jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nayem Ahmed | Ahmed Ramadan Ahmed | Samin Rhythm | Catalin Teodoriu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Metals and Alloys

Experiments and Machine Learning Investigation of Hydrogen-Assisted Fatigue Crack Growth in Vintage Pipeline Steels under Hydrogen–Natural Gas Blending Pipeline (software) ; Paris' law ; Hydrogen ; Fracture toughness ; Fracture mechanics ; Fracture (geology) ; Scanning electron microscope ; Microstructure ; Sensitivity (control systems) ; Toughness Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Material Properties and Failure Mechanisms
Authors: I. Isomäki | Michael Gasik
Source: International Journal of Refractory Metals and Hard Materials (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

An approach for a design of functionally gradated hard metals processing combining thermodynamics and data-driven machine learning Processing of FGM hard metals is possible starting from two homogenous compositions where the final gradient of properties (hardness, toughness) will result from liquid phase migration. Control and prediction of this process demands a knowledge of thermodynamics of the systems. Here such analysis for the WC-Co-TiC system has been performed to obtain the driving force for the migration of liquid phase during and after sintering of FGM hard metals, with updated data on volumetric fractions of compounds and liquid phase. Parameters of starting compositions (density, grain size, chemistry) have been linked with the liquid phase composition, properties and its migration pressure. Creation of functionally gradated composition of hard metals starting from uniform compositions for two initially homogeneous specimens of the WC-TiC-Co system has been simulated for various masses of the specimens. It is shown that the grain size of the WC is the most critical factor for liquid migration, which in many cases overcomes influence of TiC additions (wetting effect and the phases equilibrium). To develop a proper FGM design process, simple machine learning methods were applied to do feature extraction in order to get links between compositions, masses, WC grain size and properties like hardness and fracture toughness yet without deployment of a specific model. Obtained data can be used of backwards engineering to select starting specimens for a desired hardness and toughness gradients either directly or via metamodels when sufficient amount of experimental data are available and when known engineering correlations for properties could be considered sufficient. • FGM WC-Co can be produced from homogenous parts at temperatures of the liquid phase • The WC grain size, TiC and Co content determine the intrinsic migration pressure • Data analysis shows how a biphasic starting sample can transform into FGM structure • The method can be expanded to other compositions with experimental data. Machine design ; Design of experiments ; Work (physics) ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Model Reduction and Neural Networks
Authors: B. Naskręcki | P. Balewski | M. Janicki | J. Kubiak | J. Lorenz | J. Malinowski | D. Brzeziński
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Detecting symmetry groups in crystallographic tilings In crystallography, identifying symmetry groups is crucial for understanding material properties and structures.Our research combines mathematical theory with machine learning to automate the detection of symmetry groups in crystallographic tilings, cf.[3][4][5].By analyzing growth function coefficients derived from orphic diagrams [1], we develop a hierarchical classification approach that precisely identifies the 17 wallpaper groups in two-dimensional space.This interdisciplinary approach bridges pure mathematics with practical applications in crystallography and material science.Crystallographic groups characterize the symmetry of periodic patterns in nature.The classification of these groups is wellestablished mathematically, but identifying them from physical data remains challenging.In two dimensions, 17 distinct wallpaper groups exist, each with unique combinations of translations, rotations, reflections, and glide reflections.Our work develops an AI-driven approach to classify these groups based on their growth characteristics rather than conventional geometric analysis.Growth functions in crystallography describe how the number of cells, edges, and vertices increases as a periodic tiling expands.For a tessellation with symmetry group G, the growth function fd(m,n) counts the number of d-dimensional cells when expanding m steps in one direction and n steps in another.These functions have the form: f d (m,n) = C d0 mn + C d1 m + C d2 n + C d3 .The orphic diagram visualizes these functions, revealing deep connections between coefficients and symmetry properties.As demonstrated in [1] these diagrams provide a complete topological description of the tessellation.Using decision tree classifiers, feature engineering, and a hierarchical framework, we have developed a method to classify most of the 17 wallpaper symmetry groups based solely on growth function coefficients.Our approach exploits mathematical properties, particularly divisibility relationships between coefficients, to separate symmetry groups with perfect accuracy.The hierarchical classification begins with the most general group (p1) and progressively distinguishes groups with increasing symmetry complexity.By analyzing patterns in the coefficients, we identify rules that perfectly separate groups with different rotational, reflective, and glide symmetries.Our computations were done systematically with the help of the computer package [2] and recently improved to an efficient version implemented in Julia package. Symmetry (geometry) ; Tessellation (computer graphics) ; Symmetry group ; Function (biology) ; Group (periodic table) ; Symmetry operation ; Feature (linguistics) ; Group theory Quasicrystal Structures and Properties ; Machine Learning in Materials Science ; 3D Shape Modeling and Analysis
Authors: Muhammad Ishtiaq | Hong JaeYong | Kwon-Koo Cho | Sung-Gyu Kang | Jaekyung Sung | N.S. Reddy
Source: Electrochimica Acta (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network approach for predicting and interpreting specific capacitance in heteroatom-doped graphene supercapacitors Supercapacitor ; Graphene ; Capacitance ; Interpretability ; Artificial neural network ; Raman spectroscopy ; Stacking ; Electrode Supercapacitor Materials and Fabrication ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Weidong Dai | Cheng Qi | Jinpeng Zhao | Honglai Li | Weichang Zhou | Fang Li | Yipeng Zhao | Yicheng Wang | Xing Xu | Liang Ma | Xiaolin Wei
Source: ACS Applied Materials & Interfaces (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Nonvolatile Optoelectronic Synapses and Brain-Inspired Neuromorphic Applications Based on NbOCl <sub>2</sub> /α-In <sub>2</sub> Se <sub>3</sub> Heterojunction Two-dimensional van der Waals heterostructures have emerged as promising candidates for next-generation optoelectronic devices owing to their tunable band structures and strong light-matter interactions. However, achieving high-performance photodetection and neuromorphic functionalities within a single platform remains a major challenge, particularly in controlling distinct charge transport pathways of the same material system. Here, we report a multifunctional NbOCl<sub>2</sub>/α-In<sub>2</sub>Se<sub>3</sub> heterojunction device that integrates three conductive pathways: a NbOCl<sub>2</sub> channel, a hybrid vertical transport channel combining NbOCl<sub>2</sub> and α-In<sub>2</sub>Se<sub>3</sub>, and an α-In<sub>2</sub>Se<sub>3</sub> surface channel. This unique trichannel design enables efficient parallel carrier transport, resulting in remarkable optoelectronic performance characterized by a high responsivity of 2933 A/W, an ultrahigh external quantum efficiency of 7.67 × 10<sup>5</sup>%, and a detectivity of 2.29 × 10<sup>12</sup> Jones. Furthermore, the device exhibited fast photoresponse characteristics, with fast response time of 52 ms and slow decay time of 2122 ms. More importantly, the device demonstrates superior synaptic plasticity with high learning retention of 6.58 μA and low forgetting rate of 0.073, as quantitatively described by the Wickelgren power-law model. Using the optoelectronic synaptic behavior of the device, we simulated learning and forgetting in neural networks, and achieved an accuracy of 92.5% on the MNIST handwritten digit data set. This work highlights the potential of NbOCl<sub>2</sub>/α-In<sub>2</sub>Se<sub>3</sub> heterostructures as a versatile platform for integrated optoelectronic systems, enabling intelligent sensing and neuromorphic computing. Neuromorphic engineering ; Heterojunction ; Non-volatile memory ; Memristor ; Silicon 2D Materials and Applications ; Inorganic Chemistry and Materials ; Topological Materials and Phenomena
Authors: Elmira Shajari | David Gagné | Mandy Malick | Patricia Roy | M Delisle | Marie A. Brunet | F Boisvert | Jean‐François Beaulieu
Source: Journal of the Canadian Association of Gastroenterology (article, 2026-02-01)
Field: Chemistry — Spectroscopy

CAG Student Prize Paper – A5 NON-INVASIVE PEPTIDOMIC SIGNATURE DISTINGUISHING ACTIVE AND REMISSIVE IBD USING A NESTED CROSS-VALIDATED MACHINE-LEARNING STUDY Abstract Background Monitoring disease activity in inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, is critical for guiding therapy and preventing irreversible mucosal damage. Colonoscopy, the current gold standard, is invasive and impractical for frequent follow-up, while fecal calprotectin lacks precision within its diagnostic “gray zone.” In this context, stool proteomics provides a non-invasive window into intestinal inflammation through direct measurement of molecular effectors. Aims To establish a proof-of-concept study demonstrating that stool-derived peptides can be leveraged for accurate IBD activity classification (Active vs Remission) using an unbiased, reproducible nested cross-validation (NCV) machine-learning approach. Methods A total of 170 stool samples from IBD patients were collected and profiled using SWATH-DIA mass spectrometry. Feature selection was performed within the training loops only (Boruta, LASSO, RRF) across repeated subsampling, retaining peptides consistently identified in ≥ 70 % of runs. Stable features were used to train four classifiers (GLMNet, SVM-Radial, SVM-Linear, Naïve Bayes) under inner 5-fold tuning. Outer test folds provided fully unseen evaluation, and misclassified cases were tracked to assess biological ambiguity. Results Feature selection yielded 8–12 stable peptides per fold, mapping to 9 unique proteins, several of which recurred across ≥60% of outer folds. Models showed high and stable performance across folds with AUC = 0.94–0.97 and Balanced Accuracy = 0.84–0.90. Specificity remained high (≥ 0.90), while sensitivity ranged 0.77–0.93, confirming reliable detection of both active and remission states. Inner-CV results highlighted GLMNet and SVM-Radial as consistently strong performers, whereas Naïve Bayes achieved slightly higher mean sensitivity. Only a few borderline samples were repeatedly misclassified, suggesting biological rather than algorithmic uncertainty. Conclusions This work provides a proof of concept that stool-based peptidomic features can serve as reliable indicators of IBD activity when analyzed through robust, leakage-free machine-learning design. The study establishes a foundation for future translational studies aiming to refine and clinically implement stool peptide biomarkers as part of personalized, non-invasive IBD management. Funding Agencies CCC, CIHR Ulcerative colitis ; Calprotectin ; Inflammatory bowel disease ; Gold standard (test) ; Faecal calprotectin ; Disease ; Selection (genetic algorithm) ; Feature selection Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Dimitrios Sinodinos | Bahareh Nikpour | Jia Wei | Sushant K. Sinha | Xiaoping Ma | Kashif Rehman | Stephen Yue | Narges Armanfard
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challenging due to complex interactions among chemical compositions, processing parameters, and resultant microstructures. Traditional empirical and experimental methodologies, while effective, are often resource-intensive and lack adaptability to varied production conditions. Moreover, most existing approaches do not explicitly leverage the strong correlations among key mechanical properties, missing an opportunity to improve predictive accuracy through multitask learning. To address this, we present a multitask learning framework that injects multitask awareness into the prior of TabPFN--a transformer-based foundation model for in-context learning on tabular data--through novel fine-tuning strategies. Originally designed for single-target regression or classification, we augment TabPFN's prior with two complementary approaches: (i) target averaging, which provides a unified scalar signal compatible with TabPFN's single-target architecture, and (ii) task-specific adapters, which introduce task-specific supervision during fine-tuning. These strategies jointly guide the model toward a multitask-informed prior that captures cross-property relationships among key mechanical metrics. Extensive experiments on an industrial TSDR dataset demonstrate that our multitask adaptations outperform classical machine learning methods and recent state-of-the-art tabular learning models across multiple evaluation metrics. Notably, our approach enhances both predictive accuracy and computational efficiency compared to task-specific fine-tuning, demonstrating that multitask-aware prior adaptation enables foundation models for tabular data to deliver scalable, rapid, and reliable deployment for automated industrial quality control and process optimization in TSDR. Multi-task learning ; Adaptability ; Leverage (statistics) ; Key (lock) ; Process (computing) ; Software deployment Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Mustafa Emre ALTAY | Fatih Kahrıman
Source: Figshare (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Enhancing near-infrared spectroscopy calibration for accurate protein and gluten determination in wheat flour and intact grains using chemometric techniques Rapid and reliable determination of protein and gluten in wheat is crucial for quality assessment and process control. Near-infrared (NIR) spectroscopy provides a nondestructive alternative to conventional chemical analysis; however, its predictive performance depends strongly on preprocessing and modeling strategy. This study evaluated how different combinations of scatter correction, derivative, and wavelength-selection methods influence NIR calibration performance for predicting protein and gluten contents in both wheat flour and intact grain samples, using Partial Least Squares (PLS) and Support Vector Machine (SVM) regression models under identical conditions. The results demonstrated that SVM achieved superior prediction accuracy for both protein and gluten contents, particularly when combined with Standard Normal Variate (SNV) preprocessing and mild smoothing. Among the best-performing models, those developed from flour-based spectra generally achieved higher coefficients of determination (R<sup>2</sup>, Coefficient of Determination, up to 0.96) than those based on grain spectra (R<sup>2</sup> ≈ 0.88–0.90), reflecting reduced scattering and greater compositional uniformity in flour samples. The most successful combinations were SNV + SVM for protein prediction (R<sup>2</sup> = 0.99) and smoothing + SNV + Genetic Algorithm–Partial Least Squares (GA-PLS) + SVM for gluten prediction (R<sup>2</sup> = 0.93). Overall results revealed that combining NIR spectroscopy with optimized preprocessing and machine-learning algorithms enables rapid and precise quantification of wheat quality traits, supporting its broader application in industrial quality control and breeding programs. Partial least squares regression ; Calibration ; Gluten ; Support vector machine ; Wheat flour ; Preprocessor ; Data pre-processing ; Correlation coefficient ; Smoothing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food composition and properties
Authors: Elvan Sahin | Nicholas Kane | Joseph Barton | Jeremy Lee Hartvigsen | Meng Li | Temitayo O. Olowu | Micah Casteel
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Forecasting of Solid Oxide Electrolysis Cell Degradation Using Deep Transfer Learning Degradation (telecommunications) ; Transfer of learning ; Electrolysis ; Deep learning ; Convolutional neural network ; Prognostics ; Dependency (UML) Advancements in Solid Oxide Fuel Cells ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research
Authors: Dimitrios Sinodinos | Bahareh Nikpour | Jia Wei | Sushant K. Sinha | Xiaoping Ma | Kashif Rehman | Stephen Yue | Narges Armanfard
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challenging due to complex interactions among chemical compositions, processing parameters, and resultant microstructures. Traditional empirical and experimental methodologies, while effective, are often resource-intensive and lack adaptability to varied production conditions. Moreover, most existing approaches do not explicitly leverage the strong correlations among key mechanical properties, missing an opportunity to improve predictive accuracy through multitask learning. To address this, we present a multitask learning framework that injects multitask awareness into the prior of TabPFN--a transformer-based foundation model for in-context learning on tabular data--through novel fine-tuning strategies. Originally designed for single-target regression or classification, we augment TabPFN's prior with two complementary approaches: (i) target averaging, which provides a unified scalar signal compatible with TabPFN's single-target architecture, and (ii) task-specific adapters, which introduce task-specific supervision during fine-tuning. These strategies jointly guide the model toward a multitask-informed prior that captures cross-property relationships among key mechanical metrics. Extensive experiments on an industrial TSDR dataset demonstrate that our multitask adaptations outperform classical machine learning methods and recent state-of-the-art tabular learning models across multiple evaluation metrics. Notably, our approach enhances both predictive accuracy and computational efficiency compared to task-specific fine-tuning, demonstrating that multitask-aware prior adaptation enables foundation models for tabular data to deliver scalable, rapid, and reliable deployment for automated industrial quality control and process optimization in TSDR. Multi-task learning ; Adaptability ; Leverage (statistics) ; Key (lock) ; Process (computing) ; Software deployment Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: R.P. Sumithra | P.M. Siva Raja | R. Anuja | Mary A.G. Ezhil | Manjunathan Alagarsamy
Source: Sensing and Bio-Sensing Research (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Dual-band terahertz graphene–MXene–silver metasurface biosensor for brain tumor screening and early detection using machine learning This paper proposes a novel terahertz biosensor employing a multi-layer architecture of graphene, MXene and silver to detect brain tumors with high sensitivity. The proposed structure comprises a silicon dioxide substrate with periodic rectangular resonator arrays which provide dual band operation at two frequency bands of 0.1–0.3 THz and 0.4–0.45 THz. Finite element simulations performed in COMSOL Multiphysics demonstrated that the absorption characteristics of the sensor are strongly modulated by both the graphene chemical potential and the electromagnetic wave angle of incidence; peak absorptances of 98.7% and 97.3% are achieved under optimal conditions for each parameter respectively, with all absorption values verified to satisfy energy conservation (R + T + A = 1) through cosine-corrected S-parameter normalization. The refractive index sensitivity of the biosensor was found to be exceptionally high at 1538 GHz/RIU over the refractive index range of 1.3333–1.4833 RIU, spanning the biologically validated contrast between healthy and tumor-affected brain tissue. A very strong linear relationship between the resonance frequency shift and the refractive index of the biosensor yields a calibration curve F = −0.1550n + 0.6402 with a correlation coefficient of 99.326%. Machine learning validation with Locally Weighted Linear Regression (LWLR), conducted on a noise-augmented dataset of 980 samples with 10-fold cross-validation and independent test-set evaluation, shows that the proposed biosensor has high prediction accuracy with R 2 values as high as 95%. These results demonstrate that the proposed biosensor has significant potential for early stage brain tumor diagnostics and supports an integrated sensor-to-inference pipeline suitable for future clinical deployment. Terahertz radiation ; Biosensor ; Brain tumor ; Pattern recognition (psychology) MXene and MAX Phase Materials ; Terahertz technology and applications ; Metamaterials and Metasurfaces Applications