jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryo Murakami | Seiji Miura | Akihiro Endo | Satoshi Minamoto
Source: ArXiv.org (preprint, 2025-04-29)
Field: Materials Science — Materials Chemistry

Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery High-entropy alloys have attracted attention for their exceptional mechanical properties and thermal stability. However, the combinatorial explosion in the number of possible elemental compositions renders traditional trial-and-error experimental approaches highly inefficient for materials discovery. To solve this problem, machine learning techniques have been increasingly employed for property prediction and high-throughput screening. Nevertheless, highly accurate nonlinear models often suffer from a lack of interpretability, which is a major limitation. In this study, we focus on local data structures that emerge from the greedy search behavior inherent to experimental data acquisition. By introducing a linear and low-dimensional mixture regression model, we strike a balance between predictive performance and model interpretability. In addition, we develop an algorithm that simultaneously performs prediction and feature selection by considering multiple candidate descriptors. Through a case study on high-entropy alloys, this study introduces a method that combines anchor-guided clustering and sparse linear modeling to address biased data structures arising from greedy exploration in materials science. Property (philosophy) ; Focus (optics) ; Feature selection ; Greedy algorithm ; Nonlinear system ; Cluster analysis ; Feature (linguistics) ; Linear model Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Marc U. Engelhardt (16642483) | Finn Mier (21514038) | Markus O. Zimmermann (1284975) | Frank M. Boeckler (509961)
Source: Figshare (article, 2025-11-25)
Field: Chemistry — Physical and Theoretical Chemistry

A QM-AI Approach for the Acceleration of AccurateAssessments of Halogen‑π Interactions by Training NeuralNetworks Noncovalent interactions, such as halogen bonds (XB), play a crucial role in molecular recognition and drug design, yet halogen···π contacts remain comparatively underexplored. Here, we report a proof-of-concept QM-AI approach that integrates high-level quantum mechanical (QM) calculations with neural networks (NNs) to predict halogen···π interaction energies. Nearly 1.4 million MP2/TZVPP single-point calculations on halobenzene–benzene complexes were carried out to generate exhaustive training data, which were represented by simple geometric descriptors as input features for machine learning. The resulting neural network model is specifically designed to capture σ-hole-driven halogen···π interactions under well-defined geometric constraints. The resulting model reproduced reference interaction energies with excellent accuracy (<i>R</i><sup>2</sup> = 0.998, RMSE = 0.16 kJ/mol) and maintained strong performance on independent, randomly generated and PDB-derived test sets. Previously, we have demonstrated in a benchmarking study that “gold standard” CCSD(T) energies of this interaction can be appropriately represented by MP2/TZVPP calculations, but at a better calculation efficiency by 2 orders of magnitude (∼10<sup>2</sup>). Consequently, we herein exploit a methodological “extension” from CCSD(T) → MP2 → NNs. Our approach maintains accuracy close to CCSD(T) benchmarks while achieving a runtime acceleration of up to 8 orders of magnitude (∼10<sup>8</sup>) compared to MP2 calculations. This study demonstrates the feasibility of fast, accurate neural network models based on QM data for halogen···π interactions in a QM-AI approach. Artificial neural network ; Acceleration ; Benchmarking ; Exploit ; Feature (linguistics) ; Mean squared error ; Simple (philosophy) ; Training (meteorology) Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jiefeng Liu | Wen Li | Heng Zhang | Yiteng Tang | Xingang Li | Chuying Liu | Thomas Wu | Jian Hao | Zhengyong Huang
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2025-11-27)
Field: Materials Science — Polymers and Plastics

A Generative Deep Learning-Based Multiscale Molecule Screening Method for Low-Viscosity Natural Ester Insulating Oil Natural ester insulating oils are environmentally friendly alternatives to mineral oils, but their high viscosity limits their applicability in cold regions. To address this challenge, this study proposes a virtual screening framework that uses GAN to generate insulating oil molecules and a viscosity prediction model to select low-viscosity candidate molecules. The GAN model generates 4,034 candidate triglyceride structures, which are evaluated using an XGBoost model with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value ranging from 0.84 to 0.92. From these, nine low-viscosity candidates are selected based on their predicted viscosity and molecular weight. Next, Density Functional Theory (DFT) is performed to calculate key properties such as molecular ionization potential (IP), electron affinity (EA) and HOMO-LUMO energy gap, which in turn identifies molecules with good insulating properties. Finally, molecular dynamics (MD) simulations indicate that the selected molecules exhibit a 26.23% reduction in kinematic viscosity at 273.15 K compared to commercial ester oils. These findings provide theoretical guidance for the molecular-level design of insulating oils suitable for low-temperature scenarios, offering a computational foundation for developing eco-friendly alternatives with optimized properties. Viscosity ; Molecule ; Molecular dynamics ; Density functional theory ; Ionization ; Environmentally friendly Polymer composites and self-healing ; Power Transformer Diagnostics and Insulation ; Phase Equilibria and Thermodynamics
Authors: Yan Bai | Baosheng Liu | Yuezhong Zhang | Shaohua Zhang | Pengpeng Wu | Xiaoxia Ren | Yinghui Wei
Source: Engineered Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accurate Long-Term Atmospheric Corrosion Prediction of Weathering Steel via SHapley Additive exPlanations (SHAP) Enhanced Neural Network The growth of the photovoltaic (PV) industry is driving demand for structural materials with superior corrosion resistance, strength, and environmental adaptability.Weathering steels have emerged as promising candidates.However, the long-term atmospheric corrosion performance of weathering steels in complex environments remains insufficiently studied.This paper proposes a data-driven framework to predict corrosion rates of weathering steels based on a neural network model enhanced by SHapley Additive exPlanations (SHAP).The model takes alloy composition, environmental parameters, and exposure time as inputs and outputs the predicted atmospheric corrosion rates.Bayesian optimization is utilized to explore the optimal network configuration and hyperparameter set.SHapley Additive exPlanations is used to identify physically meaningful features, leading to the construction of a SHapley Additive exPlanations -enhanced neural network (ENN).The ENN is evaluated through five-fold cross-validation to assess its robustness and generalization capability, achieving a coefficient of determination (R) of 0.941.Furthermore, when tested on oneyear independent data, the model maintains a low relative error of 2.31%.Moreover, the predicted long-term corrosion trends of WP720 weathering steel under ISO-defined atmospheric conditions align well with established corrosion mechanisms.These results demonstrate the enhanced neural network reliability, high accuracy, and potential for practical deployment in corrosion forecasting and lifespan assessment of weathering steels. Weathering steel ; Artificial neural network ; Weathering ; Corrosion ; Atmospheric model Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Machine Learning in Materials Science
Authors: Shukai Wu | Yan Luo | Shuli Niu | Wentao Lin | Chao Fang
Source: The Journal of Physical Chemistry Letters (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

AI-Informed Solvation Engineering for Thermogalvanic Electrolytes with High Thermopower Thermogalvanic cells utilize redox couple ions to achieve continuous heat-to-electricity conversion, yet the development of high-performance electrolytes is limited by the complex relationship between solvation of redox couple ions and thermopower. We present a data-driven machine learning strategy to identify high-performance solvents for thermogalvanic electrolytes. Our approach parametrizes the Seebeck coefficient by leveraging the strong correlation between DFT-derived ion-solvent interaction features and limited experimental data. A hybrid ML framework combining high-throughput DFT and interval regression was developed to screen extensive solvent candidates with SHAP analysis identifying polarity-related features as dominant factors. Experimental validation enabled model augmentation via a binary feature filter that incorporates solvation-relevant molecular features beyond two-body interactions. The framework validated known effective solvents and identified new candidates, which were confirmed via solvation structure calculations and UV-vis spectroscopy. This solvation-driven ML approach accelerates electrolyte discovery while providing fundamental insights into design principles for large solvation entropy differences. Solvation ; Electrolyte ; Binary number ; Seebeck coefficient ; Ion ; Strong electrolyte ; Entropy (arrow of time) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Ryo Murakami | Seiji Miura | Akihiro Endo | Satoshi Minamoto
Source: Science and Technology of Advanced Materials Methods (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery High-entropy alloys have attracted attention for their exceptional mechanical properties and thermal stability. However, the combinatorial explosion in the number of possible elemental compositions renders traditional trial-and-error experimental approaches highly inefficient for materials discovery. To solve this problem, machine learning techniques have been increasingly employed for property prediction and high-throughput screening. Nevertheless, highly accurate nonlinear models often suffer from a lack of interpretability, which is a major limitation. In this study, we focus on local data structures that emerge from the greedy search behavior inherent to experimental data acquisition. By introducing a linear and low-dimensional mixture regression model, we strike a balance between predictive performance and model interpretability. In addition, we develop an algorithm that simultaneously performs prediction and feature selection by considering multiple candidate descriptors. Through a case study on high-entropy alloys, this study introduces a method that combines anchor-guided clustering and sparse linear modeling to address biased data structures arising from greedy exploration in materials science. Property (philosophy) ; Focus (optics) ; Feature selection ; Greedy algorithm ; Nonlinear system ; Cluster analysis ; Feature (linguistics) ; Linear model Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Libin Wu | Haiyong Weng | Shangpeng Sun | Dapeng Ye
Source:  (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Integrating Micro-Hyperspectral Imaging and Mie Scattering for Early Detection of Microbial Contamination in Liquid Culture <b><sc>Abstract.</sc></b> Liquid culture is a nutrient-rich liquid medium used to grow and study microorganisms, yet it is highly vulnerable to contamination, which only can be observed at the symptomatic stage when turbidity appears, leaving limited opportunities for timely intervention. Optical chemometrics has been extensively explored for determination of contamination by analyzing the absorption spectra of specific molecular bands. However, its effectiveness is often constrained in complex solutions due to water's strong absorption. To this end, this study presents a rapid, non-invasive optical approach that combines micro-hyperspectral imaging with machine learning to explore scattering characteristics for the early detection of microbial contamination. First, micro-hyperspectral imaging data were collected using the transmittance mode. Secondly, hyperspectral data was processed with feature selection algorithms-Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE) to reduce the dimensionality of the original hyperspectral data. Thirdly, machine learning models, including Neural Networks (NN), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Partial Least Squares Discriminant Analysis (PLSDA), were trained using the selected feature vectors. The experimental results demonstrated that the combination of PLSDA and CARS achieved the highest accuracy of 89.4% in detecting asymptomatic microbial contamination, 99.2% in uncontaminated and 99.7% in contaminated liquid culture. Finally, Mie scattering simulations were conducted to confirm the characteristic scattering patterns associated with early microbial contamination. Overall, this study highlights the effectiveness of integrating Mie scattering, hyperspectral imaging, and machine learning to enable a rapid optical approach for early detection of microbial contamination in liquid cultures. Hyperspectral imaging ; Contamination ; Mie scattering ; Light scattering ; Materials science ; Environmental science ; Remote sensing ; Scattering ; Computer science ; Optics ; Artificial intelligence ; Physics ; Biology ; Geology ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Giulio Benedini | Antoine Loew | Matti Hellström | Silvana Botti | Miguel A. L. Marques
Source: ArXiv.org (preprint, 2025-08-21)
Field: Materials Science — Materials Chemistry

Universal Machine Learning Potential for Systems with Reduced Dimensionality We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for three-dimensional systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01-0.02 angstrom and errors in the energy below 10~meV/atom across all dimensionalities. These results demonstrate that state-of-the-art universal machine learning interatomic potentials have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces. Benchmark (surveying) ; Curse of dimensionality ; Range (aeronautics) ; Energy (signal processing) ; Energy minimization ; Equivariant map ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; 2D Materials and Applications
Authors: Andre Mazega Fontes
Source: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) (article, 2025-11-27)
Field: Materials Science — Biomaterials

Unlocking nanocellulose production through the implementation of real-time monitoring strategies ENG- The work first investigates the TEMPO-mediated oxidation of bleached kraft pulps, establishing the influence of reaction temperature, reagent concentration, and fiber surface area on oxidation kinetics. A strong linear relationship between NaOH consumption and carboxyl content was demonstrated, offering a practical strategy for real-time monitoring and precise reaction control. Complementary to this, portable near-infrared (NIR) spectroscopy coupled with multivariate calibration was shown to accurately predict the extent of oxidation across different pulps. By integrating PCA- and LDA-based classification of fiber types with pulp-specific regression models, the study proposes a versatile approach to overcome feedstock variability in monitoring strategies. Building on this, the research explores how the inherent chemical and supramolecular features of eucalyptus, pine, hemp, and sisal pulps govern both oxidation efficiency and the structural, rheological, and morphological properties of the resulting CNMs. The results demonstrate that wood-derived CNMs favor entangled fibrillar networks, while non-wood pulps yield more fragmented nanostructures, underscoring the need for tailored processing strategies depending on material origin. In parallel, enzymatic pretreatment using endoglucanase was examined as a sustainable alternative to chemical oxidation. Fiber-level changes were tracked using polarized light microscopy, while reducing sugar release was monitored as a proxy for enzymatic activity. Machine learning tools, particularly random forest regression, were developed to predict sugar concentration from measurable parameters such as fines content, apparent viscosity, and stirring power consumption. This novel approach minimizes reliance on offline chemical assays and opens pathways for agile process control. Together, these contributions provide an integrated framework for CNF production that couples mechanistic insights with real-time monitoring and predictive modeling. By demonstrating scalable approaches for both chemical and enzymatic pretreatments, this thesis bridges the gap between fundamental cellulose science and industrial implementation, highlighting how control over reaction kinetics, feedstock variability, and monitoring strategies can accelerate the deployment of nanocellulose as a sustainable material of the future Nanocellulose ; Raw material ; Cellulose ; Production (economics) ; Papermaking ; Pulp (tooth) ; Process (computing) ; Work (physics) ; Automatic summarization Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Biofuel production and bioconversion
Authors: Tiannian Zhu | Zhong Fang | Quansheng Wu | Hongming Weng
Source: Chinese Physics B (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

MaterialsGalaxy: A platform fusing experimental and theoretical data in condensed matter physics Abstract Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated “silos”. Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records — spanning diffraction, crystal growth, computations, and literature — based on their underlying atomic structures. The platform integrates artificial intelligence (AI) tools, including large language models (LLMs) for knowledge extraction, generative models for crystal structure prediction, and machine learning property predictors, to enhance data interpretation and accelerate materials discovery. We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources, uncovering hidden correlations and guiding the design of novel materials. By bridging the long-standing gap between experiment and theory, MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials. Bridging (networking) ; Generative grammar ; Generative model ; Core (optical fiber) ; Experimental data ; Interpretation (philosophy) ; Property (philosophy) Machine Learning in Materials Science
Authors: Shuai Ren | Xinyu Zhang | H. Li | Changjun Hu | Dandan Chen
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Advanced analysis of defect clusters in nuclear reactors using machine learning techniques Studying defects and defect clusters in reactor materials is essential for understanding the degradation mechanisms of materials under irradiation. By uncovering the formation and evolution of defects, this paper provides critical insights for enhancing the radiation resistance of reactor materials and extending their service life. The contributions of this study are as follows: (1) Integration of a large-scale molecular dynamics (MD) dataset generated by cascade collisions and the application of machine learning (ML) techniques to investigate point defects and their clusters in reactor pressure vessel (RPV) materials; (2) Proposal of a novel clustering method based on physical characteristics, enabling efficient classification of defect clusters while effectively reducing data noise; (3) Development of a component-based defect cluster configuration recognition method using a dual-pointer lattice-filling technique, accurately capturing all defects and identifying several cluster morphologies observed in experiments; (4) Demonstration of the algorithm's outstanding performance in handling systems containing millions of atomic coordinates, showcasing its scalability and robustness; (5) Visualization of the three-dimensional spatial distribution of defect clusters and provision of two-dimensional spatial density distribution maps for vacancy clusters and interstitial clusters, offering precise characterization of spatial relationships within defect clusters and new insights into the irradiation mechanisms of materials. Computer science ; Artificial intelligence ; Nuclear engineering ; Computational biology ; Biology ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Graphite, nuclear technology, radiation studies