jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maciej Kaliciak | Tadeusz Uhl | Marek Nowak
Source: Materials (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for Estimation of Uniaxial Tensile Test Equivalent Properties from Small Punch Test The Small Punch Test (SPT) has been developed as a small sample technique for the evaluation of mechanical properties of structural materials. However, the SPT is subject to systematic biases, resulting in inaccurate estimates of Uniaxial Tensile Test (UTT) properties. In this study, an experimental approach has been adopted to investigate the potential of neural networks to predict UTT-equivalent behavior from SPT measurements. An experimental database containing paired SPT and UTT data has been prepared for three boiler steels (10H2M, 13HMF, and 15HM) in both new and service-degraded states. Convolutional neural networks (CNN) have been trained and evaluated for curve-to-curve prediction. The working hypothesis is that CNN models, by exploiting local curve features, are capable of reducing the systematic bias inherent to SPT, generating estimates of UTT properties with precision comparable to conventional UTT measurements. Consistent with trends in applied deep learning, the results confirm the robustness of convolutional architectures. In general, the findings provide strong evidence that CNNs can translate SPT data into UTT equivalent material properties, thereby bridging a long-standing methodological gap and supporting automated evaluation of structural steels in service. Convolutional neural network ; Robustness (evolution) ; Test data ; Bridging (networking) ; Ultimate tensile strength ; Artificial neural network Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels
Authors: Jing Wang (6206297) | Yusheng Jia (7572653) | Rui Li (4631) | Yupeng Zhang (632467) | Caiping Ma (5487746) | Yang Zhang (30734) | Riguang Zhang (1601113) | Xiaofeng Li (342614) | Baojun Wang (172491) | Lixia Ling (640233)
Source: Figshare (article, 2025-05-30)
Field: Chemistry — Inorganic Chemistry

Machine Learning-AcceleratedPrediction of Lewis AcidSite Positioning for Long-Chain Mono-olefin Aromatization on Zn<sup>2+</sup>/HZSM‑5 Catalysts Long-chain mono-olefin aromatization is an important reaction during synthesis of coal to aromatics, in which the positioning of Lewis acid in ZSM-5 zeolite determines the performance. This study aims to reveal the relationship between the location of Lewis acid and catalytic performance during the aromatization of C6–C8 mono-olefins and to rapid-screen the optimized positioning of Lewis acid to enhance the efficiency and selectivity of aromatic production. Based on density functional theory (DFT) to obtain high-quality data sets of C6 and C7 dehydrogenation reactions in Zn<sup>2+</sup>/HZSM-5 catalysts, a data-driven ML model is developed to predict the C8 dehydrogenation energy barriers and screen the Lewis acid sites T5-T3 or T11-T3 with high dehydrogenation activity, while DFT confirms the accuracy of the results. The BTX (benzene, toluene, and xylene) selectivity reached 64.1% among various products via microkinetic analysis after investigating the cyclization process and cracking reactions on the Lewis acid site positioning, where the dehydrogenation activity of the catalyst is optimal. The six-membered ring is found to be the key cyclic intermediate due to the pore confinement effect, which effectively stabilizes cyclic intermediates, and toluene and <i>p</i>-xylene show a small steric effect through weak interactions analysis. This can provide important clues and research paradigms for the rational design of ZSM-5 zeolite modified with different metal species. Aromatization ; Dehydrogenation ; Lewis acids and bases ; Catalysis ; Zeolite ; Selectivity ; Toluene ; Isomerization Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Ross James Urquhart | Alexander van Teijlingen | Tell Tuttle
Source: Chemical Communications (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Application of neural network potentials to modelling transition states Transition state modelling remains a challenge in computational chemistry, often requiring chemical intuition and expensive, iterative recalculations. This work presents a more efficient approach using umbrella sampling to explore free energy surface and more importantly, the conformational space around transition states, reducing the effort needed for structure identification. By employing a machine learning potential, ANI-2x, [C. Devereux <i>et al.</i>, <i>J. Chem. Theory Comput.</i>, 2020, <b>16</b>, 4192-4202] to drive the sampling, we demonstrate enhanced FES exploration and efficiency compared to traditional DFT methods. The approach is applied to two different reactions: amide formation <i>via</i> a thioester intermediate and disulphide bridge formation. It was found that ANI-2x performs poorly at the prediction of high energy structures yet provides rapid, thorough sampling of reaction pathways making it useful for informing further calculations at higher levels of theory. Intuition ; Computer science ; Artificial neural network ; Transition state ; Transition (genetics) ; Molecular dynamics ; Chemistry ; Biological system ; Statistical physics ; Artificial intelligence ; Biochemical engineering ; Theoretical computer science ; Computational chemistry ; Physics ; Cognitive science ; Engineering ; Biology Analytical Chemistry and Chromatography ; Various Chemistry Research Topics
Authors: Wei Li | Liming Ma
Source: ACS Applied Polymer Materials (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction Model for the Stretching-Induced II–I Phase Transition of Polybutene-1 Semicrystalline polymers undergo real-time changes in their multiscale aggregation structures when subjected to mechanical stretching, which significantly influence their macroscopic material properties. Establishing a quantitative relationship between external field parameters and structural evolution in semicrystalline polymers is essential for guiding both their production and practical applications. In this study, we focus on polybutene-1, a polymorphic semicrystalline polymer, and develop a data-driven machine learning approach to predict its stretching-induced II–I phase transition. By constructing a feature descriptor incorporating yield stress and employing the random forest algorithm, we achieve accurate prediction of phase transition behaviors under various stretching conditions and for different molecular chain architectures. This study not only offers valuable insights for the design and application of polybutene-based materials but also presents a promising framework for establishing multiscale structure–property relationships in semicrystalline polymers. Crystallinity ; Phase transition ; Polymer ; Phase (matter) ; Field (mathematics) ; Stress (linguistics) ; Feature (linguistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Li Ni | Ryan Jacobs | Matthew Lynch | Vidit Agrawal | Kevin G. Field | Dane Morgan
Source: ArXiv.org (preprint, 2025-01-14)
Field: Materials Science — Surfaces, Coatings and Films

Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to estimate the performance of deep learning-based object detection models for quantifying defects in transmission electron microscopy (TEM) images, focusing on detecting irradiation-induced cavities in TEM images of metal alloys. We developed a random forest regression model that predicts the object detection F1 score, a statistical metric used to evaluate the ability to accurately locate and classify objects of interest. The random forest model uses features extracted from the predictions of the object detection model whose uncertainty is being quantified, enabling fast prediction on new, unlabeled images. The mean absolute error (MAE) for predicting F1 of the trained model on test data is 0.09, and the $R^2$ score is 0.77, indicating there is a significant correlation between the random forest regression model predicted and true defect detection F1 scores. The approach is shown to be robust across three distinct TEM image datasets with varying imaging and material domains. Our approach enables users to estimate the reliability of a defect detection and segmentation model predictions and assess the applicability of the model to their specific datasets, providing valuable information about possible domain shifts and whether the model needs to be fine-tuned or trained on additional data to be maximally effective for the desired use case. Random forest ; Object (grammar) ; Artificial intelligence ; Electron microscope ; Computer science ; Pattern recognition (psychology) ; Statistical physics ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Hongwei Du | Hong Wang
Source: ArXiv.org (preprint, 2025-09-06)
Field: Materials Science — Materials Chemistry

Unveiling the critical factors in crystal structure graph representation: a comparative analysis using streamlined MLPSets frameworks Graph Neural Networks have rapidly advanced in materials science and chemistry,with their performance critically dependent on comprehensive representations of crystal or molecular structures across five dimensions: elemental information, geometric topology, electronic interactions, symmetry, and long-range interactions. Existing models still exhibit limitations in representing electronic interactions, symmetry, and long-range information. This study compares physics-based site feature calculators with data-driven graph representation strategies. We find that the latter achieve superior performance in representation completeness, convergence speed, and extrapolation capability by incorporating electronic structure generation models-such as variational autoencoders (VAEs) that compress Kohn-Sham wave functions and leveraging multi-task learning. Notably, the CHGNet-V1/V2 strategies, when integrated into the DenseGNN model,significantly outperform state-of-the-art models across 35 datasets from Matbench and JARVIS-DFT, yielding predictions with accuracy close to that of DFT calculations. Furthermore, applying a pre-training and fine-tuning strategy substantially reduces the prediction error for band gaps of complex disordered materials, demonstrating the superiority and potential of data-driven graph representations in accelerating materials discovery. Extrapolation ; Representation (politics) ; Graph ; Convergence (economics) ; Artificial neural network ; Electronic structure ; Graph theory ; Feature (linguistics) Machine Learning in Materials Science
Authors: Léo Thiercelin
Source: Materials Letters (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Data driven prediction of thermal hysteresis in NiTi-based high entropy shape memory alloys This study provides a data-driven approach to predict thermal hysteresis in NiTi-based high-entropy shape memory alloys (HESMAs), using a dataset of NiTi-based shape memory alloys that includes the four transformation temperatures (M s , M f , A s , A f ). Machine learning models based on extremely randomized trees were proposed to predict M s and the thermal hysteresis (∆T = A f - M s ), using 11 physical material descriptors as inputs. M s prediction is dominated by mixing enthalpy and the valence electron ratio, while hysteresis depends on 5 additional balanced features such as the melting temperature, electronegativity, atomic radius, and the mixing entropy. The predictions models are highly reliable with mean absolute errors of 38 °C for M s and 14 °C for ΔT. Finally, a design strategy is proposed for (NiCuCo)(TiZrHf) HESMAs that optimises both M s and ΔT with targeted values. • Database of NiTi-like high entropy and shape memory alloys of the transformation temperatures. • Machine learning algorithm for thermal hysteresis and martensitic transformation temperature prediction. • Tool for high entropy shape memory alloys design with desired properties. • Exploration of the compositional space of (NiCuCo)(TiZrHf) HESMAs. Shape-memory alloy ; Diffusionless transformation ; Hysteresis ; High entropy alloys ; Thermal ; Valence electron ; Entropy (arrow of time) ; Enthalpy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Machine Learning in Materials Science
Authors: Raphaël La Rocca (6689231) | Anthony Cioppa (11580124) | Enrico Ferrarini (14072247) | Monica Höfte (21093732) | Marc Van Droogenbroeck (11580133) | Edwin De Pauw (7163165) | Gauthier Eppe (1718800) | Loïc Quinton (1598419)
Source: Figshare (article, 2025-09-01)
Field: Chemistry — Spectroscopy

Relational Graph ConvolutionalNetwork for RobustMass Spectrum Classification Supervised machine learning methods have shown impressive performance in interpreting mass signals and automatically segmenting spatially meaningful regions in Mass Spectrometry Imaging (MSI). Such segmentation generates maps that provide researchers with valuable insights into sample composition and serve as a foundation for downstream statistical analyses. However, these models often require data set-specific preprocessing and do not fully exploit the rich mass features available in high-resolution mass spectrometry (HRMS). Unlike low-resolution mass spectrometry, HRMS reveals additional features such as mass defects and repeated mass differences that carry important chemical information. In this work, we propose a novel deep learning architecture based on a Relational Graph Convolutional Network (R-GCN) that captures and leverages those HRMS mass features. Our model explicitly encodes structural features such as mass defects and known mass differences to represent each spectrum as a graph, enabling the learning of associations between chemically related ion families. To the best of our knowledge, no existing deep learning models for MSI classification incorporate this level of chemically informed mass structure. Most existing methods treat spectra as flat vectors or image-like inputs, thereby ignoring the underlying mass relationships. We evaluate our R-GCN approach against several conventional machine learning and deep learning baselines across diverse MSI data sets, demonstrating its robustness to common signal variations (e.g., mass shift, ion loss). Finally, we integrate Class Activation Mapping (CAM) to enhance model interpretability, enabling the identification of ion families that are relevant to specific biological or spatial regions. Preprocessor ; Robustness (evolution) ; Pattern recognition (psychology) ; Mass spectrum ; Graph ; Deep learning ; Convolutional neural network ; Identification (biology) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Brook Xhabrahimi | Emil I. Jaffal | Danila Shiryaev | Nikhil K. Barua | Madison Donohoe | Natalia Pozdnyakova | Mariam Ismail | Balaranjan Selvaratnam | Ehsan Niknam | Holger Kleinke | Anton O. Oliynyk
Source: ChemRxiv (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd10RuCd3, a Neutron Absorption Material The search for a neutron absorption material was aided by materials informatics tools, composing the developed recommendation engines. A new member in the ternary RE10MCd3 series, Gd10RuCd3, was predicted and synthesized as an experimental validation. The recommendation engine is based on partial least squares – discriminant analysis (PLS-DA) crystallographic site processing to classify the site preference of elements in compounds crystallizing in the Y10RuCd3-type structure and upon projecting other elements onto Principal Component map, the titled compound emerged as the top candidate. The distinguishing feature of the developed recommendation engine is in the three explainable methods utilized in the predictive framework: unrestricted, conservative, and cluster methods. Predictions are visualized with convenient property projection tools. The prediction was validated with high-temperature synthesis. The structure was confirmed with single crystal and powder X-ray diffraction. The cold-water-quenched samples quenched from 800 °C have smaller unit cell volume than the samples quenched from 600 °C annealing temperature. Transport properties measurements show an unusually low thermal conductivity (5-7 W·m-1K-1) and the trend change indicating a possible structure transition between 600 and 800 °C. The unusual low thermal conductivity was predicted with machine-learning model that focuses on thermoelectric property prediction. DFT analysis reveals the presence of a 0D electride phenomenon. The neutron cross section and absorption based on the constituent elements put our material within the top 0.4% of neutron absorbers. The negative thermal expansion and high mass absorption suggest Gd10RuCd3 as an attractive neutron absorption material. Characterization (materials science) ; Intermetallic ; Absorption (acoustics) ; Neutron capture ; Materials science ; Neutron ; Nuclear engineering ; Nuclear physics ; Metallurgy ; Nanotechnology ; Composite material ; Engineering ; Physics ; Alloy Nuclear Materials and Properties ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science
Authors: Wei Li (7081) | Liming Ma (470302)
Source: Figshare (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction Model for the Stretching-InducedII–I Phase Transition of Polybutene‑1 Semicrystalline polymers undergo real-time changes in their multiscale aggregation structures when subjected to mechanical stretching, which significantly influence their macroscopic material properties. Establishing a quantitative relationship between external field parameters and structural evolution in semicrystalline polymers is essential for guiding both their production and practical applications. In this study, we focus on polybutene-1, a polymorphic semicrystalline polymer, and develop a data-driven machine learning approach to predict its stretching-induced II–I phase transition. By constructing a feature descriptor incorporating yield stress and employing the random forest algorithm, we achieve accurate prediction of phase transition behaviors under various stretching conditions and for different molecular chain architectures. This study not only offers valuable insights for the design and application of polybutene-based materials but also presents a promising framework for establishing multiscale structure–property relationships in semicrystalline polymers. Crystallinity ; Phase transition ; Polymer ; Phase (matter) ; Field (mathematics) ; Stress (linguistics) ; Feature (linguistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Shaharukh Shaikh | Priti Yenare | Nayana Dorugade | C. E. Patil | Prof. Renuka Gavali
Source: IOSR Journal of Computer Engineering (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Predictive Medicine Model For Plant Wellness Assessment Agriculture is the backbone of many economies, providing food and raw materials essential for human survival. However, plant diseases significantly impact crop productivity, leading to financial losses and food shortages. Early detection and appropriate treatment of plant diseases are critical to maintaining agricultural sustainability. This project, "Plant Disease Detection and Fertilizer Suggestion," presents an AI-powered system that identifies plant diseases using deep learning techniques and suggests suitable fertilizers based on the detected diseases. Traditional plant disease detection methods rely on manual inspection, which is time consuming, prone to human error, and requires expert knowledge. With advancements in computer vision and artificial intelligence, automated systems can now accurately classify plant diseases and provide recommendations. Our system utilizes Convolutional Neural Networks (CNNs) to analyze plant leaf images and detect diseases with high accuracy. By leveraging deep learning, the model can differentiate between multiple plant diseases, ensuring precise diagnosis. Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Statistical Methods and Applications
Authors: J. Wang | Kui Tang | Jin-xiang Wang | Minhui Gu | Yuanbo Li
Source: International Journal of Impact Engineering (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Machine learning and numerical simulation based prediction of the penetration efficiency of depleted uranium long-rod projectiles: a multi-factor analysis Projectile ; Artificial neural network ; Penetration (warfare) ; Kinetic energy ; Depleted uranium ; Uranium ; Computer simulation ; Penetration depth High-Velocity Impact and Material Behavior ; Nuclear Materials and Properties ; Energetic Materials and Combustion
Authors: Yang Hu | Vladyslav Turlo
Source: ArXiv.org (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

Hydroxylation-driven surface reconstruction at the origin of compressive-to-tensile stress transition in metal oxide nanoparticles Experiments reveal negative (non-Laplacian) surface stresses in metal oxide nanoparticles, partly associated with humidity during fabrication and annealing. Using a neural network interatomic potential for MgO, we prove that water adsorption induces surface hydroxylation, shifting facets from {100} to {110} to {111} and switching the average surface stress from positive to negative. Predicted lattice strains versus nanoparticle size agree well with experiments, clarifying experimental correlations. The new framework informs broad applications in catalysis, sensors, batteries, and biomedicine. Oxide ; Surface stress ; Nanoparticle ; Adsorption ; Transition metal ; Metal ; Surface (topology) Machine Learning in Materials Science ; Magnesium Oxide Properties and Applications ; Nanoporous metals and alloys