jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael Bezick | B. B. Wilson | Vaishnavi Iyer | Yuheng Chen | Vladimir M. Shalaev | Sabre Kais | Alexander V. Kildishev | Brad Lackey | Alexandra Boltasseva
Source: Advanced Optical Materials (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

PearSAN: A Machine Learning Method for Inverse Design Using Pearson Correlated Surrogate Annealing ABSTRACT Inverse design in nanophotonics is often bottlenecked by the curse of dimensionality, particularly for metasurfaces with many degrees of freedom, where the optimization landscape is highly non‐convex. In this work, we introduce Pearson Correlated Surrogate Annealing (PearSAN): a machine learning‐assisted framework that efficiently navigates these complex spaces. PearSAN uses a surrogate model designed to capture relative performance trends within the latent space of a generative model. This allows the algorithm to rapidly converge toward optimal solutions without requiring exhaustive physical simulations. As a showcase example, PearSAN is applied to the design of thermophotovoltaic (TPV) metasurfaces by matching the working bands of a thermal radiator and a photovoltaic (PV) cell. PearSAN achieves a state‐of‐the‐art design efficiency of , outperforming conventional baselines in both convergence speed and final device performance. These results establish PearSAN as a robust tool for photonics applications requiring precise spectral engineering. Thermophotovoltaic ; Simulated annealing ; Curse of dimensionality ; Surrogate model ; Inverse ; Convergence (economics) ; Generative Design ; Inverse problem ; Nanophotonics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Neural Networks and Reservoir Computing
Authors: Sergio L. Gómez
Source: Tekhne. (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Análisis Comparativo de Metodologías para la Predicción de Propiedades Mecánicas de Materiales Compuestos La predicción precisa de las propiedades mecánicas de los materiales compuestos representa un desafío técnico significativo, debido a su compleja estructura interna y a la amplia variedad de combinaciones posibles entre matrices y refuerzos. Este trabajo tiene como objetivo analizar y comparar las principales metodologías empleadas para este propósito, agrupándolas según su enfoque. El estudio se basó en una revisión sistemática de la literatura, mediante la cual se identificaron y clasificaron las estrategias utilizadas en cuatro categorías: experimentales, analíticas, numéricas (particularmente el método de elementos finitos) y basadas en inteligencia artificial. Posteriormente, se realizó un análisis comparativo de sus beneficios y limitaciones, considerando criterios como precisión, costo, flexibilidad, requerimientos técnicos y capacidad predictiva. Los resultados muestran que, si bien los ensayos experimentales ofrecen alta fidelidad, presentan limitaciones en términos de costos y replicabilidad. Los modelos analíticos se destacan por su simplicidad y rapidez, aunque tienden a perder exactitud en sistemas más complejos. Las simulaciones numéricas permiten representar estructuras heterogéneas con gran detalle, pero demandan recursos computacionales significativos. Finalmente, los enfoques basados en inteligencia artificial demuestran un alto potencial para gestionar grandes volúmenes de datos y reconocer patrones complejos, aunque su eficacia depende en gran medida de la calidad y representatividad de los datos disponibles. Internet of Things ; Risk prevention Machine Learning in Materials Science ; Material Selection and Properties ; Computational Physics and Python Applications
Authors: Xiaoyan Shen | Hai Bin Zhong | Ruiqing Han
Source: Magnetochemistry (article, 2026-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Physics-Informed Neural Network with Hybrid Architecture for Magnetic Core Loss Prediction Under Complex Conditions Magnetic core loss is an important indicator for describing the performance of magnetic elements. The traditional physical model has an insufficient performance for predicting the magnetic core loss of magnetic elements under complex conditions such as high temperature, non-sinusoidal waveform, and high frequency. To address this issue, this study proposes a physics-informed neural network (PINN)-based model for magnetic core loss prediction. In particular, this PINN-based model is constructed with a hybrid network architecture as a baseline algorithm, which combines a convolutional long short-term memory network (Conv-LSTM), power spectral density (PSD), and an ensemble learning method (including extreme gradient boosting (XGB), gradient boosting regression (GBR), and random forest (RF)). This design aims to address the complexity of magnetic core loss prediction. Moreover, the Steinmetz equation (SE) is improved to enhance the adaptability under complex conditions, and this improved Steinmetz equation (ISE) is integrated as physical constraints embedded in the neural network for magnetic core loss prediction. Based on the traditional data-driven loss term, the physical residual term is introduced as a regularization constraint to enable the prediction to satisfy both the observed data distribution and physical law. The experimental results show that the PINN-based model has a good prediction performance of magnetic core loss under complex conditions. Artificial neural network ; Boosting (machine learning) ; Gradient boosting ; Convolutional neural network ; Core (optical fiber) ; Regularization (linguistics) ; Magnetic core ; Residual Magnetic Properties and Applications ; Magnetic properties of thin films ; Electromagnetic Simulation and Numerical Methods
Authors: Jiwon Yun | Hyeim Yu | Youngho Yun | Wonil Nam
Source: Micromachines (article, 2026-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Label-Free Malignancy Phenotyping of Living Cancer Cells by High-Performance Surface-Enhanced Raman Spectroscopy Substrates Surface-enhanced Raman spectroscopy (SERS) amplifies Raman scattering by placing molecules in the near-field of plasmonic nanostructures, enabling label-free molecular fingerprinting. While attractive for living cell phenotyping, many cellular SERS works rely on internalized colloidal nanoparticles, leading to variable uptake/localization, aggregation-driven hotspot fluctuations, and potential cellular perturbation. Here, we report a chip-like Au/SiO2 nanolaminate SERS substrate that supports direct culture and label-free measurements of living cells on spatially defined hotspots without nanoparticle uptake. The periodic nanolaminate forms dense nanogaps and is engineered for 785 nm excitation, providing uniform enhancement over a large, culture-compatible area with high hotspot uniformity. By engineering the cell–substrate nano–bio interface, the platform enables reproducible acquisition of intrinsic cellular vibrational fingerprints under physiological conditions without Raman tags. Using MCF-7 and MDA-MB-231 breast cancer cells, we collected hundreds of spectra per line, and MDA-MB-231 exhibited broader spectral variations, indicating greater heterogeneity. Principal component analysis and linear discriminant analysis achieved 99% classification accuracy for MCF-7 and MDA-MB-231, and bright-field imaging confirmed preserved adhesion and canonical morphologies. This chip-based, label-free living cell SERS platform enables scalable, nonperturbative phenotyping and may support rapid malignancy classification and treatment response screening across subtle cancer states. Raman spectroscopy ; Malignancy ; Raman scattering ; Cancer cell ; Plasmon ; Nanoparticle ; Surface-enhanced Raman spectroscopy ; Cancer metastasis ; Cell adhesion Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Microfluidic and Bio-sensing Technologies
Authors: Yong-Xuan Hong | Jia-Wei Tang | Jie Chen | Yun-Yun Xie | Zhang-Wen Ma | Qing-Hua Liu | Liang Wang
Source: Current Research in Food Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Rapid classification and quality assessment of citrus essential oils via machine-learning-assisted Raman spectroscopy Citrus essential oils (EOs) require accurate identification and assessment to ensure authenticity and consistency. However, conventional techniques such as gas chromatography (GC) and mass spectrometry (MS) are time-consuming and expensive, highlighting the need for novel analytical methods. This study proposes an approach for EOs detection using Raman spectroscopy (RS) combined with machine learning (ML) algorithms. Six citrus EOs underwent an evaporation experiment, with Raman spectra collected at five time points and GC-MS was used to analyze compositional changes at the starting and ending points of evaporation as a standard reference. Five ML algorithms were developed to identify differences among EOs and monitor their temporal changes. The predictive performance was evaluated using multiple quantitative metrics. The results show that the support vector machine (SVM) consistently achieves the best performance across all prediction tasks, and the interpretability algorithm identified key components in the Raman spectra of EOs. Taken together, RS-SVM is proven to be an accurate and cost-effective analytical technique for the rapid identification and quality assessment of citrus EOs. Interpretability ; Raman spectroscopy ; Quality assessment ; Support vector machine ; Analytical Chemistry (journal) ; Identification (biology) ; Evaporation Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity
Authors: Anna Mantelli | A. Pola | Marcello Gelfi | Cristian Viscardi | Flavio Ricchini | Massimo Svanera | Francesco Bergamaschi
Source: International Journal of Metalcasting (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Modelling and Simulation of Carbon Macrosegregation in a 36-Tonne Steel Ingot Abstract A new user function was developed to simulate carbon macrosegregation in medium to large-sized steel ingots, derived for use in the post-processing stage of casting simulations. This new proposed formulation accounts for critical parameters such as cooling rate, thermal gradient, and ingot geometry, and can be implemented within commercial simulation software to estimate steel carbon content. The methodology was first applied to an industrial-scale 36-tonne ingot, comparing simulation and experimental analyses. Characterisation of the industrial ingot, performed by sectioning and chemical analysis, showed good agreement between the carbon segregative index values predicted by the user function and those experimentally measured. To further assess the reliability of the model, a second case study was conducted on a 40-tonne ingot using the same approach. The results confirmed the predictive capability of the developed function, which can be effectively employed to evaluate carbon macrosegregation in medium to large-sized ingots, particularly for medium- to low-alloy steel grades. Graphical Abstract Ingot ; Carbon fibers ; Carbon steel ; Casting ; Reliability (semiconductor) ; Structural material Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Aluminum Alloy Microstructure Properties
Authors: Jing Hu | D.Q. Peng | Yue Liu | Shaogui Wu
Source: ChemNanoMat (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bifunctional Nitrogen‐Doped Yellow‐Green Carbon Dots: Integrated Fluorescent Probe for Sensitive Antibiotic Detection and Oxygen “Breathing Color” Tracking Nitrofurantoin (Nit), a widely used antimicrobial agent classified as a Group 3 carcinogen, demands rapid, sensitive detection and practical monitoring for environmental and food safety. This study's unique contribution lies in developing bifunctional nitrogen‐doped yellow‐green carbon dots (YG‐CDs) integrating sensitive Nit detection and oxygen “breathing color” tracking. YG‐CDs were synthesized via a one‐step solvothermal method using bromothymol blue and 2‐mercaptobenzimidazole, with structural/optical characterization via Fourier transform infrared, X‐ray photoelectron spectroscopy, UV–vis, and fluorescence spectroscopy; YG‐CDs‐gelatin composites were fabricated for visualization. Key findings: YG‐CDs emit at 555 nm (long‐wavelength) with a quantum yield of 26.1%, excellent stability (salt, temperature, UV), and high selectivity/anti‐interference for Nit, achieving a detection limit of 2.1 μM and 96.5–100.5% recovery in lake water. The YG‐CDs‐gelatin composite exhibits oxygen‐responsive color shifts (yellow‐green to green), enabling instrument‐free food freshness monitoring. This work provides a novel, integrated strategy for on‐site antibiotic residue detection and intelligent packaging, advancing practical applications in environmental monitoring and food safety. Bifunctional ; Bromothymol blue ; Fluorescence ; Detection limit ; Carbon fibers ; Fourier transform infrared spectroscopy ; Quantum yield ; Residue (chemistry) Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Simone Adorinni | Houyang Xu | Jonathan R. Nitschke | Silvia Marchesan
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Biomaterials

Peptide cages: bioinspired supramolecular architectures for next-generation applications This review examines the design and synthesis of peptide-based supramolecular cages, highlighting the versatility and functional diversity achievable by incorporating small peptides as structural components. Inspired by natural supramolecular architectures, synthetic peptide cages offer unique advantages, including tunable chirality, structural predictability, biocompatibility, and ease of functionalisation. The discussion focuses on two principal strategies. The first involves cages in which peptides constitute the primary structural framework, with cage geometry dictated either by intrinsic backbone conformations or by externally imposed directional interactions such as metal coordination. The second covers hybrid systems in which peptides play a functional rather than framework-determining role and are integrated with rigid aromatic or synthetic scaffolds that define the overall architecture. These approaches enable precise control over cage geometry, cavity characteristics, and dynamic behaviour, facilitating applications in biosensing, targeted drug delivery, molecular separation, and environmental remediation. By bridging principles from natural assembly and synthetic supramolecular chemistry, peptide cages represent a powerful platform for developing next-generation functional materials. Supramolecular chemistry ; Peptide ; Supramolecular assembly ; Bridging (networking) ; Molecular recognition ; Structural motif Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Chemical Synthesis and Analysis
Authors: Karen Gonzales-Flores | Ramón Castañeda-Priego | Francisco Alarcón
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Confinement and shear effects on the rotational diffusion of a minimal virus-inspired colloidal particle The rotational diffusion of a rigid spherical body decorated with dimers in an explicit fluid environment is reported. This model acts as a simplified representation of an enveloped virus bearing peplomers immersed in a coarse-grained fluid. Using dissipative particle dynamics, we explicitly study the hydrodynamic effects on the rotational diffusion of this virus-inspired particle subjected to oscillatory shear flow and confined between two solid-like surfaces. Since the rotational response depends on the type of imposed flow, we first characterize the oscillatory shear, identifying distinct flow regimes in terms of the so-called Péclet number, $Pe$. Our findings indicate that, under confinement, the rotational diffusivity is strongly modulated by the oscillatory flow amplitude and only weakly affected by the number of peplomers, since their effect is mainly determined by their dimeric structure and associated effective size. For high $Pe$, the rotational diffusion coefficient, $D_{r}$, tends to decrease as the number of peplomers ($N_{s}$) increases, whereas at low $Pe$, rotational diffusion becomes weakly dependent on the number of peplomers. However, at intermediate values of $Pe$, the interplay between oscillatory forcing and thermal fluctuations prevents the emergence of a clear trend between $D_{r}$ and $N_{s}$. Our results provide a clear picture of how, in confined environments, the interplay between fluid flow and thermal fluctuations affects the rotational diffusion of spiked particles, thereby helping to explain how fluid conditions can modify the alignment of peplomers with their potential targets. Rotational diffusion ; Thermal diffusivity ; Shear flow ; Dissipative system ; Shear (geology) ; Rotational temperature ; Thermal ; Diffusion ; Thermal fluctuations Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Micro and Nano Robotics
Authors: Nanxuan Wu | Chao Qian | Hongsheng Chen
Source: Chinese Science Bulletin (Chinese Version) (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Frontiers of Intelligent Metamaterials and Metamaterials Intelligence 超材料与人工智能的相互作用正形成两个双向耦合的新兴方向：智能超材料与超材料智能。智能超材料是指通过智能算法实现按需设计和自适应电磁调控，推动其在隐身、通信与成像等场景中应用；超材料智能，又称类脑超材料，是以电磁波作为计算载体，通过利用可调谐结构、非线性效应与多维度复用在电磁空间直接计算，构造神经网络和数学算符的同素异形体，实现光速推理、分析与信息处理。这两个方向共同推动超材料从单一功能器件向具备感知、决策与执行能力的超材料智能体演进。本文以“物理机制—算法协同—系统应用”架构为主线，系统梳理两大方向的核心原理、方法体系与前沿进展，重点分析两者在智能实现方式与系统目标上的差异，进一步探讨在物理—数据一致性、器件可重构性与系统集成等方面的挑战，并展望物理驱动的少样本与在线学习方法，推动超材料走向可部署的智能工程化系统。 Metamaterial ; Camouflage ; Key (lock) ; Intelligent decision support system Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Neural Networks and Reservoir Computing
Authors: Rehan Mujtaba | Sumaiya Karim | Md Suhaib Ahmed
Source: International Journal of Research in Medical Sciences & Technology (article, 2026-01-01)
Field: Materials Science — Biomaterials

Translational Nanomedicine: Bridging the Gap Between Smart Delivery Systems and Quality Control Despite the fact that research into nanomedicine has made it possible to discover fascinating new avenues for the distribution of medications, there is still a significant distance to go before the limited number of nanomedicine products that have been granted regulatory clearance can compete with the innovative smart delivery systems that have been developed in laboratories. In spite of the dramatic growth in both academic publications and technological skill, the proportion of nanomedicine research that results in therapies that are commercially viable is less than one percent. A number of issues, including those pertaining to regulation, quality control, safety, and the scalability of manufacturing, have contributed to this distinction. In addition, it is the outcome of inconsistent clinical performance, which is particularly troublesome when dealing with biological phenomena such as the Enhanced Permeability and Retention (EPR) effect, which may vary greatly from one instance to the next. There were early successes that demonstrated the viability of the notion, such as lipid nanoparticle platforms and liposomes, but these platforms also demonstrated that there were limitations in terms of repeatability and predictability. Therefore, it is necessary to employ improved formulation procedures in order to overcome biological obstacles. Lipid-based, polymer-based, hybrid/inorganic systems, and carriers originating from biological sources are some of the techniques that fall under this category. In order for these ideas to become a reality, there must be stringent control over key quality attributes (CQAs), a comprehensive characterization of bio-nano interactions, scalable procedures that are in accordance with Good Manufacturing Practice (GMP), and unified regulatory channels. The DELIVER framework provides structured instructions for synchronizing the steps of early-stage design, experimental validation, production preparation, and regulatory strategy. This is done with the intention of reducing the likelihood of translational risk occurring. It is essential to place a high priority on quality-by-design (QBD), immunological risk assessment, standardized analytical methodologies, and early regulatory participation in order to increase clinical success rates. Last but not least, in order to bring nanomedicine products that are safe, effective, and reproducible from the laboratory to the bedside of the patient, it is necessary to bridge the gap between the development of smart nanocarriers and the incorporation of quality control Nanomedicine ; Bridging (networking) ; Scalability ; Quality (philosophy) ; Control (management) ; Quality control ; Quality by Design Nanoparticle-Based Drug Delivery ; Biosimilars and Bioanalytical Methods ; Cancer Research and Treatment
Authors: Renan Falcioni | Nicole Ghinzelli Vedana | Caio Almeida de Oliveira | João Vitor Ferreira Gonçalves | Marcelo Luiz Chicati | JOSÉ ALEXANDRE M. DEMATTÊ | Marcos Rafael Nanni
Source: Plants (article, 2026-03-18)
Field: Chemistry — Analytical Chemistry

VIS–NIR–SWIR Hyperspectral Imaging and Advanced Machine and Deep Learning Algorithms for a Controlled Benchmark of Bean Seed Identification and Classification Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible-near-infrared-shortwave infrared (VIS-NIR-SWIR) hyperspectral imaging (HSI; 449.54-2399.17 nm; 563 bands) was used to classify 32 grain-legume accessions (<i>n</i> = 3200 seeds; 100 seeds per accession), comprising 30 common bean (<i>Phaseolus vulgaris</i> L.) landraces plus two outgroup legumes (<i>Vigna angularis</i> (Willd.) Ohwi & Ohashi and <i>Cajanus cajan</i> (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (<i>n</i> = 2240) reserved for fully independent training and 30 seeds per accession (<i>n</i> = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R<sup>2</sup> of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54-456.30 nm and 577.02-597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; Support vector machine ; Outlier ; Pixel ; Deep learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture