jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriella Onila Nascimento Soares | Vitor Santi | Andrey Coatrini Soares | Diego Sousa | Sarah Oliveira Lamas de Souza | Osvaldo Novais de Oliveira
Source: Nanotechnology (article, 2026-01-15)
Field: Materials Science — Biomaterials

Machine learning-enhanced nanofiber systems: a new frontier in controlled drug release The use of machine learning (ML) is reshaping the design and optimization of nanofiber-based drug delivery systems (N-DDS). Electrospun nanofibers offer high surface area, tunable porosity, and versatile drug encapsulation strategies, making them attractive for controlled release applications in multiple therapeutic areas. However, the optimization of materials, fabrication conditions, encapsulation strategies, and release mechanisms is challenging due to the multitude of interdependent parameters. This review outlines how ML has been applied to accelerate N-DDS development, replacing traditional trial-and-error approaches with predictive and adaptive models. We first present a bibliometric landscape of the literature on nanofibers and drug delivery systems (DDS), highlighting the role of electrospinning. We then discuss recent applications of ML in polymer selection, electrospinning optimization, encapsulation strategies, and drug release kinetics. Special attention is given to case studies where ML models achieved high predictive accuracy in tailoring nanofiber morphology, encapsulation efficiency, and release profiles. We also elaborate upon the key challenges for clinical translation, including data quality, scalability, sustainability, and ethical concerns. By integrating ML and other artificial intelligence (AI) methods with nanofiber engineering, N-DDS can progress toward patient-specific, sustainable, and industrially scalable therapeutic platforms, opening new frontiers in precision medicine. Nanofiber ; Encapsulation (networking) ; Drug delivery ; Electrospinning ; Controlled release ; Polymer ; Fabrication Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Machine Learning in Materials Science
Authors: Alexander E. Dashuta | Ryan J. Miller | Pierre Baldi | Thomas Sander | David L. Van Vranken
Source: Scientific Data (article, 2026-01-10)
Field: Chemistry — Inorganic Chemistry

A Dataset of Plausible Proton Transfer Steps for Arrow-Pushing Mechanisms Proton transfers are fundamental steps in polar reaction mechanisms. We generated a large dataset of over 51 million kinetically plausible proton transfer steps between heteroatoms from about 8,000 acids and conjugate bases with experimental aqueous pK<sub>a</sub>s, spanning pK<sub>a</sub> values from -15 to +37. Rate factors were estimated at 25 °C using a simplified Eigen equation with pK<sub>a</sub>s but without statistical factors. Steps with estimated rate constants ≥ 10<sup>3</sup> M<sup>-1</sup> s<sup>-1</sup> were included in the final dataset. Additionally, 5,043 proton transfer steps from carbon acids to heteroatom bases were estimated using the Eigen-Bernasconi equation based on reported intrinsic rate constants and Brønsted β values. Carbon proton transfers with rate constants ≥ 10<sup>3</sup> M<sup>-1</sup> s<sup>-1</sup> were added to the final dataset. Each entry was encoded in SMIRKS format with electron-flow specification for machine learning compatibility. Diversity of structure was prioritized over diversity of conditions; calculated rate constants are expected to be accurate in aqueous environments. This approach and dataset should prove valuable for training models to predict stepwise mechanistic pathways. Proton ; Heteroatom ; Reaction rate constant ; Aqueous solution ; Transfer (computing) ; Constant (computer programming) ; Carbon fibers Metal-Catalyzed Oxygenation Mechanisms ; Photochemistry and Electron Transfer Studies ; Radical Photochemical Reactions
Authors: J. Wang | J. Zuo | Liyun Ma | Y. Wang | J. Chen | Y. Li | H. Zhang
Source: Journal of Applied Spectroscopy (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid Quantitative Analysis of Silver Nanowire Synthesis Via UV-Vis Spectral Deconvolution Polyvinylpyrrolidone ; Yield (engineering) ; Surface plasmon resonance ; Deconvolution ; Nanowire ; Aspect ratio (aeronautics) ; Adsorption ; Analytical Chemistry (journal) Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies ; Nanoparticles: synthesis and applications
Authors: Xinxin Fang | Shuo Xu | Xiangyu Yang | Jingyun Cao | Zhong Yang
Source: Industrial Crops and Products (article, 2026-01-18)
Field: Chemistry — Organic Chemistry

High-throughput discrimination of ten poplar (Populus spp.) wood cultivars using near-infrared spectroscopy coupled with convolutional neural networks Poplar ( Populus spp.) is an important industrial crop, providing sources of fuelwood, raw material for the pulp and paper industry, and wood-based panels. Advances in genetic breeding have led to a diverse array of poplar cultivars, which exhibit significant differences in their suitability for various industrial and ecological applications. To date, wood identification has typically been limited to the taxonomic level of "family" or "genera", with limited literature on precise discrimination of poplar cultivars. This limitation is primarily due to low genetic and morphological variability among poplar species, which poses challenges for accurate classification. This study presents a high-throughput and non-destructive technique for discriminating poplar cultivars by integrating convolutional neural networks (CNNs) with near-infrared (NIR) spectroscopy. Different cultivars of poplar wood were used to gather NIR spectroscopy data in the 350–2500 nm region, and several preprocessing techniques were evaluated methodically. Based on the preprocessed spectra, three convolutional neural network models, NIR-Net, NIR-ResNet, and NIR-Inception, were constructed and trained for variety classification, and the performance of the models was evaluated on an independent validation set. The results showed that the combination of Savitzky-Golay derivatives (SGD) and multivariate scatter correction (MSC) significantly improves the classification accuracy of the NIR-ResNet model: achieving an accuracy of 97.52 % for distinguishing six conventional hybrids cultivars and 98.18 % for discriminating five closely related 'Lulin' hybrids cultivars. This method not only significantly improves classification accuracy of poplar cultivars but also lays a foundation for the industrial and ecological application of specific poplar cultivars. • Accurate discrimination of ten poplar ( Populus spp.) wood cultivars by near infrared spectroscopy. • Convolutional neural networks were employed to significantly enhance the prediction accuracy of poplar cultivars. • Develop high-throughput identification techniques for poplar cultivars suitable for industrial applications. Cultivar ; Convolutional neural network ; Hybrid ; Preprocessor ; Pattern recognition (psychology) ; Artificial neural network ; Multivariate statistics Wood and Agarwood Research ; Wood Treatment and Properties ; Spectroscopy and Chemometric Analyses
Authors: Lucas B. T. de Kam | Jiaxin Zhu | Ankit Mathanker | Katharina Doblhoff-Dier | Nitish Govindarajan
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The emergence of efficient, short-range machine learning interatomic potentials (MLIPs) offers a promising alternative to computationally expensive density functional theory-based molecular dynamics (DFT-MD) simulations in this regard. However, in standard periodic DFT calculations of metal surfaces, the surface charge is implicitly set by the number of counterions in the simulation cell, making it a global property that is difficult to represent with strictly local MLIPs. Here, we benchmark common MLIP architectures (DP, ACE, MACE) for charged Au/water interfaces containing solvated sodium ions. We find that MLIPs trained on datasets spanning multiple surface charge states yield inconsistent predictions of interfacial water orientation and ion distributions, although message-passing models with a larger receptive field exhibit greater robustness to training on mixed-charge datasets. In contrast, models trained on a single charge state produce consistent equilibrium interfacial properties. Finally, we assess the performance of the eSEN model trained on the recently released Open Catalyst 2025 dataset, which includes solid/liquid interfaces that span a wide range of surface charge densities. Overall, our results characterize the limitations of short-range MLIPs for simulations of electrochemical interfaces and provide practical guidance for constructing training datasets for simulations of charged metal/electrolyte interfaces. Molecular dynamics ; Robustness (evolution) ; Surface charge ; Charge density ; Charge (physics) ; Electrostatics ; Benchmark (surveying) ; Benchmarking ; Scaling ; Range (aeronautics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Borhen Louhichi | Djuansjah Joy | Santosh Kumar Sahu | Nadir Ayrılmış | It Ee Lee | Eng Eng Ngu | Harishbabu Sundarasetty
Source: Industrial Crops and Products (article, 2026-02-20)
Field: Materials Science — Polymers and Plastics

Methods to optimize tribological properties of pineapple leaf fiber epoxy composites This study explores the enhancement of tribological properties in epoxy composites reinforced with pineapple leaf fibre (PALF) ( Ananas comosus ) using optimization and machine learning techniques. The rationale behind this research is to develop sustainable, high-performance materials for industrial applications by utilizing bio-based fibers, which offer environmental benefits. Response Surface Methodology (RSM) with Central Composite Design (CCD) was used, involving 24 runs. Key factors included fiber weight percentage (0 wt% pure epoxy and 30 wt% PALF/epoxy), applied load (10 N and 30 N), sliding velocity (0.7 mm/s and 2 mm/s), and sliding distance (500 mm and 1500 mm). X-ray diffraction (XRD) analysis showed that pure epoxy exhibited an amorphous structure, while the 30 wt% PALF/epoxy composite displayed a peak shift to 23.9°, indicating increased crystallinity. Analysis of Variance (ANOVA) revealed that increasing fiber content improved tribological properties. The composite with 30 wt% PALF at optimized condition showed an 94 % reduction in wear rate and a 78 % decrease in the coefficient of friction (COF), with R² values of 0.9135 for wear rate and 0.9203 for COF. Three machine learning models—linear regression, gradient boosting, and Gaussian Process (GP) regression—were employed for predictions. The GP model outperformed the others, achieving R² values above 0.98. SHAP analysis identified fiber weight percentage as the most influential factor on wear rate. This study demonstrates that experimental, statistical, and machine learning methods optimize tribological properties of bio-composites for industrial applications. • Pineapple leaf fiber (PALF) reinforced epoxy composites made by hand lay-up and cured for fabrication. • X-ray Diffraction (XRD) showed a shift in the main peak to 23.9° for 30 wt.% PALF/epoxy sample. • The experiment used Response Surface Methodology (RSM) with Central Composite Design (CCD), considering fiber ratio. • Increasing the fiber content to 30 wt.% led to a 94% reduction in wear rate and a 78% decrease in friction coefficient. • Gaussian Process (GP) regression showed high predictive accuracy with a coefficient of determination (R²) > 0.98. Tribology ; Epoxy ; Composite number ; Response surface methodology ; Fiber ; Glass fiber ; Lubrication Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Aluminum Alloys Composites Properties
Authors: Mourad Kharbach | Mohammed Alaoui Mansouri | Mohammed Taabouz | Tao Zhang | Huiwen Yu
Source: Food Chemistry (article, 2026-01-08)
Field: Chemistry — Analytical Chemistry

Current advances in LC-MS/MS and GC-MS/MS coupled with chemometrics for integrated food analysis and quality control Chemometrics ; Instrumentation (computer programming) ; Quality (philosophy) ; Food safety ; Food supply ; Food quality ; Set (abstract data type) ; Control (management) Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Metabolomics and Mass Spectrometry Studies
Authors: Lanqing Yang | Shilin Li | Baoqing Bai | Tao Bo | Jinhua Zhang | Ligang Yu | Ying Zhang | WenYan YAN | Hailong Qian | Yukun Yang
Source: Chemical Engineering Journal (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

HOFs/QDs synergy in a triple-channel array: Multi-mechanism sensing decoded by YOLOv12 for ultra-rapid on-site identification of nine heavy metal ions Workflow ; Discriminative model ; Multiplexing ; Sensor array ; Automation ; Scalability ; Identification (biology) ; Fluorescence Carbon and Quantum Dots Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection
Authors: M. Sahaya Sheela | A. Ponraj | S. Kumarganesh | B. Thiyaneswaran | P. Rishabavarthani | I. Rajesh | Binay Kumar Pandey | Digvijay Pandey | Mesfin Esayas Lelisho
Source: Discover Nano (article, 2026-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection Plasmonic biosensors, particularly Surface Plasmon Resonance and Surface-Enhanced Raman Spectroscopy, have gained significant attention for real-time, label-free biochemical detection. However, optimizing these sensors for maximum sensitivity and selectivity remains a challenge due to their complex plasmonic interactions with different biomolecules. This work proposes SERA, an AI driven framework that integrates machine learning algorithms with experimental Surface-Enhanced Raman Spectroscopy (SERS) data for the predictive modeling and optimization of plasmonic sensing performance. Using supervised learning techniques, the ML models are trained on a spectral dataset - SERS-DB obtained from various plasmonic nanostructures. The model predicts key parameters such as resonance shift, intensity variations, and molecular binding efficiency, allowing for rapid optimization of biosensor designs without extensive trial-and-error experimentation. This approach accelerates plasmonic biosensor development and enables real-time adaptive sensing based on live data. The results through evaluation on the SERS-DB database with 420 samples for training and 180 for the testing phase, 6 classes like Thiacloprid, Imidacloprid, Thiamethoxam, Nitenpyram, Tetrahydrofolate, and Dihydrofolate, an accuracy of 92%, precision & recall of 90%, and F1-score of 92% were attained. The SERA model excelled with an overall score of around 0.90 in all 6 classes, proving additional superiority in biosensing applications. Further comparative analysis of the proposed approach with conventional methods underscores the best performance in accuracy with 92%, sensitivity, 1000 nm/RIU, and 95% in optimization efficiency. Overall, this research highlights a scalable and cost-effective strategy for advancing biosensor technology in medical diagnostics, environmental monitoring, and bio photonics. Biosensor ; Plasmon ; Surface plasmon resonance ; Sensitivity (control systems) ; Surface-enhanced Raman spectroscopy ; Scalability Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Yang Shi | Naijun Zhao | Deqiang Meng
Source: Physics Letters A (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing the transmission performance of magnetoelectric antennas through machine learning algorithms Converse ; Coupling (piping) ; Antenna (radio) ; Transmission (telecommunications) ; Nonlinear system ; Feature (linguistics) ; Current (fluid) Multiferroics and related materials ; Antenna Design and Analysis ; Magnetic Properties and Synthesis of Ferrites
Authors: Taesoo Lee | Minwoo Song | Jisung Baek | Yu-Na Jung | Gaeun Choi
Source: Chemical Physics Reviews (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Advancing quantum-dot optoelectronics: Simulation and machine learning for performance optimization Colloidal quantum-dots (QDs) are garnering significant attention as a promising material for next-generation optoelectronic devices due to their tunable emission wavelengths, high photoluminescence quantum yield, and solution-process compatibility and scalability. While QD-based technologies have seen widespread adoption in display applications, expanding their use into broader optoelectronic fields requires a precise understanding of their optical and electrical properties. Accurate modeling and prediction of device behaviors are critical for performance optimization, necessitating a comprehensive approach that integrates advanced computational techniques. This review explores state-of-the-art simulation methods and machine learning (ML)-based predictive models for QD devices. Key processes, including charge transport, exciton dynamics, and light outcoupling, are introduced to provide insights into efficiency and stability improvements. Modified electrical simulations are discussed alongside advanced optical simulations to assess the role of material properties and device architectures in determining performance. Additionally, the integration of ML algorithms has emerged as a powerful tool for accelerating device design, leveraging large datasets to efficiently predict and optimize QD structures, material compositions, and processing conditions. By combining computational simulations with ML-driven approaches, this review aims to establish a comprehensive framework for QD optoelectronic device research. The synergy between theoretical modeling and data-driven optimization is expected to enhance the development of high-performance QD-based technologies, paving the way for applications beyond conventional display systems. Key (lock) ; Stability (learning theory) ; Compatibility (geochemistry) ; Quantum dot ; Simulation modeling Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Semiconductor Quantum Structures and Devices
Authors: Peilin Kang | Jintu Zhang | Enrico Trizio | Tingjun Hou | Michele Parrinello
Source: Journal of Chemical Theory and Computation (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Committors without Descriptors The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods toward its solution have been proposed. Recently, it has been suggested that the use of the committor, which provides a precise formal description of rare events, could be of use in this context. We have recently followed up on this suggestion and proposed a committor-based method that promotes frequent transitions between the metastable states of the system and allows extensive sampling of the process transition state ensemble. One of the strengths of our approach is being self-consistent and semiautomatic, exploiting a variational criterion to iteratively optimize a neural-network-based parametrization of the committor, which uses a set of physical descriptors as input. Here, we further automate this procedure by combining our previous method with the expressive power of graph neural networks, which can directly process atomic coordinates rather than descriptors. Besides applications on benchmark systems, we highlight the advantages of a graph-based approach in describing the role of solvent molecules in systems, such as ion pair dissociation or ligand binding. Dissociation (chemistry) ; Parametrization (atmospheric modeling) ; Benchmark (surveying) ; Metastability ; Graph ; Rare events Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Ramsey Issa | Said Hamad | Ricardo Grau-Crespo | Emad Awad | Taylor D. Sparks
Source: Computational Materials Science (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Sample-efficient active learning for materials informatics using integrated posterior variance Developing accurate machine learning models with minimal data remains a central challenge in materials informatics. Efficient models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate the integrated posterior variance acquisition function within an active learning framework, comparing its performance against three established methods: random sampling, point-wise uncertainty sampling, and query-by-committee. We evaluate these methods across three diverse datasets: AutoAM, Thermoelectric, and NMR. Our results demonstrate that integrated posterior variance consistently outperforms conventional methods in selecting candidates that minimize prediction error with fewer labeled samples. We identify two key limitations: computational overhead that increases with dataset size and diminished effectiveness in high-dimensional feature spaces where distance metrics become less meaningful. Despite these constraints, our approach demonstrates how strategic experimental selection can substantially improve model performance across varying materials informatics domains while minimizing the number of required experiments, offering significant resource savings for materials discovery workflows. • Active learning - Compares Integrated Posterior Variance (IPV) against random sampling, point-wise uncertainty sampling, and query-by-committee. • Multi-dataset evaluation - Benchmarked on three diverse datasets; AutoAM, Thermoelectric, and NMR. • Performance gain - IPV consistently selects candidates that reduce prediction error with fewer labeled samples than other selection strategies. • Limitations - (1) Computational overhead grows with dataset size; (2) Effectiveness declines in high-dimensional spaces where distance metrics degrade. • Practical impact - Enables strategic experiment selection that cuts required experiments for materials discovery workflows. Variance (accounting) ; Overhead (engineering) ; Selection (genetic algorithm) ; Active learning (machine learning) ; Feature selection ; Posterior probability ; Key (lock) Machine Learning in Materials Science ; Machine Learning and Algorithms ; Gaussian Processes and Bayesian Inference