict these properties in (CeO2 + La2O3)-doped 45S5 bio-active glass. Several machine learning models, including Random Forest, LightGBM, Support Vector Regressor, CatBoost, and XGBoost, were trained and validated using data from EpiXS and Monte Carlo Geant4 simulations, achieving high predictive accuracy. The machine learning model results, including predictions for mass attenuation coefficients (MAC), which serve as key indicators of radiation shielding effectiveness, were compared to Monte Carlo Geant4 simulations and the EpiXS database, demonstrating strong agreement. Among the evaluated models, CatBoost, Extra Trees, LightGBM, and Random Forest demonstrated solid predictive accuracy, with R2 scores of 0.9999, 0.9998, 0.9996, and 0.9996 (MAE: 0.0226, 0.0239, 0.0460, 0.0364; MSE: 0.0029, 0.0062, 0.0128, 0.0131), respectively. Although slight deviations were observed at very low photon energies (&amp;lt;0.1 MeV), reflecting the increased complexity of photon–matter interactions in this region, the overall agreement remained strong across the investigated energy range. These results highlight the potential of machine learning to improve the optimization of doped bio-active glasses (BGs), significantly reducing the time and cost of conventional methods. This approach enables the rapid development of bio-active glass compositions with enhanced mechanical and radiation shielding properties, advancing biomedical implants and protective materials for radiological applications. Electromagnetic shielding ; Monte Carlo method ; Support vector machine ; Bioactive glass ; Oxide ; Random forest Radiation Shielding Materials Analysis ; Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects
Authors: Romel Mark Irabon | Rustom Hibone | Francis Kirby Burnea | Giovanni Paylaga | Leo Cristobal Ambolode II
Source: Research Square (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Prediction And Investigation of Transition Metal Oxides for Hydrogen Storage Application via Machine Learning-Assisted Density Functional Theory Density functional theory ; Transition metal ; Hydrogen ; Hydrogen storage ; Saturation (graph theory) ; Electronic structure ; Metal Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Xinwei Zhu | Diandong Tang | Lin Shen | Wei-Hai FANG
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Fewest-Switches Surface Hopping with Combined Deep Learning Potential and Long Short-Term Memory Network Propagator for Simulating Realistic Photochemical Processes Fewest-switches surface hopping (FSSH) is the most popular method for simulating photochemical processes of molecular systems. Recently, we have constructed long short-term memory (LSTM) networks as a propagator for electronic subsystems in FSSH dynamics simulations. The collective results on Tully's three models have been reproduced satisfactorily. In the present work, we develop an extended LSTM-FSSH framework to simulate realistic photochemical reactions. The input features of LSTM as well as the training procedure are redesigned to represent high-dimensional nuclear degrees of freedom in an effective way. Equivariant neural networks are integrated with LSTM to build adiabatic potential energy surfaces in ground and excited states. Photoisomerizations of $\mathrm{CH_2NH}$ and azobenzene are simulated, showing that our new proposed LSTM-FSSH method can produce excited-state lifetimes and product yields accurately in comparison with conventional FSSH simulations as reference. Only 10 reference trajectories are required for training LSTM networks, and then a trajectory ensemble can be generated with very efficient LSTM-FSSH dynamics simulations to obtain collective results. Surface hopping ; Propagator ; Trajectory ; Excited state ; Adiabatic process ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Surface (topology) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Photochromic and Fluorescence Chemistry