jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paulmar Pushparaj John | Ramadoss R | John Solomon I | Jayabalan C
Source: Figshare (article, 2026-04-10)
Field: Materials Science — Polymers and Plastics

Effect of chemical treatment and biosilica fillers on the mechanical, thermal, and tribological properties of Ricinus communis stem fiber composites: a machine learning and RSM approach This study explores the enhancement of mechanical, thermal, and tribological properties of Ricinus communis stem fiber (RCSF)-reinforced polyester composites by incorporating biosilica derived from Proso millet husk as a filler. The RCSF fibers were treated with 5 wt% NaOH to improve the fiber-matrix bonding. Biosilica was added at varying concentrations (1–5 vol%) into the polyester matrix. The tensile strength increased from 42.6 MPa to 61.5 MPa with 4% biosilica, and the flexural strength improved from 72.15 MPa to 93.65 MPa at the same filler content. The 4% biosilica sample also showed the highest impact strength at 80.71 kJ/m<sup>2</sup>. Thermal analysis indicated improved stability, with a significant increase in weight retention at higher temperatures, especially for composites with higher biosilica content. In tribological tests, the optimal biosilica content (3–4%) reduced the specific wear rate (SWR) to 1.56 × 10<sup>−5</sup> mm<sup>3</sup> /Nm and the coefficient of friction (COF) to 0.239. However, higher concentrations of biosilica led to agglomeration, reducing composite performance. The study highlights the potential of biosilica-reinforced RCSF composites as a sustainable material, providing a balance of mechanical strength, thermal stability, and wear resistance for engineering applications. Alkaline treatment improves fiber-matrix bonding in RCSF composites.Biosilica enhances the mechanical, thermal, and tribological properties of RCSF composites.Optimal biosilica content (3–4%) improves wear resistance and reduces COF.Thermal stability of RCSF composites improves with biosilica reinforcement.XGBoost model effectively predicts and optimizes sliding wear behavior. Alkaline treatment improves fiber-matrix bonding in RCSF composites. Biosilica enhances the mechanical, thermal, and tribological properties of RCSF composites. Optimal biosilica content (3–4%) improves wear resistance and reduces COF. Thermal stability of RCSF composites improves with biosilica reinforcement. XGBoost model effectively predicts and optimizes sliding wear behavior. Tribology ; Ultimate tensile strength ; Composite number ; Durability ; Wear resistance ; Friction coefficient ; Husk Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Fiber-reinforced polymer composites
Authors: Najam US Sahar Riyaz | Ahmed Ben Ali | Mazen Khaled | Ibnelwaleed A. Hussein | Saeed Al-Meer
Source: Industrial & Engineering Chemistry Research (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Chitosan-Derived Green Corrosion Inhibitor for Carbon Steel in CO <sub>2</sub> -Saturated Media: A Graph Convolutional Network Approach Creating environmentally friendly corrosion inhibitors is vital for sustainable operations in the oil and gas industry. This study presents an integrated green chemistry and deep learning approach to design and test chitosan-grafted polyacrylamide (CsAM) as a biodegradable corrosion inhibitor for carbon steel in CO2-rich environments. A graph convolutional network, trained on a curated data set of over 70 inhibitors, predicted that CsAM could achieve approximately 84% inhibition at 200 ppm, guiding experimental efforts. Four CsAM formulations with different chitosan-to-polyacrylamide ratios were synthesized and characterized by FTIR, SEM, and contact angle analysis to demonstrate beneficial functional and surface-active properties. Electrochemical tests showed that the 1:30 CsAM ratio achieved an impressive 98% inhibition efficiency, acting as a mixed-type inhibitor with physisorption as the main adsorption mechanism. These findings demonstrate that combining AI-based molecular prediction with sustainable polymer synthesis can significantly accelerate the development of effective green inhibitors while lowering reliance on toxic alternatives. The proposed computational–experimental approach offers a scalable pathway to creating high-performance, environmentally friendly corrosion control solutions for industry use. Corrosion inhibitor ; Corrosion ; Physisorption ; Environmentally friendly ; Carbon steel ; Adsorption ; Scalability ; Electrochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Polymer composites and self-healing
Authors: Matej Martinc | Goran Dražić | Anton Kokalj | Katarina Žiberna | Janina Roknić | Matic Poberžnik | Sašo Džeroski | Andreja Benčan Golob
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties - such as polarization directions essential for understanding functional properties of ferroelectrics - remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to automate the detection of polarization directions from 4D-STEM diffraction patterns in ferroelectric potassium sodium niobate. While models trained on synthetic data achieve high accuracy on idealized synthetic diffraction patterns of equivalent thickness, the domain gap between simulation and experiment remains a critical barrier to real-world deployment. In this context, a custom made prototype representation training regime and PCA-based methods, combined with data augmentation and filtering, can better bridge this gap. Error analysis reveals periodic missclassification patterns, indicating that not all diffraction patterns carry enough information for a successful classification. Additionally, our qualitative analysis demonstrates that irregularities in the model's prediction patterns correlate with defects in the crystal structure, suggesting that supervised models could be used for detecting structural defects. These findings guide the development of robust, transferable machine learning tools for electron microscopy analysis. External Data Representation ; Polarization (electrochemistry) ; Benchmarking ; Diffraction ; Benchmark (surveying) ; Ferroelectricity ; Convolutional neural network ; Experimental data Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Changhua Jiang | Jiaming Gu | Hui Lu | Jie Zhou | Enze Zhang | Jinwen Guo | Cheng Zhang | Faxian Xiu
Source: Science Advances (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Acoustoelectric control of optoelectronic anisotropy for reconfigurable polarimetry Harnessing light polarization provides a powerful degree of freedom for optical communications, imaging, and sensing. Two-dimensional (2D) anisotropic semiconductors have emerged as a promising platform for miniaturized on-chip polarimetry; however, their polarization responses remain constrained by intrinsic crystal symmetries and static device geometries, limiting functional tunability. Here, we demonstrate continuous and dynamic control over optoelectronic anisotropy in a 2D rhenium disulfide semiconductor through acoustoelectric coupling with surface acoustic waves (SAWs). SAW propagation through the semiconducting channel substantially enhances the photovoltage response via an acousto-drag photovoltaic mechanism. This acoustoelectric coupling not only amplifies the global photoresponse but also continuously rotates its polarization symmetry axis-shifting from the intrinsic orientation of the rhenium disulfide crystal to that of the lithium niobate substrate-as the acoustic power increases. Crucially, by adopting a machine learning algorithm, i.e., random forest, we achieve independent and simultaneous detection of both optical power and linear polarization angle within a planar, integrated device. These findings establish a previously unknown paradigm in acoustoelectronics for dynamically reconfigurable polarimetry, mediated by hybrid phonon-charge interactions in 2D materials. Anisotropy ; Polarization (electrochemistry) ; Semiconductor ; Lithium niobate ; Polarimetry ; Nanosecond ; Coupling (piping) ; Optical power 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena
Authors: Iman Peivaste | Salim Belouettar | Francesco Mercuri | Nicholas Fantuzzi | Hamidreza Dehghani | Razieh Izadi | Halliru Ibrahim | Jakub Lengiewicz | Mael Belouettar-Mathis | Kouider Bendine | Ahmed Makradi | Martin Horsch | Peter Klein | M. Hachemi | Heinz A. Preisig | Yacine Rezgui | Natalia Konchakova | Ali Daouadji
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed. Pace ; Leverage (statistics) ; Generative grammar ; Key (lock) ; Probabilistic logic ; Applications of artificial intelligence Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Yagnik Bandyopadhyay | Dylan Noel Serrao | Houlong Zhuang
Source: arXiv (Cornell University) (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

Large Language Model Assisted Discovery of Optimal Dopants for Enhanced Thermoelectric Performance in CoSb$_3$ Based Skutterudites We present a data-driven approach for accelerating the discovery of high-performance CoSb$_3$-based skutterudites by curating a comprehensive dataset of compositions with various filler elements from over 300 research articles. Leveraging large language models (LLMs), we extract and embed compositional representations, which are then used to train a regression head for predicting thermoelectric figure of merit. Compared to traditional deep neural networks relying on elemental descriptors such as atomic radii, our LLM-based model achieves significantly lower mean-squared error losses. We further employ the trained model to propose novel filler compositions with promising thermoelectric properties. Finally, we support these predicted candidates through density functional theory and molecular dynamics calculations to assess their electrical and thermal conductivity. This data-driven approach demonstrates the potential of combining natural language processing, machine learning, and quantum simulations for thermoelectric materials design. Thermoelectric effect ; Thermoelectric materials ; Language model ; Dopant ; Artificial neural network ; NIST ; Density functional theory ; Thermal ; Molecular dynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Weize Lv | Hu Bi | Yong Fang | Haiyang Lin | Guodong Jiang | Yucai Shen
Source: Composites Communications (article, 2026-02-06)
Field: Materials Science — Polymers and Plastics

Accelerating the design of flame-retardant polypropylene composites: An approach driven by explainable ensemble machine learning and Bayesian optimization Interpretability ; Bayesian optimization ; Inverse ; Bayesian probability ; Kriging ; Gaussian process ; Surrogate model ; Design of experiments Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer crystallization and properties
Authors: Bo Wang | Yangyang Xu | Yumei Zhou | Dezhen Xue
Source: Journal of Materials Informatics (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Multi-fidelity learning in materials informatics: methodologies, applications, and outlook Data-driven methods are transforming materials design by accelerating the discovery of new compounds and the optimization of existing systems. However, the progress of such approaches is often constrained by the scarcity of high-fidelity data from experiments and advanced simulations. Multi-fidelity (MF) learning has emerged as a powerful strategy to address this challenge by integrating information from diverse data sources that vary in accuracy and cost. In this review, we provide a systematic overview of the major methodologies for MF learning, including statistical and parametric models, machine learning models with fidelity features, correction-based models such as co-kriging, deep learning frameworks, and active learning frameworks. We discuss the strengths, limitations, and typical applications of each method in materials science, with illustrative examples spanning electronic structure modeling, alloy design, and interatomic potential development. Cross-cutting issues are also examined, including the bias-variance trade-off, data requirements for nested vs. non-nested designs, and computational scalability. Finally, we highlight outstanding challenges and outline emerging opportunities, such as physics-informed and generative MF models, standardized datasets, and integration with autonomous laboratories. Together, these perspectives define a roadmap for advancing MF learning as a core enabler of next-generation materials discovery. Generative grammar ; Deep learning ; Data integration ; Enabling ; Generative model ; Core (optical fiber) ; Fidelity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Drug Discovery Methods
Authors: Yagnik Bandyopadhyay | Dylan Noel Serrao | Houlong Zhuang
Source: ArXiv.org (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Large Language Model Assisted Discovery of Optimal Dopants for Enhanced Thermoelectric Performance in CoSb$_3$ Based Skutterudites We present a data-driven approach for accelerating the discovery of high-performance CoSb$_3$-based skutterudites by curating a comprehensive dataset of compositions with various filler elements from over 300 research articles. Leveraging large language models (LLMs), we extract and embed compositional representations, which are then used to train a regression head for predicting thermoelectric figure of merit. Compared to traditional deep neural networks relying on elemental descriptors such as atomic radii, our LLM-based model achieves significantly lower mean-squared error losses. We further employ the trained model to propose novel filler compositions with promising thermoelectric properties. Finally, we support these predicted candidates through density functional theory and molecular dynamics calculations to assess their electrical and thermal conductivity. This data-driven approach demonstrates the potential of combining natural language processing, machine learning, and quantum simulations for thermoelectric materials design. Thermoelectric effect ; Thermoelectric materials ; Language model ; Dopant ; Artificial neural network ; NIST ; Density functional theory ; Thermal ; Molecular dynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Inas Bouzateur | Sarah GUENOU | L. Djoudi | Hamza Bennacer | Mohammed Ouali | Ayat HADJI | Pietro Romano | Ali Benghia
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction for Double Oxide Perovskites Lattice constant using a hierarchical Convolutional Neural Network Convolutional neural network ; Robustness (evolution) ; Artificial neural network ; Deep learning ; Lattice (music) ; Feature extraction ; Reliability (semiconductor) ; Lattice constant Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Perovskite Materials and Applications
Authors: Hans Rabus
Source: Radiation Physics and Chemistry (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

When data science and physics-ignorant machine learning produces hallucinations: Comment on “Machine Learning-Driven Prediction of Gamma-Ray Shielding Performance in TeO2−Bi2O3−ZnO−BaF2 Glasses” [Tang, Y., Radiation Physics and Chemistry, https://doi.org/10.1016/j.radphyschem.2025.113586] Variance (accounting) ; Training set ; Electromagnetic shielding ; Regression ; Experimental data ; Training (meteorology) ; Regression analysis ; Labeled data Radiation Shielding Materials Analysis ; Radiation Detection and Scintillator Technologies ; Nuclear materials and radiation effects
Authors: Xiaoyang Wang | Chengqian Zhang | Zhenyu Wang | Hanyu Liu | Jian Lv | Han Wang | Weinan E | Yanming Ma
Source: National Science Review (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Computational discovery of high-temperature superconducting ternary hydrides via deep learning The discovery of novel high-temperature, or even room-temperature, superconducting materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant challenge for both experimental and computational investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be effectively addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal-structure exploration, physics-informed screening and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-[Formula: see text] superconductors with predicted [Formula: see text] K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-[Formula: see text] hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary systems. Ternary operation ; Superconductivity ; Hydride ; Scalability ; Space (punctuation) ; Chemical space ; Transformative learning ; Parameter space Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Hydrogen Storage and Materials
Authors: Josep Saurí
Source: Molecules (article, 2026-03-07)
Field: Chemistry — Spectroscopy

The Evolving Landscape of NMR Structural Elucidation Nuclear Magnetic Resonance (NMR) spectroscopy has long been a cornerstone in the structural elucidation of molecules, offering unique insights into atomic-level connectivity, conformation, and dynamics. Over the past decades, methodological and technological advances have significantly expanded its capabilities and applications. This manuscript charts the evolution of NMR from classical 1D/2D experiments to modern methods empowered by ultrahigh magnetic fields, cryogenic probes, non-uniform sampling, new methodologies, and hyperpolarization. We emphasize the growing synergy between experiment and computation, where automated analysis, quantum chemical calculations, and machine learning are dramatically enhancing the accuracy and efficiency of structure determination. We also highlight NMR's broadening scope in areas ranging from complex mixtures and natural products to biomolecular and materials science. Scope (computer science) ; Nuclear magnetic resonance spectroscopy ; Cornerstone ; Natural (archaeology) ; Characterization (materials science) Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Jan Pavšek | Alexander Mitsos | Elvis Jonathan Sim | Jan G. Rittig
Source: ArXiv.org (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure, liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model promising for practical application in data scarce scenarios of chemical engineering practice. Artificial neural network ; Enthalpy ; Component (thermodynamics) ; Graph ; Process (computing) ; Experimental data ; Function (biology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks