jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: C.J. Luis Pérez | Alexia Torres | I. Puertas
Source: Journal of Materials Research and Technology (article, 2025-03-10)
Field: Materials Science — Ceramics and Composites

Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications This study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB 2 ), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB 2 highly suitable for extreme environments, such as aerospace and nuclear applications. To the best of our knowledge, no comprehensive studies have addressed the manufacturing of ZrB 2 parts by EDM, positioning this research as a cutting-edge contribution. Two electrode materials, graphite (C) and copper-graphite (Cu-C), were used to analyze the material removal rate (MRR) and surface roughness (Ra) as functions of current intensity (I), pulse time (t i ), and duty cycle (η). Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) were used to model the response variables. While MLR was effective for MRR (R 2 > 0.9), ANN outperformed it in predicting Ra, especially for Cu-C electrodes (R 2 = 0.9366 vs. 0.3847 for MLR). Current intensity was the most influential parameter for MRR, while pulse time significantly affected Ra. Residual analysis confirmed ANN's superior accuracy for Ra, with residuals below ±1 vs. ±2 for MLR. The study culminated in the successful EDM manufacture of a ZrB 2 hexagonal nut, employing optimized parameters (I = 6 A, t i = 50 μs, η = 0.3, for the C electrode) derived using ANN models and particle swarm optimization. This result demonstrates the EDM process's ability to produce high-precision components with complex geometries, showcasing its versatility and industrial potential. Therefore, this study broadens the understanding of ZrB 2 machinability and expands its applications in advanced technologies. Materials science ; Ceramic ; Materials processing ; Engineering physics ; Metallurgy ; Process engineering ; Engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Advanced Machining and Optimization Techniques
Authors: Chuan-Shen Hu | Rishikanta Mayengbam | Kelin Xia | Tze Chien Sum
Source: Journal of Chemical Information and Modeling (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Quotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Effective data representations are key to the success of all learning models. Currently, the lack of comprehensive and accurate material representations has hindered AI-based design and discovery of 2D perovskites, limiting their potential for advanced photovoltaic applications. In this context, this work introduces a novel computational topology framework termed the quotient complex (QC), which serves as the foundation for the material representation. The proposed QC-based features are seamlessly integrated with learning models for the advancement of 2D perovskite design. At the heart of this framework lies the quotient complex descriptors (QCDs), representing a quotient variation of simplicial complexes derived from materials' unit cell and periodic boundary conditions. Differing from prior material representations, this approach encodes higher-order interactions and periodicity information simultaneously. Based on the well-established new materials for solar energetics (NMSE) databank, the proposed QC-based machine learning models exhibit superior performance against all existing counterparts. This underscores the paramount role of periodicity information in predicting material functionality, while also showcasing the remarkable efficiency of the QC-based model in characterizing materials' structural attributes. Context (archaeology) ; Computer science ; Quotient ; Photovoltaic system ; Representation (politics) ; Limiting ; Artificial intelligence ; Perovskite (structure) ; Topology (electrical circuits) ; Mathematics ; Mechanical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Covalent Organic Framework Applications
Authors: Kwabena Koranteng Asiedu | Luke E. K. Achenie | Tracy Asamoah | Emmanuel Kwesi Arthur | Nana Yaw Asiedu
Source: Industrial & Engineering Chemistry Research (article, 2025-02-10)
Field: Chemistry — Inorganic Chemistry

Incorporating Mechanistic Insights into Structure–Property Modeling of Metal–Organic Frameworks for <i>H</i><sub>2</sub> and <i>CH</i><sub>4</sub> Adsorption: A CGCNN Approach In this paper, we introduce a novel approach for understanding gas adsorption mechanisms in Metal–Organic Frameworks (MOFs) by combining a tailored Crystal Graph Convolutional Neural Network (AG-CGCNN) with game theory through Shapley Additive Explanations (SHAP). Our technique identifies the root causes of suboptimal performance in crystal structures through the analysis of softmax probabilities of performance outcomes, alongside the marginal contributions of crystalline and geometric features to these outcomes. Specifically, our approach discerns whether poor performance stems from inefficient atom–atom interactions indicating suboptimal gas molecule capture or from the subpar distribution of geometric characteristics, including surface area, pore limiting diameter (PLD), or largest cavity diameter (LCD). Additionally, the modeling framework adopted in this study clearly captures how intrinsic atomic features facilitate excess adsorption while volumetric spatial characteristics account for bulk absorption in complex porous MOF networks. Furthermore, we have introduced an innovative technique to quantitatively evaluate the amount of both excess gas absorbed within site interactions and bulk gas within the void spaces in a single MOF unit cell using a modified CGCNN regression model and SHAP. Additionally, we highlight the excellent performance of three 100 K-trained AG-CGCNN and demonstrate how its architecture can be used to determine the efficiency of a MOF gas capture system. Metal-organic framework ; Adsorption ; Property (philosophy) ; Chemistry ; Metal ; Materials science ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xiao-Dong Li | Chengxiang Liu | Yan-Yu Xie | Shiquan Feng | Xiuying Liu
Source: International Journal of Hydrogen Energy (article, 2025-03-14)
Field: Chemistry — Inorganic Chemistry

Machine-learning-assisted high-throughput computational study of CH4/H2 adsorption and separation in anion-pillared MOFs Throughput ; Adsorption ; Ion ; Materials science ; Chemistry ; Computer science ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Engineering ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Inorganic Fluorides and Related Compounds
Authors: Muhammad Irfan | Umme Habiba | Aqsa Maryam
Source: Microchimica Acta (review, 2025-06-16)
Field: Materials Science — Biomaterials

Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics Cancer ; Liposome ; Computational biology ; Bioinformatics ; Nanotechnology ; Computer science ; Medicine ; Biology ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; Advanced Biosensing Techniques and Applications
Authors: Quan Gan | Hai-Jing Song | Derek Elsworth | Sida Jia | Junjun Chen | Funing Ma | Qian Li | Yujun Yang | Xiaoping Wang | Zhenxue Dai
Source: Energy (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Deep learning-enhanced global sensitivity analysis for uncertainty quantification in THMC coupled scCO2-EGS Sensitivity (control systems) ; Uncertainty quantification ; Environmental science ; Engineering ; Computer science ; Machine learning High voltage insulation and dielectric phenomena ; Probabilistic and Robust Engineering Design ; Structural Response to Dynamic Loads
Authors: Ruili Li | Shu-Ting Huang | Yuyang Hu | Xiaotong Sun | Zhipeng Zhang | Zaixuan Yang | Qi Liu | Xiaoqing Chen
Source: Analytical Chemistry (article, 2025-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides Research on nanoplastic (NP) toxicity and their "carrier effects" on human health remains nascent, especially for real-time, in situ monitoring of metabolic reactions in live cells. Herein, we developed smart surface-enhanced Raman spectroscopy (SERS) slides using a cyclic centrifugation-enhanced electrostatic loading (CCEL) method to facilitatively track live-cell metabolic signals. The designed core-shell polystyrene NPs (mPS) with embedded Raman probes successfully identified intracellular accumulation via a distinct Raman-silent peak. The smart SERS slide effectively monitored the metabolic changes induced by mPS at the molecular level, distinguishing different stages of membrane interaction, the endocytosis process, endosomal aggregation, and cell apoptosis. Besides, this platform was employed to perform a real-time, in situ comparison of cell cycle alterations induced by bare NPs and their "carrier effects", revealing that NPs extended both the S and G2 phases in BEAS-2B cells, while the "carrier effects" further prolonged G2 and disrupted S-phase progression. Additionally, we integrated machine learning algorithms to accurately predict the cell cycle impacts associated with mPS and their "carrier effects". This study provides a label-free, in situ, real-time method for monitoring NP-induced metabolic changes in live cells, laying the groundwork for further investigation into cytotoxic behaviors and strategies to mitigate NP toxicity. Chemistry ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Analytical Chemistry (journal) ; Environmental chemistry ; Raman scattering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Sivagnana Sundaram Anandakrishnan | Suhas Yadav | Mohadeseh Tabeshfar | Mikko Nelo | Jani Peräntie | Yang Bai
Source: Materials & Design (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling • Incompatibility across models for specific compositing techniques is mitigated. • Different from conventional models, fitting constants become variables. • Contributors to upside-down composite functionality are established using new models. • Training dataset for artificial intelligence-driven, high-throughput recycling of piezoceramics is demonstrated. Using upside-down composites to recycle retired/discarded piezoceramics and then give them a second life in sensor applications paves the way towards sustainable production of piezoelectric materials. However, the piezoelectric properties of the recycled materials need to be significantly improved. The advancement of recycled materials should benefit from the recently developed AI-assisted methods, in order to minimize overconsumption of resources during the experimental trial and error. Previous works have identified obstacles to developing reliable models that can provide a systematic understanding of the contributors to the properties of the recycled materials. This work aims to overcome such obstacles by appropriately changing the fitting constants so that the models coincide with each experimental datapoint. These constants include descriptors of the microgeometry, size and orientation of the fillers, and the extent of polarization in the composites. By analyzing the variation of these constants between the datapoints, a clear perspective of the contributors to the properties of the recycled materials is established. This multi-variable approach is also extended to different fabrication techniques. The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes. Materials science ; Piezoelectricity ; Composite material Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Ultrasonics and Acoustic Wave Propagation
Authors: Grigory Smirnov | Oleg E. Peil | A. V. Ruban | S. I. Simak | A. B. Belonoshko
Source: Physical Review Materials (article, 2025-04-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of magnetism on Fe phase diagram under extreme conditions Iron is a major component of the cores of the Earth and inhabited exoplanets. Its phase diagram at extreme pressures (P) and temperatures (T) is the subject of extensive debate. While recent experiments provide evidence for the stability of the body-centered cubic (bcc) phase, several theoretical studies point to the stability (even though marginal) of the hexagonal close-packed phase (hcp). None of those studies considered the itinerant magnetism of iron at extreme conditions. We compute the high-pressure phase diagram of Fe using density functional theory-based molecular dynamics (DFT MD) in which the paramagnetic nature of Fe is treated within the model of thermally induced longitudinal spin fluctuations (LSF). The LSF DFT MD with 16 valence electrons favors bcc phase stability. Two-phase large-scale simulations with quantum accurate machine learning potentials provide us with both melting and hcp-bcc phase boundaries. The computed phase diagram agrees with most of the experimental data and solves most of the numerous controversies. We conclude that the account for magnetism results in the new physics of iron under extreme conditions and brings the theory in agreement with experiment and seismic data. We expect that the approach we use can be applied to other metals where itinerant magnetism is important. Materials science ; Magnetism ; Phase diagram ; Condensed matter physics ; Phase (matter) ; Diagram ; Physics ; Quantum mechanics ; Computer science Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Ankit Sinha | Nadja Sachs | Elena Kratz | Jessica Pauli | Sophia Steigerwald | Vincent Albrecht | Thierry M. Nordmann | Enes Ugur | Edwin H. Rodriguez | Marie-Luise Engl | Patricia Skowronek | Moritz von Scheidt | Hanna Winter | Daniela Branzan | Heribert Schunkert | Lars Mäegdefessel | Matthias Mann
Source: medRxiv (preprint, 2025-02-12)
Field: Chemistry — Spectroscopy

Spatially resolved proteomic signatures of atherosclerotic carotid artery disease Abstract Atherosclerotic plaque rupture remains a leading cause of adverse cardiovascular events, yet the molecular drivers of plaque vulnerability are incompletely understood. To address this challenge, we developed an integrated approach that combines histomorphology-guided spatial proteomics with machine learning to map protein signatures across spatially distinct plaque subregions. Our analysis revealed that vulnerability signatures concentrate in the necrotic core and fibrous cap subregions, and are significantly enriched for ossification, inflammation, cholesterol metabolism, and extracellular matrix degradation pathways. When comparing the vulnerability status across subregions, we found that the necrotic core has the most distinctive vulnerability-associated proteome, with 454 proteins significantly altered between stable and vulnerable states. We identified a mechanistic link between inflammation and oxidative stress, PCSK9 upregulation, and vascular smooth muscle cell dysfunction in vulnerable plaques. This finding suggests arterial PCSK9 as a therapeutic target beyond its established role in hepatic lipid metabolism. By employing machine learning, we developed and independently validated a seven-protein tissue panel (receiver operating characteristic– area under the curve = 0.86) and found a 12-protein serum panel to predict plaque vulnerability status. Thus, plaque vulnerability signatures are spatially concentrated in specific subregions and highlight actionable biomarkers and therapeutic targets for advanced carotid artery disease. Carotid arteries ; Cardiology ; Disease ; Carotid artery disease ; Medicine ; Internal medicine ; Carotid endarterectomy Advanced Proteomics Techniques and Applications ; Cerebrovascular and Carotid Artery Diseases ; Atherosclerosis and Cardiovascular Diseases
Authors: Yuan Ming-jun | Longzhen Ding | Ruibin Bai | Jian Yang | Zhi-Lai Zhan | Zihan Zhao | Qing Hu | Luqi Huang
Source: Microchemical Journal (article, 2025-06-06)
Field: Chemistry — Analytical Chemistry

Feature-level hyperspectral data fusion with CNN modeling for non-destructive authentication of “Weilian” from different origins Hyperspectral imaging ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial intelligence ; Authentication (law) ; Fusion ; Computer science ; Computer security Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Identification and Quantification in Food
Authors: Pengxin Wang | G.M.A.M. El-Fallah
Source: Journal of Nuclear Materials (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

A Data-driven machine learning model for radiation-induced DBTT shifts in RAFM steels • Built a stacking model (XGBoost, RF, GBDT, MLP) for DBTT prediction (R² = 0.96). • Identified dose, temperature, Ta, W, and Cr as key factors for DBTT evolution. • DBTT peaks from 150 to 300°C and >30 dpa, then drops above 350°C due to recovery. • Ta and W reduce DBTT by stabilising precipitates and limiting defect growth. • SHAP shows that W lowers DBTT via solid solution strengthening at 6–9% Cr and 0–0.04% Ta. This study develops a stacking ensemble machine learning model to predict ductile-to-brittle transition temperature (DBTT) in irradiated Reduced-Activation Ferritic-Martensitic (RAFM) steels. Using a dataset of 490 irradiation cases, the model integrates XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Multi-Layer Perceptron (MLP), achieving a high predictive accuracy (R² = 0.96) and outperforming individual models. The results highlight the significant influence of irradiation dose and temperature on DBTT Beyond 30 dpa, defect accumulation causes a sharp DBTT increase, while irradiation temperature exhibits a nonlinear effect, peaking at 150-300°C due to radiation-enhanced precipitation and declining above 350°C as defect recovery improves ductility. Additionally, alloying elements play a crucial role: Ta and W help mitigate embrittlement, moderate Cr (4–6 wt.%) increases DBTT, and higher Cr levels (>6 wt.%) reduce it at elevated temperatures. SHAP analysis reveals that W is particularly effective in reducing embrittlement in alloy with moderate Cr (6–9 wt.%) and low Ta, while higher Cr concentrations (>6 wt.%) help stabilise DBTT at elevated temperatures. To enable practical alloy design, a genetic algorithm was combined with the model to optimise steel compositions under defined irradiation conditions (200–350 °C, 10 dpa). The approach successfully identified candidate alloys with predicted DBTT values below 50 °C. This study provides a robust predictive framework for understanding and optimising DBTT in irradiated RAFM steels, offering valuable insights into their performance in next-generation nuclear reactors. Materials science ; Chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Dashuang Wang | Ping Tuo | Zhilan Du | Tianyi Liu | Yuxin Zhang
Source: Biogeotechnics (review, 2025-02-22)
Field: Chemistry — Organic Chemistry

Progress and prospect of biomimetic optical materials: A review Recent advances in bionic optical materials (BOMs) are systematically reviewed, emphasizing bioinspired strategies derived from natural models such as plant epidermis, aquatic organisms, avian plumage, and insect photonic architectures (e.g., butterfly wings and inverse opal structures). These biological systems exhibit sophisticated light manipulation mechanisms, including absorption, scattering, and structural coloration, which have guided the design of novel extinction materials with tunable optical properties. By mimicking hierarchical structures and dynamic light-regulation principles, researchers have developed BOMs exhibiting exceptional broadband extinction performance. Notably, applications in optical sensing and imaging systems are critically discussed, highlighting their role in enhancing camera sensitivity and adaptive optical sensor design. Furthermore, this review identifies emerging trends in nanofabrication, machine learning-assisted optimization, and biohybrid material systems. The integration of cross-disciplinary approaches is projected to accelerate the development of multifunctional BOMs, paving the way for breakthroughs in adaptive optics, environmental monitoring, and intelligent photonic devices. In the future, the integration of BOMs and distributed fiber optic sensing technology is expected to realize the whole-life optical monitoring of pile foundation structural health, and promote the development of geotechnical engineering in the direction of intelligence and high precision. Nanotechnology ; Materials science ; Engineering ethics ; Engineering physics ; Engineering Polydiacetylene-based materials and applications ; Perovskite Materials and Applications ; Nanoplatforms for cancer theranostics
Authors: Zhepeng Zhang | Shanshan Xue | Ruiyun Zhou | Chen Wang | Heera Jayan | Jiangbo Li | Xiaobo Zou | Xiaonan Lu | Zhiming Guo
Source: Advanced Functional Materials (article, 2025-09-30)
Field: Materials Science — Biomaterials

Advanced Applications of Responsive Nanomaterials in Intelligent Food Packaging Abstract To meet the growing for convenience markets, intelligent packaging materials have become integral to innovative packaging systems distinguished by their embedded visualization and digitization capabilities. However, the widespread adoption of such systems is hindered by the inherent limitations of conventional indicator materials, particularly their low resistance to moisture, heat, and light. Recent advances in nanoscience have positioned multifunctional responsive nanomaterials as highly promising candidates for intelligent packaging applications owing to their superior sensitivity, selectivity, recyclability, and resistance to migration. The incorporation of nanomaterials is shown to significantly improve the accuracy and sensitivity of monitoring platforms, while enabling the replacement of traditional water‐ or alcohol‐soluble pigments to prevent indicator leakage. This review begins by outlining the mechanisms of food spoilage, followed by an in‐depth discussion of the fundamental principles behind intelligent packaging indicators, including colorimetric, time‐temperature, humidity, and biosensor‐based systems. It further presents a comprehensive overview of the fabrication strategies, sensing mechanisms, and practical applications of responsive nanomaterials in intelligent food packaging. Lastly, future directions are highlighted, including the development of novel composite materials for multifaceted analysis, the shift toward sustainable and low‐toxicity nanomaterials, and the integration of advanced analytics with machine learning for enhanced monitoring interfaces. Active packaging ; Food packaging ; Nanomaterials ; Digitization ; Visualization Nanocomposite Films for Food Packaging ; Polydiacetylene-based materials and applications ; Advanced Sensor and Energy Harvesting Materials