jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. Shen | T. Wang | X. Yao | O. Wu | C. Xie | C. Qian | H. Y. Chen
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network Narrowband perfect absorbers are interesting for spectrum sensing, molecular detection, and infrared imaging. However, their design remains constrained by intuitive, iterative methods that lack flexibility, while also facing challenges in multi-objective optimization. Here, we introduce a deep learning-enabled inverse-design framework that overcomes these limitations through a conditional Wasserstein Generative Adversarial Network (WGAN). The main contribution of this work is a dual-channel image encoding scheme that jointly represents the geometry and thickness of a Si$_3$N$_4$ meta-layer, facilitating the network to learn the distribution of viable structures for a target optical response. This approach naturally solves the inherent ``one-to-many'' design issue, giving a diverse portfolio of functional candidates from a single input spectrum. The designed absorbers achieve exceptional spectral fidelity, with resonance peak errors below 5 nm, a mean squared error (MSE) on the order of $10^{-3}$, and the capacity to produce over 10 distinct, high-performance designs per target. Furthermore, we demonstrate the model's robustness under oblique illumination, showing that it can be efficiently fine-tuned to maintain spectral accuracy across incidence angles from $10^\circ$ to $40^\circ$ by transfer learning, thus extending its practical utility to non-normal operating conditions. Full-wave simulations confirm that the generated geometries support a hybrid plasmonic-dielectric resonance, leading to near-perfect absorption and strong near-field enhancement. Our study provides a robust, physics-aware design paradigm that moves beyond conventional parametric optimization. The introduced framework establishes a versatile platform for the on-demand inverse design of advanced photonic devices for sensing, spectroscopy, and optical signal processing. Robustness (evolution) ; Narrowband ; Parametric statistics ; Inverse ; Photonics ; Optical filter ; Inverse problem ; Encoding (memory) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Acoustic Wave Phenomena Research
Authors: Yuanji Xiao | Jianyu Wang | Jie You | Fang Zhang | Xueming He
Source: Food Research International (article, 2026-03-11)
Field: Chemistry — Analytical Chemistry

Rapid quality detection in colored potatoes during alternating cold and ambient storage: Utilizing absorption and scattering properties Colored ; Absorption (acoustics) ; Quality (philosophy) ; Scattering ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Listeria monocytogenes in Food Safety
Authors: Chang-Ti Chou | Menghang Wang | Chao Yang | Peter A. van Aken | Nicola H. Perry | Boris Kozinsky | Christopher M. Wolverton
Source: ArXiv.org (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential Predicting atomic-scale interfacial structures remains a central challenge in materials science due to their structural complexity and the difficulty of direct comparison between computational and experimental results. In this study, we present an efficient approach for interface structure prediction that integrates the Minima Hopping Method (MHM) with the state-of-the-art machine learning interatomic potential (MLIP), Allegro. We demonstrate that the MHM-Allegro approach provides a robust and computationally efficient route for predicting interfacial structures in the benchmark system SrTiO3 Sigma 3 (112)[110] tilt grain boundaries (GBs), consistently identifying the lowest-energy configurations across different stoichiometries. Furthermore, we introduce a strategy for constructing defect-representative training datasets without explicitly including defective configurations, achieving excellent extrapolative performance in interface predictions. The predictive capability is further validated through direct comparison with experimental observations of the SrTiO3 Sigma 5 (310)[001] GB, where the predicted atomic configurations show strong agreement with experimental measurements. This work represents a significant step toward bridging the gap between ab initio predictions and experimentally observed interfacial structures. Maxima and minima ; Ab initio ; Interatomic potential ; Bridging (networking) ; Sigma ; Interface (matter) ; Work (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: MOHAMMED HDID , HAKIM JEBARI , HASSAN SAMADI
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

A COMPUTATIONALLY-STRUCTURED FRAMEWORK FOR GENERALIZABLE AND EXPLAINABLE AI IN INJECTION MOLDING: INTEGRATING DATA STANDARDIZATION, TRANSFER LEARNING, AND EDGE DEPLOYMENT The emergence of Artificial Intelligence (AI) and Machine Learning (ML) is now the key element of modern manufacturing, but the majority of the models applied in injection molding are domain-specific and cannot be generalized to other machines, materials, or process conditions. The given paper deals with this computing problem by developing a structured literature review devoted to the issue of the generalization and transferability of industrial AI systems. Based on this discussion, a conceptual framework of 5 steps is put forward to formalize the way in which cross-domain robustness may be attained based on fundamental computing systems like data normalization, feature relevance control, regularization, domain adaptation, and explainable modeling. The stages are all based on the latest developments in AI, the introduction of digital-twin, and the integration of hybrid edge-cloud architecture. The framework suggested opens up a hypothetical basis of constructing domain resistant and intelligible artificial pipelines, that links machine particular learning with scalable and transferable intelligence. This effort to coordinate the data of industrial processes with the principles of reproducibility and generalization in computing leads to the further development of both AI methodology and practical use of smart manufacturing in the direction of the reliable and cross-domain automatization. Robustness (evolution) ; Relevance (law) ; Transferability ; Generalization ; Domain (mathematical analysis) ; Scalability ; Process (computing) ; Key (lock) Machine Learning in Materials Science ; Digital Transformation in Industry ; Manufacturing Process and Optimization
Authors: Khalid El-Awady
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Data-Driven Plasticity Modeling via Acoustic Profiling This paper presents a data-driven framework for modeling plastic deformation in crystalline metals through acoustic emission (AE) analysis. Building on experimental data from compressive loading of nickel micropillars, the study introduces a wavelet-based method using Morlet transforms to detect AE events across distinct frequency bands, enabling identification of both large and previously overlooked small-scale events. The detected events are validated against stress-drop dynamics, demonstrating strong physical consistency and revealing a relationship between AE energy release and strain evolution, including the onset of increased strain rate following major events. Leveraging labeled datasets of events and non-events, the work applies machine learning techniques, showing that engineered time and frequency domain features significantly outperform raw signal classifiers, and identifies key discriminative features such as RMS amplitude, zero crossing rate, and spectral centroid. Finally, clustering analysis uncovers four distinct AE event archetypes corresponding to different deformation mechanisms, highlighting the potential for transitioning from retrospective analysis to predictive modeling of material behavior using acoustic signals. Acoustic emission ; Discriminative model ; Cluster analysis ; Plasticity ; Pattern recognition (psychology) ; Profiling (computer programming) ; Event (particle physics) ; Frequency domain ; Consistency (knowledge bases) Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; High-Velocity Impact and Material Behavior
Authors: Hejuan Du | Xinyue Cui | Xingfeng Chen | Dawa Drolma | Shihao Xie | Jiaguo Li | Limin Zhao | Jun Liu | Tingting Shi
Source: Processes (article, 2026-01-24)
Field: Chemistry — Analytical Chemistry

Selection of Specimen Orientations for Hyperspectral Identification of Wild and Cultivated Ophiocordyceps sinensis Ophiocordyceps sinensis is a precious medicinal material with significant pharmacological and economic value. However, the visual similarity between its wild and cultivated forms poses a challenge for authentication. This study investigates the influence of specimen orientation on the accuracy of hyperspectral identification. Hyperspectral data were systematically acquired from four standard specimen orientations (left lateral, right lateral, dorsal, and ventral) for each sample. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Fully Connected Neural Network (FCNN) models were trained and evaluated using both single-orientation and multi-orientation fused data. Results indicate that the LR model achieved superior and stable performance, with an average identification accuracy exceeding 98%. Crucially, for all tested models, no statistically significant difference in identification accuracy was observed across the different specimen orientations. This finding demonstrates that specimen orientation does not significantly influence identification accuracy. The conclusion was further corroborated in experiments using randomly orientation-fused datasets, in which model performance remained consistent and reliable. It is therefore concluded that precise specimen orientation control is unnecessary for the hyperspectral identification of Ophiocordyceps sinensis. This insight substantially simplifies the hardware design of dedicated identification devices by eliminating the need for complex orientation-fixing mechanisms and facilitating the standardization of operational protocols. The study provides a practical theoretical foundation for developing cost-effective, user-friendly, and widely applicable identification instruments for Ophiocordyceps sinensis and offers a reference for similar non-destructive testing applications involving anisotropic medicinal materials. Hyperspectral imaging ; Identification (biology) ; Orientation (vector space) ; Pattern recognition (psychology) ; Selection (genetic algorithm) ; Support vector machine Spectroscopy and Chemometric Analyses ; Fungal Biology and Applications ; Remote-Sensing Image Classification
Authors: MOHAMMED HDID , HAKIM JEBARI , HASSAN SAMADI
Source: Open MIND (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

A COMPUTATIONALLY-STRUCTURED FRAMEWORK FOR GENERALIZABLE AND EXPLAINABLE AI IN INJECTION MOLDING: INTEGRATING DATA STANDARDIZATION, TRANSFER LEARNING, AND EDGE DEPLOYMENT The emergence of Artificial Intelligence (AI) and Machine Learning (ML) is now the key element of modern manufacturing, but the majority of the models applied in injection molding are domain-specific and cannot be generalized to other machines, materials, or process conditions. The given paper deals with this computing problem by developing a structured literature review devoted to the issue of the generalization and transferability of industrial AI systems. Based on this discussion, a conceptual framework of 5 steps is put forward to formalize the way in which cross-domain robustness may be attained based on fundamental computing systems like data normalization, feature relevance control, regularization, domain adaptation, and explainable modeling. The stages are all based on the latest developments in AI, the introduction of digital-twin, and the integration of hybrid edge-cloud architecture. The framework suggested opens up a hypothetical basis of constructing domain resistant and intelligible artificial pipelines, that links machine particular learning with scalable and transferable intelligence. This effort to coordinate the data of industrial processes with the principles of reproducibility and generalization in computing leads to the further development of both AI methodology and practical use of smart manufacturing in the direction of the reliable and cross-domain automatization. Robustness (evolution) ; Relevance (law) ; Transferability ; Generalization ; Domain (mathematical analysis) ; Scalability ; Process (computing) ; Key (lock) Machine Learning in Materials Science ; Digital Transformation in Industry ; Manufacturing Process and Optimization
Authors: Chang-Ti Chou | Menghang Wang | Chao Yang | Peter A. van Aken | Nicola H. Perry | Boris Kozinsky | Christopher M. Wolverton
Source: arXiv (Cornell University) (preprint, 2026-01-24)
Field: Materials Science — Materials Chemistry

Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential Predicting atomic-scale interfacial structures remains a central challenge in materials science due to their structural complexity and the difficulty of direct comparison between computational and experimental results. In this study, we present an efficient approach for interface structure prediction that integrates the Minima Hopping Method (MHM) with the state-of-the-art machine learning interatomic potential (MLIP), Allegro. We demonstrate that the MHM-Allegro approach provides a robust and computationally efficient route for predicting interfacial structures in the benchmark system SrTiO3 Sigma 3 (112)[110] tilt grain boundaries (GBs), consistently identifying the lowest-energy configurations across different stoichiometries. Furthermore, we introduce a strategy for constructing defect-representative training datasets without explicitly including defective configurations, achieving excellent extrapolative performance in interface predictions. The predictive capability is further validated through direct comparison with experimental observations of the SrTiO3 Sigma 5 (310)[001] GB, where the predicted atomic configurations show strong agreement with experimental measurements. This work represents a significant step toward bridging the gap between ab initio predictions and experimentally observed interfacial structures. Maxima and minima ; Ab initio ; Interatomic potential ; Bridging (networking) ; Sigma ; Interface (matter) ; Work (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: R. Gholami | A. Nourmohammadi | E. Ahmadi | H. Rabbani
Source: SHILAP Revista de lepidopterología (article, 2026-03-01)
Field: Materials Science — Biomaterials

Engineering Properties of Tomato Affected by Ultrasonic and Packaging During Storage In this study, ultrasonic radiation (US), packaging film, controlled atmosphere packaging, and controlled storage temperature were utilized for tomaoto packaging. Prior to packaging, the samples underwent ultrasonic treatment and were subsequently packed using polyethylene film (PE) and polyethylene film equipped with 2% nanoclay particles (Nano film) under both normal atmospheric conditions and modified atmosphere (MA) (5% O2 + 3% CO2). These tomatoes were stored at 25°C and 4°C for 28 days. Weekly assessments of storage properties included an examination of physical aspects such as moisture and color indices, chemical factors like pH, total soluble solids (TSS), lycopene, and total phenolic content, as well as mechanical properties encompassing penetration force and elastic modulus. The results indicated that the storage had a detrimental effect on the trends of property changes. Utilizing a modified atmosphere, appropriate storage temperatures, and applying ultrasonic treatment and Nanofilm were found to regulate specific properties effectively. Statistical analysis revealed a significant impact of the applied treatments on most properties at both the 1% and 5% significance levels. On the other hand, an Artificial Neural Network (ANN) was employed for data prediction, and the results showed that the best structure in predicting the physical, mechanical, and chemical properties was 5-10-11. O2 and CO2 were predicted with high accuracy with R2 = 0.93 and R2 = 0.86, respectively, which has shown the accurate performance of the ANN in predicting the data with the selected structure. Ultrasonic sensor ; Polyethylene ; Moisture ; Physical property ; Penetration (warfare) ; Modified atmosphere ; Atmosphere (unit) ; Statistical analysis Nanocomposite Films for Food Packaging ; Microbial Inactivation Methods ; Postharvest Quality and Shelf Life Management
Authors: Swapnil Pradhan, Gauri Bhamare, Dr. Manish Nungade, Kalyani Patil, Neha Patil, Suvidha Kabadi
Source: Open MIND (article, 2026-02-28)
Field: Materials Science — Biomaterials

Smart Packaging: Innovative Packaging in Pharmaceuticals Smart packaging represents an advanced packaging approach developed to address challenges in the pharmaceutical, food, and cosmetic industries, including product safety, quality assurance, patient compliance, and supply chain traceability. This review outlines the evolution of smart packaging, its classifications, and emerging technological advancements. Smart packaging is broadly divided into two main categories: active packaging and intelligent packaging. Active packaging enhances shelf life and maintains freshness by regulating internal conditions—such as controlling oxygen and carbon dioxide levels, managing moisture, and inhibiting microbial growth. Intelligent packaging incorporates technologies including sensors, colorimetric indicators, barcodes, and RFID tags to monitor freshness, storage parameters, and product safety. Additionally, sustainable innovations such as biodegradable materials, edible packaging, and 3D-printed solutions are being investigated to reduce environmental impact. Despite its advantages, smart packaging faces limitations including high implementation costs, insufficient regulatory harmonization, and electronic waste concerns. Future advancements are expected to integrate Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) to develop safer, more sustainable, and consumer-oriented packaging systems. Active packaging ; Supply chain ; Product (mathematics) ; Internet of Things ; Quality (philosophy) ; Packaging engineering Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends ; Material Properties and Processing
Authors: José  Pablo Osorio-Murillo | Adriana Martínez-Oviedo | Mishael Sánchez-Pérez | Cesaré Ovando-Vázquez
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Spectroscopy

Brain Regions-Specific Peptide and Proteomic Signatures Inform Alzheimer’s Disease Biomarker Panels Biomarker discovery ; Proteostasis ; Biomarker ; Proteomics ; Disease ; Interactome ; Microarray analysis techniques ; Proteome Advanced Proteomics Techniques and Applications ; Alzheimer's disease research and treatments ; Machine Learning in Bioinformatics
Authors: S.Jothi Arunachalam | R. Saravanan | T. Sathish | G. Vinuja
Source: Next Materials (article, 2026-02-20)
Field: Materials Science — Polymers and Plastics

Effect of water absorption on the behaviour of jute/kenaf fiber at various lengths by ANN and RSM modelling The study investigates the impact of water absorption on local materials and natural resources-based composites of jute/epoxy (J/epoxy) and kenaf/epoxy (K/epoxy) using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). The research focused on short fiber laminates with fiber lengths of 4 mm, 8 mm, and 12 mm to evaluate water uptake behavior. It was observed that kenaf-based composites reached saturation after 25 days of immersion. The kinetic characteristics of moisture absorption were analyzed and compared with predicted outcomes from the developed models. Among the samples, kenaf composites reinforced with 4 mm fibers showed reduced shrinkage compared to those with 8 mm and 12 mm fibers. Water absorption rates were recorded at 0.88 % for JL5 and 2.71 % for KL5, which were significantly lower than those of JL10, JL15, KL10, and KL15, which exhibited absorption rates of 1.13 %, 3.75 %, 1.74 %, and 4.66 %, respectively. Optimal performance, with a desirability value of 1.000, was achieved after 482 h of immersion using fibers of 11.73 mm length. ANN models demonstrated superior predictive performance compared to RSM, with RSM showing higher prediction errors. ANN's high accuracy in modeling water absorption behavior offers a valuable tool for reducing the need for extensive laboratory testing. The findings support the use of jute and kenaf fiber composites as eco-friendly materials and provide engineers with a cost-effective method for estimating swelling behavior without relying solely on physical experimentation. Kenaf ; Absorption of water ; Response surface methodology ; Shrinkage ; Natural fiber ; Fiber ; Moisture ; Absorption (acoustics) Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Wood Treatment and Properties
Authors: Yuwen You | Yuxin Huo | Chunmei Guo | Zhonglu He | yuan zhao | Bin Yang | Jianxing Chen | Chong Meng | Xi Chen
Source: Energy and Buildings (article, 2026-01-05)
Field: Chemistry — Spectroscopy

Prediction of the thermodynamic performance evolution of a novel aerogel composite insulation material under service conditions based on data-driven methods • The research object is novel ACIM for building internal insulation. • Experimental platform for thermal property monitoring of ACIM was constructed. • ACIM’s thermal conductivity ranges from 2.1% to 34.2% under service conditions. • Proposed data-driven ACIM performance prediction model has average R 2 of 0.96. The thermodynamic properties of building insulation materials are influenced by the temperature and humidity of the service environment. Selecting appropriate design parameters of building insulation materials based on actual meteorological changes can effectively reduce energy waste caused by errors in building heating and cooling load calculations. This study proposes a high-precision, high-generalizability prediction model for a novel aerogel composite insulation material(ACIM). Experiments were conducted under different temperature and humidity conditions that reflect the service environment, aiming to investigate the changes in the thermodynamic properties and to construct a comprehensive dataset of ACIM. A data-driven prediction model with two inputs (temperature and humidity) and three outputs (temperature difference, heat flux density, and thermal conductivity) was constructed to determine the relationship between the ACIM’s thermodynamic properties and the environmental temperature and humidity. Finally, the proposed model was validated using experimental data and compared with the performance of interpolation and linear regression methods. The results indicate that the thermal conductivity of ACIM increases under higher environmental temperature and humidity conditions. The proposed prediction model performs well and the deep learning model performs excellently on all three target variables. The proposed method demonstrates good generalization and scalability, and provides insight in the relationship between the thermodynamic properties of ACIM with the service environment conditions. Service life ; Thermal conductivity ; Humidity ; Thermal insulation ; Heat flux ; Material properties ; Thermal ; Predictive modelling ; Composite number Aerogels and thermal insulation ; Hygrothermal properties of building materials ; Facilities and Workplace Management