jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuyu Zhu | Gang Liu | Eric Inae | Tengfei Luo | Ming Jiang
Source: ArXiv.org (preprint, 2025-05-15)
Field: Materials Science — Materials Chemistry

Learning Repetition-Invariant Representations for Polymer Informatics Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aerospace. However, existing graph neural network methods, though effective for small molecules, only model the single unit of polymers and fail to produce consistent vector representations for the true polymer structure with varying numbers of units. To address this challenge, we introduce Graph Repetition Invariance (GRIN), a novel method to learn polymer representations that are invariant to the number of repeating units in their graph representations. GRIN integrates a graph-based maximum spanning tree alignment with repeat-unit augmentation to ensure structural consistency. We provide theoretical guarantees for repetition-invariance from both model and data perspectives, demonstrating that three repeating units are the minimal augmentation required for optimal invariant representation learning. GRIN outperforms state-of-the-art baselines on both homopolymer and copolymer benchmarks, learning stable, repetition-invariant representations that generalize effectively to polymer chains of unseen sizes. Graph ; Invariant (physics) ; Polymer ; Representation (politics) ; Spanning tree ; Artificial neural network ; Graph theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Wenlong Li | Yu Liu | Zhendong Li | Pei Zhang | Xinghua Li | Tao Ouyang
Source: Chinese Physics B (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Thermal transport properties of 2D narrow bandgap semiconductor Ca <sub>3</sub> N <sub>2</sub> , Ba <sub>3</sub> P <sub>2</sub> , and Ba <sub>3</sub> As <sub>2</sub> : Machine learning potential study Abstract By combining neuroevolution potential (NEP) with phonon Boltzmann transport theory, we systematically investigate the thermal transport properties of three two-dimensional (2D) narrow bandgap semiconductors: Ca 3 N 2 , Ba 3 P 2 , and Ba 3 As 2 . The room-temperature lattice thermal conductivities ( κ L ) of Ca 3 N 2 , Ba 3 P 2 , and Ba 3 As 2 considering only three-phonon scattering are 6.60 W/mK, 11.90 W/mK, and 8.88 W/mK, respectively. When taking into account the higher-order phonon (four-phonon) scattering processes, the κ L of these three materials decrease to 6.12 W/mK, 9.73 W/mK and 6.77 W/mK, respectively. Among these systems, Ba 3 As 2 undergoes the most pronounced suppression with a reduction of 23.8%. This is mainly due to the greater scattering phase space which enhances the four-phonon scattering. Meanwhile, it is revealed that unlike the traditional evaluation using the P 4 / P 3 ratio as an indicator of the strength of four-phonon interactions, the thermal conductivity of Ba 3 P 2 exhibits weaker four-phonon suppression behavior compared to Ba 3 As 2 , despite hosting a higher P 4 / P 3 ratio. That is to say, the strength of four-phonon scattering cannot be evaluated solely by the ratio of P 4 / P 3 . These results presented in this work shed light on the thermal transport properties of such new 2D semiconductors with narrow bandgaps. Materials science ; Band gap ; Semiconductor ; Thermal ; Semiconductor materials ; Optoelectronics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; MXene and MAX Phase Materials
Authors: Soheila Kookalani | Gözde Başak Özturk | Hamidreza Alavi | Erika Pärn | Ioannis Brilakis
Source: Proceedings of the ... ISARC (article, 2025-07-27)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Predicting the Fatigue Strength of Steel: Influence of Composition and Processing Parameters The prediction of fatigue strength in steel is critical for the design and analysis of structural components, given the high costs and time associated with fatigue testing and the severe consequences of fatigue failures.This study explores the application of the CatBoost machine learning algorithm to predict the fatigue strength of steel based on chemical composition, processing parameters, and mechanical properties.The model's interpretability is enhanced by integrating Shapley additive explanations, providing insights into the contributions of key features such as tempering temperature (TT), chromium content (Cr), and molybdenum content (Mo).The proposed framework achieves high predictive accuracy, with an R of 0.952 and an RMSE of 31.625.This study fosters trust and utility in safetycritical applications by addressing the limitations of black-box models.The results underscore the potential of interpretable machine learning in advancing fatigue strength prediction methodologies and informing structural and construction engineering practices, while Enhancing the SHAPbased feature importance analysis to refine the selection of key predictors, potentially simplifying the model while maintaining accuracy. Computer science ; Machine learning ; Artificial intelligence ; Composition (language) Metal Alloys Wear and Properties ; Fatigue and fracture mechanics ; Advanced machining processes and optimization
Authors: Yulong Zhu | Yu Cao | Boyang Xu | Jieke Zhang | Qi He | Rui Luo | Xuhong Jia | Quanyi Liu | Ziyong Hou
Source: Journal of Materials Informatics (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Deep learning-enhanced cellular automaton framework for modeling static recrystallization behavior Accurately characterizing dislocation behavior - the driving force behind nucleation and growth of recrystallized grains - has long been a formidable challenge for traditional cellular automata methods. We are proud to unveil a groundbreaking, machine learning-enhanced cellular automaton framework that fundamentally transforms the mapping of dislocation substructures while expertly modeling static recrystallization (SRX) behavior. By integrating the dislocation escape assumption during recovery, we decisively eliminate spatial resolution limitations and capture the intricate mesoscopic dynamics of dislocation evolution with unprecedented precision. Our innovative approach has demonstrated extraordinary effectiveness in predicting the recrystallization kinetics of a typical austenitic alloy, bolstered by strong experimental validation. The deep learning-based dislocation implantation module, SRX-net, stands out as a game-changer, surpassing traditional techniques such as random forests and U-net by showcasing exceptional capabilities in identifying complex intracrystalline substructures and managing uneven strain concentrations. The proposed advanced simulations yield critical insights into dislocation density variations, highlighting significant local fluctuations driven by dislocation escape. Importantly, while the migration and accumulation of dislocations may falter in meeting nucleation conditions during grain growth, our model excels in accurately predicting average SRX grain sizes without introducing any unphysical artifacts. This revolutionary framework dramatically reduces time-to-solution, empowering comprehensive parametric studies and enabling near real-time recrystallization simulations, thus setting a bold new standard for industrial applications. Nucleation ; Dislocation ; Cellular automaton ; Recrystallization (geology) ; Mesoscopic physics ; Parametric statistics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Machine Learning in Materials Science
Authors: Zhe Liu | Pu Yang | Zhiyuan Dai | Yifan Zhou | Ruihao Chen | Hongqiang Wang
Source:  (article, 2025-06-08)
Field: Materials Science — Materials Chemistry

Machine Learning Co-pilot Framework forMolecular Additive Discovery in Perovskite Solar Cells Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors. The present work thus proposes an effective approach, Co-Pilot for Perovskite Additive Screener (Co-PAS), an ML-driven framework designed to accelerate additive screening for perovskite solar cells (PSCs). Co-PAS overcomes predictive biases by integrating the Molecular Scaffold Classifier (MSC) for scaffold-based pre-screening and utilizing Junction Tree Variational Autoencoder (JTVAE) latent vectors to enhance molecular structure representation, thereby improving the accuracy of power conversion efficiency (PCE) predictions. Leveraging Co-PAS, we integrate domain knowledge to screen an extensive dataset of 250,000 molecules from PubChem, prioritizing candidates based on predicted PCE values and key molecular properties such as donor number, dipole moment, and hydrogen bond acceptor count. This workflow leads to the identification of several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccinimide ester (BTN), which, to our knowledge, has not been explored as an additive in PSCs and achieves a device PCE of 25.20%. Our results underscore the potential of Co-PAS in advancing additive discovery for high-performance PSCs. Perovskite (structure) ; Computer science ; Materials science ; Engineering physics ; Artificial intelligence ; Physics ; Engineering ; Chemical engineering Machine Learning in Materials Science
Authors: Songda Li | Chen Zou | Liping Chen | Yang, Fangwen | Yang, Zhifu | Chen, Bingwei | Zhongkang Han | Wentao Yuan | Hangsheng Yang | David J. Wales | Yong Wang
Source: ArXiv.org (preprint, 2025-09-15)
Field: Materials Science — Materials Chemistry

Orbital Hybridization-Driven Stabilization and Reactivity on an Asymmetrically Reconstructed Polar CeO2(100) Surface Understanding and controlling the atomic structure of polar oxide surfaces is essential for unraveling surface reactivilty and designing advanced catalytic materials. Among these, the polar CeO2(100) surface is a prototypical and industrially important system in heterogeneous catalysis. However, due to the vast complexity of the surface configurations, its reconstruction behavior remains an open question. Here, we report a previously unidentified asymmetric (1x2) reconstructed structure on the CeO2(100) surface, discovered through an integrated approach that combines global structure search algorithms, machine learning-based atomic potential models, density functional theory (DFT) calculations, and in situ scanning transmission electron microscopy (STEM). The reconstructed surface is both thermodynamically and kinetically stable, characterized by an alternating arrangement of Ce3+ and Ce4+ ions, increased interlayer spacing, and reconfigured surface oxygen atoms. Importantly, the formation of localized Ce3+ polarons introduces occupied 4f states that strongly hybridize with surface O 2p orbitals, resulting in new occupied electronic states below the Fermi level. This orbital hybridization activates the O 2p states, enhances their electron-donating capacity, and facilitates the dissociation of adsorbed molecules such as H2O. These findings reveal a fundamental orbital-mediated mechanism by which surface reconstruction governs electronic structure and reactivity, offering critical insights and a new design strategy for tuning catalytic performance on polar oxide surfaces. Surface reconstruction ; Polar ; Dissociation (chemistry) ; Polaron ; Density functional theory ; Electronic structure ; Adsorption ; Oxide ; Molecule Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Chandra Chowdhury
Source: Advanced Intelligent Discovery (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting hydrogen storage capacities for nanoporous materials, with a focus on metal–organic frameworks (MOFs). Utilizing a comprehensive dataset of 98,694 entries, we explore the efficacy of both methods in handling complex, nonlinear relationships. SVR utilizes polynomial kernels, but QSVR incorporates quantum feature maps and quantum kernels to improve regression in high‐dimensional environments. Our analysis, based on seven structural and physical properties of MOFs, reveals that SVR outperforms QSVR, achieving an average root mean squared error (RMSE) of 1.84 compared to QSVR's 2.11 across 100 experiments. Interestingly, when the feature set is reduced to two key parameters (density and void fraction), both models show improved performance, with QSVR demonstrating competitive accuracy (RMSE of 1.30) relative to SVR (RMSE of 1.10). These findings illuminate both the current limitations and the potential of QSVR in real‐world applications, particularly in materials informatics. While classical methods currently maintain an edge in accuracy, the promising performance of QSVR with reduced feature sets suggests significant potential for quantum machine learning approaches. As quantum hardware and algorithms continue to advance, we anticipate enhanced capabilities of QSVR in modeling complex, high‐dimensional datasets, potentially revolutionizing predictive modeling in materials science. Support vector machine ; Nanoporous ; Quantum ; Mean squared error ; Regression ; Feature (linguistics) ; Regression analysis ; Polynomial regression ; Polynomial Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Wenyang Ding | Jiang Guo | Meng An | Koji Tsuda | Junichiro Shiomi
Source: ArXiv.org (preprint, 2025-02-08)
Field: Materials Science — Materials Chemistry

Human-AI collaboration for modeling heat conduction in nanostructures In recent years, materials informatics, which combines data science and artificial intelligence (AI), has garnered significant attention owing to its ability to accelerate material development, reduce costs, and enhance product design. However, despite the widespread use of AI, human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes more substantial roles that capitalize on human intuition or domain expertise. Consequently, true human-AI collaborations, where integrated insights can be maximized, are scarce. This study considers the problem of heat conduction in a two-dimensional nanostructure as a case study. An integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity. This approach is used to determine the parameters that govern phonon transmission over the full range of frequencies and incidence angles. During operation, the self-learning entropic population annealing technique, which combines entropic sampling with a surrogate machine learning model, generates a small dataset that can be interpreted by a human. Therefore, data-efficient and global modeling is achieved, and parameters with physical interpretations are developed, which can guide nanostructural design to produce materials with specific properties. The proposed framework can leverage the complementary strengths of humans and AI, thereby enhancing the understanding and control of materials. Thermal conduction ; Nanostructure ; Materials science ; Nanotechnology ; Business ; Composite material Machine Learning in Materials Science
Authors: Maria Salveline Pinheiro Aquino | Maxwell Tavares Monte | Pedro F.G. Silvino | Daniel V. Gonçalves | José Carlos Alexandre de Oliveira | Sebastião M. P. Lucena | Andréa Pereira da Silva
Source: Process Safety and Environmental Protection (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Hydrogen storage in zeolites by adsorption: impact of charges and descriptors for artificial intelligence models Hydrogen storage ; Nanoporous ; Adsorption ; Zeolite ; Grand canonical ensemble ; Volume (thermodynamics) ; Energy storage Hydrogen Storage and Materials ; Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xingze Geng | Wentao Zhang | Lin‐Wang Wang | Xiangying Meng
Source: Research Square (preprint, 2025-10-27)
Field: Materials Science — Materials Chemistry

Origin of the Machine Learning Forces Field Errors Across Metal Elements Field (mathematics) ; Range (aeronautics) ; Face (sociological concept) ; Surface (topology) ; Energy (signal processing) ; Development (topology) ; Key (lock) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Force Microscopy Techniques and Applications
Authors: Lin Tan (147501) | Yue Wang (65477) | Hailiang Zhang (691203) | Jinyu Sun (222947) | Qiong Yang (28822) | Xiao Yang (19898) | Zhimin Zhang (408803) | Hongmei Lu (2630605)
Source: Figshare (article, 2025-12-12)
Field: Chemistry — Analytical Chemistry

DeepMIR: AHybrid Convolutional Neural Network-TransformerFramework for Accurate Identification of Target Components from Mid-InfraredSpectra of Mixtures Accurate identification of components from mid-infrared (MIR) spectra of mixtures remains a significant challenge in analytical chemistry due to severe spectral overlap and instrumental variability. To address this, we present DeepMIR, a deep learning framework designed for targeted component identification in complex mixtures using user-defined spectral libraries. DeepMIR uniquely integrates a convolutional neural network for extracting high-fidelity local spectral features and a transformer encoder to capture global, long-range dependencies across the spectrum. This hybrid architecture enables robust identification of target components even when reference and mixture spectra are acquired using different techniques, such as transmission and attenuated total reflectance. The model is trained and validated on a data set of over 67,000 synthetically augmented spectral pairs, achieving an accuracy of 99.5% on the test set, with statistical significance confirmed by <i>t</i> test analysis (<i>p</i> < 0.05). Extensive real-world validation demonstrates high accuracy across diverse scenarios, 94.8% for complex liquid solvents, 93.9% for solid pigment mixtures relevant to cultural heritage, and 99.1% for commercial blended textiles. Dedicated limit-of-detection studies establish reliable detection thresholds of 20% v/v in liquids and 10% w/w in solids. To ensure accessibility, DeepMIR has been deployed as an open-access web server, providing a powerful and practical tool for the scientific community that significantly outperforms traditional library search methods in both accuracy and reliability. Convolutional neural network ; Pattern recognition (psychology) ; Identification (biology) ; Artificial neural network ; Component (thermodynamics) ; Encoder ; Test set ; Set (abstract data type) ; Spectral signature Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yufan Shen | Feng Zhang | Mingjun Li | Guo Li | Dasheng Zhu | Hao Su | Wenchun Jiang
Source: Applied Physics A (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

The effect of twin boundary spacing, orientation and separation of grain boundary affected zones on the mechanical properties of twinned Cu-Ag alloys Crystal twinning ; Ultimate tensile strength ; Deformation (meteorology) ; Dislocation ; Grain boundary ; Maxima and minima ; Stress (linguistics) ; Boundary (topology) ; Flow stress ; Orientation (vector space) Microstructure and mechanical properties ; Metallurgy and Material Forming ; High Temperature Alloys and Creep
Authors: A. Yang | Dingfeng Jin | Mingkang Liu | Daye Zheng | Qi Wang | Qiangqiang Gu | Jian‐Hua Jiang
Source: ArXiv.org (preprint, 2025-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory, which still largely relies on case by case studies. Here, we employ a high-throughput screening strategy, applying a multi-step filter to the Materials Project database (&gt;154,000 materials), which yielded 2,519 candidate materials for detailed first-principle evaluation. From these calculations, we identify 32 NLO materials with strong shift current response ($σ$ &gt; 100 $μA/V^2$). Our work reveals that layered structures with $C_{3v}$ symmetry and heavy $p$-block elements (e.g. Te, Sb) exhibit apparent superiority in enhancing shift current. More importantly, 9 of these compounds show shift current response peaks in the IR region, with the strongest reaching 616 $μA/V^2$, holding significant application potential in fields such as IR photodetection, sensing, and energy harvesting. Beyond identifying promising candidates, this work establishes a comprehensive and high-quality first-principles dataset for NLO response, providing a solid foundation for future AI-driven screening and accelerated discovery of high-performance NLO materials, as demonstrated by a prototype machine-learning application. Current (fluid) ; Nonlinear optical ; Infrared ; Nonlinear system ; Work (physics) ; Optical materials ; Filter (signal processing) ; Foundation (evidence) Crystal Structures and Properties ; 2D Materials and Applications ; Photorefractive and Nonlinear Optics
Authors: Zongjun Li
Source: Research Square (preprint, 2025-08-20)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Sorghum Starch With Machine Learning and Near-Infrared Spectroscopy Sorghum ; Starch ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Food science ; Agronomy ; Physics ; Chromatography ; Optics ; Biology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Flügel, Simon
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Chebifier 2: An Ensemble for Chemistry Presentation given at the Ontologies4Chem Workshop 2025. Abstract: Chemical classification is a complex task without a one-size-fits-all solution. Rule-based approaches are explainable and reliable, but require expert knowledge. Deep Learning methods are easier to scale, but depend on the presence of sufficient data and are black boxes to the user. Chebifier 2 is an ensemble approach that combines deep learning, rule-based and LLM-based approaches to leverage their individual strengths, covering a total of more than 1,700 ChEBI classes. Leverage (statistics) ; Task (project management) ; Ensemble learning ; Deep learning ; Presentation (obstetrics) ; Ensemble forecasting Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics