jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kurnaz, Salih Can
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Prediction of Ambient-Temperature Superconductivity in Entropy-Stabilized Perovskite Hydrides: A Computational Approach via Meta-Wall Theory The discovery of high-temperature superconductivity in compressed hydrides (e.g., LaH10 at 250 K under 170 GPa) has renewed interest in room-temperature superconductors, yet the requirement of extreme pressures remains a fundamental obstacle. Here, we present a computational framework based on post-Migdal-Eliashberg theory to identify ambient-pressure superconductors by targeting the "Meta-Wall" regime. Through systematic screening, we identify Mg0.7Ca0.3PdH3 as a promising candidate exhibiting metallic character, thermodynamic stability at 1 atm, and a predicted critical temperature Tc ≈ 295 K. The material employs entropy stabilization via Ca doping to suppress phase separation. We propose a synthesis protocol incorporating mechanical alloying and high-pressure hydrogenation with kinetic trapping. This work demonstrates the viability of AI-driven computational materials design in circumventing the pressure barrier. Superconductivity ; Perovskite (structure) ; Work (physics) ; Density functional theory ; Entropy (arrow of time) ; Doping ; Phase (matter) ; Kinetic energy Hydrogen Storage and Materials ; Inorganic Chemistry and Materials ; High-pressure geophysics and materials
Authors: Himendra Perera (16642179) | Vahid Rahmanian (4507105) | Mariam Sohail (10840066) | Sahel Mohammadkhah (12968568) | Amit Ahuja (8214430) | Saad A. Khan (1404925)
Source: Figshare (article, 2025-08-13)
Field: Materials Science — Polymers and Plastics

Using SupercriticalCO<sub>2</sub> for Viscosity Reductionof Polymers: A Data-Driven Modeling Approach for Predicting the DiffusiveProperties of Polystyrene Melt This study investigates the influence of supercritical CO<sub>2</sub> on the rheological properties of polystyrene (PS) through a comprehensive rheological analysis. By generating time-dependent viscosity curves and the corresponding torque profiles, we accurately assess the progressive plasticization effects of CO<sub>2</sub>. Results show that CO<sub>2</sub> can significantly lower the viscosity of PS, achieving up to a 96% reduction when transitioning from atmospheric to subcritical conditions and an additional 56% decrease when moving from subcritical to supercritical states. These findings highlight the effectiveness of supercritical CO<sub>2</sub> in enhancing the processability of polymers through viscosity reduction. Furthermore, we develop a universal viscosity model that incorporates temperature, shear rate, and CO<sub>2</sub> concentration effects for PS-CO<sub>2</sub> melts. This model in combination with CO<sub>2</sub> solubility models offers a robust framework for accurately predicting CO<sub>2</sub> diffusion coefficients. This methodology not only deepens our understanding of CO<sub>2</sub>–PS interactions but also provides a reliable tool for future research and industrial applications, presenting a more efficient and environmentally friendly alternative to traditional methods. Supercritical fluid ; Rheology ; Polystyrene ; Viscosity ; Polymer ; Plasticizer ; Diffusion ; Rheometry ; Solubility Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Injection Molding Process and Properties
Authors: Kurnaz, Salih Can
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

Prediction of Ambient-Temperature Superconductivity in Entropy-Stabilized Perovskite Hydrides: A Computational Approach via Meta-Wall Theory The discovery of high-temperature superconductivity in compressed hydrides (e.g., LaH10 at 250 K under 170 GPa) has renewed interest in room-temperature superconductors, yet the requirement of extreme pressures remains a fundamental obstacle. Here, we present a computational framework based on post-Migdal-Eliashberg theory to identify ambient-pressure superconductors by targeting the "Meta-Wall" regime. Through systematic screening, we identify Mg0.7Ca0.3PdH3 as a promising candidate exhibiting metallic character, thermodynamic stability at 1 atm, and a predicted critical temperature Tc ≈ 295 K. The material employs entropy stabilization via Ca doping to suppress phase separation. We propose a synthesis protocol incorporating mechanical alloying and high-pressure hydrogenation with kinetic trapping. This work demonstrates the viability of AI-driven computational materials design in circumventing the pressure barrier. Superconductivity ; Perovskite (structure) ; Work (physics) ; Density functional theory ; Entropy (arrow of time) ; Doping ; Phase (matter) ; Kinetic energy Hydrogen Storage and Materials ; Inorganic Chemistry and Materials ; High-pressure geophysics and materials
Authors: Oleg V. Loginovsky | Alexander Beliakov | Elena A. Zvereva
Source:  (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Digital Twin-Based Module for the Analysis of the Mechanical Response of Crystalline Materials This paper presents the development of a digital module designed for the storage and analysis of mechanical response characteristics in crystalline materials. The module processes the stiffness tensor obtained from structural models derived through quantum-chemical methods, incorporating periodic boundary conditions. It includes comprehensive data on the mechanical properties of organic crystalline compounds, highlighting significant features such as elastic moduli, directions of maximum and minimum compressibility, and negative linear compressibility. The collected data undergoes thorough processing and analysis, focusing on the distribution of critical elastic modulus values. This dataset is prepared for future processing using artificial intelligence techniques, enabling the construction of predictive models and enhancing the exploration of “structure-property” relationships. Additionally, an algorithm for visualizing spatial dependencies of Young's modulus, linear compressibility, linear stiffness, and uniaxial compressibility has been implemented, allowing for adjustable resolution. This visualization is integrated with atomic-molecular systems, including periodic structures, to elucidate the relationship between mechanical response and crystal structure. A user-friendly interface has also been developed for loading and storing raw data, facilitating the analysis of mechanical response characteristics derived from quantum-chemical calculations. Configuration files for the web server container have been created to support continuous software integration and deployment on new platforms. This innovative digital module represents a significant advancement in the field of materials science, contributing to improved understanding and optimization of crystalline materials for various applications. Response analysis ; Computer science ; Materials science ; Structural engineering ; Engineering Titanium Alloys Microstructure and Properties ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Engineering Technology and Methodologies
Authors: YoungDo Kweon | SangHyuk Kim | MinJae Kong
Source: IMAPSource Proceedings (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Large Body Lidded FCBGA Thermal Performance Study with Indium-Silver Alloy TIM The power consumption required for semiconductors has increased to hundreds of watts due to the growing artificial intelligence (AI) and high-performance computing (HPC) industries. High temperatures resulting from high-power consumption can reduce the electrical performance of integrated circuit (IC) chips, therefore, the effective heat dissipation of IC packages is essential to enhance their performance and extend their life. The thermal interface material (TIM) is one of the critical key factors to thermally improved packaging. Many TIMs, such as polymer, graphite and metal TIMs, have been developed to provide the best thermal performance for packages. Among the various TIMs, indium-silver (InAg) alloy TIMs showed superior thermal performance because of their higher thermal bulk conductivity and much lower interfacial thermal resistance. However, it is also important to maintain the excellent performance even after board level long-term reliability tests. Therefore, the thermal performance of InAg alloy TIMs is described with two long-term reliability tests: 1) 2,000 cycles temperature cycling (TC) test at Condition K and 2) 2,000 hours high temperature storage (HTS) test at 135°C. Junction-to-case thermal resistance (ӨJC) measurements of 60 x 60-mm body lidded flip chip ball grid array (FCBGA) packages were performed with a water-cooled, cold plate cooling system. The 60 x 60-mm body lidded FCBGA thermal test vehicle (TTV) has a 25.6 x 25.6-mm die divided into 36 unit-cells with the temperature of each cell measured individually. The indium-silver alloy TIM thermal performance was quite different from polymer or graphite TIMs. The difference of the heat flow mechanism from the die to lid of both HTS and TC test conditions is described using three-dimensional (3D) thermal simulation to further understand the thermal characteristics of the metal TIM. Ball grid array ; Thermal grease ; Thermal conductivity ; Temperature cycling ; Die (integrated circuit) ; Graphite ; Thermal resistance ; Alloy ; Thermal Thermal properties of materials ; Electronic Packaging and Soldering Technologies ; Silicon Carbide Semiconductor Technologies
Authors: Divya Bajpai Tripathy | Subhalaxmi Pradhanand Pooja Agarwal | Pooja Agarwal
Source: Journal of environmental chemical engineering (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Covalent Organic Framework (COF) composite membranes in water remediation and desalination Nanofiltration ; Desalination ; Membrane ; Reverse osmosis ; Fouling ; Leaching (pedology) ; Water treatment ; Covalent organic framework ; Portable water purification Covalent Organic Framework Applications ; Membrane Separation Technologies ; Environmental remediation with nanomaterials
Source: Journal of Physics Conference Series (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Preface The 2025 2nd International Conference on Materials Physics and Composites (ICMPC 2025) was successfully held from June 27 to 29, 2025, in Kunming, China. This academic gathering served as a dynamic platform for the exchange of cutting-edge research and advancements in the fields of materials physics and composite materials. Drawing over 100 participants from universities, research institutes, and industry, the conference facilitated the dissemination of new theoretical insights, experimental findings, and engineering applications. A total of 156 submissions were received, and after a rigorous peer-review process, 86 high-quality papers were accepted for presentation and publication. The conference featured four keynote speeches delivered by internationally renowned scholars, each highlighting emerging trends and fundamental challenges in their respective areas of expertise. Prof. Xiaoyu Chong of Kunming University of Science and Technology introduced a novel paradigm for noble metal alloy research through the integration of computation, data, and artificial intelligence. Prof. Zhihui Zeng from Shandong University presented advancements in lightweight nanocomposites for electromagnetic wave shielding and absorption. Prof. Lujun Pan of Dalian University of Technology offered insights into carbon nanocoils and their functional applications in microwave absorption technologies. Finally, Prof. Osman Adiguzel of Firat University provided an in-depth analysis of twinning and detwinning mechanisms in martensitic transformations relevant to shape memory alloys. In addition to these plenary sessions, the conference also hosted several oral presentations, which enriched the discourse by covering a wide range of topics and enabling extensive academic interaction. The scope of ICMPC 2025 encompassed a broad spectrum of contemporary issues and innovations in both fundamental and applied materials science. Topics of discussion ranged from material thermodynamics, atomic and solid-state physics, and quantum mechanics, to the mechanical, optoelectronic, and microelectronic properties of emerging materials. Special emphasis was placed on semiconductor and superconducting materials, nanomaterials, and technologies relevant to material processing and surface/interface engineering. In the realm of composites, research on advanced functional materials, polymer-based nanocomposites, porous structures, energy storage and catalytic materials, as well as the development of new-generation solar cells and refractory systems featured prominently in the conference program. List of Committee Member is available in this PDF. Presentation (obstetrics) ; Scope (computer science) ; Research centre ; Chemical technology ; Emerging technologies Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Material Selection and Properties
Authors: Sunbin Hwang | Mutsunori Uenuma | Junya Kurumida
Source: Advanced Electronic Materials (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

The Platform to Characterizing the Thermoelectric Materials by Photo‐Crosslinked Ionic Gel Electrolyte Abstract Advances in semiconductor processing have enabled the miniaturization and reduction of power consumption of devices, positioning organic thermoelectric devices—which generate electricity from any heat source—as a promising solution for self‐powered microelectronics. To make off‐grid electronics more practical, innovative thermoelectric materials with high performance need to be developed, leveraging artificial intelligence and materials informatics. This study introduces a device platform featuring a photo‐crosslinked ionic gel capable of evaluating and screening a wide range of thermoelectric material candidates with high potential. By enabling doping levels from shallow to deep, the platform provides rapid feedback for researchers. Ionic liquids, with their extensive range of oxidation and reduction capabilities, minimize the effort required to identify dopants compatible with semiconductors. During redox reactions within the ionic liquid, the platform allows continuous monitoring of changes in thermoelectric properties, optical characteristics, and crystalline structure—all within a single device. Furthermore, adjusting the ionic liquid concentration in the gel stabilizes the redox state of thermoelectric materials. The thermoelectric properties measured using this platform closely align with those achieved via chemical doping, demonstrating its potential to significantly reduce fabrication and measurement costs and time. This approach accelerates the discovery of high‐performance organic thermoelectric materials through materials informatics. Materials science ; Electrolyte ; Ionic bonding ; Thermoelectric effect ; Chemical engineering ; Nanotechnology ; Thermoelectric materials ; Composite material ; Ion ; Electrode ; Organic chemistry ; Physical chemistry Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials
Authors: Jinbin Hu | Mengfan Wu | Jie Ren
Source: Physical Review Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Machine learning for the generative discovery of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi mathvariant="normal">K</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup></mml:math>-selective porous structures with aluminum sites Zeolites, renowned for their unique porous structures, are widely used in applications like gas separation and ion-exchange. However, discovering zeolite-like materials with high potassium ion (K⁺) selectivity has traditionally been time-consuming and expensive. In this study, the authors propose a data-driven paradigm combining Artificial Intelligence and Density Functional Theory to efficiently identify new zeolite-like structures with enhanced K⁺ selectivity. Their framework identifies a novel porous material with the highest K⁺ adsorption capacity to date. This approach paves the way for more efficient materials discovery and the development of advanced ion-selective membranes and energy storage materials. Materials science ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis
Authors: Amgad Ahmed Ali Ibrahim | Hein Htet | Ryoji Asahi
Source: ArXiv.org (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Hierarchical Stacking Optimization Using Dirichlet's Process (SoDip): Towards Accelerated Design for Graft Polymerization Radiation-induced grafting (RIG) enables precise functionalization of polymer films for ion-exchange membranes, CO2-separation membranes, and battery electrolytes by generating radicals on robust substrates to graft desired monomers. However, reproducibility remains limited due to unreported variability in base-film morphology (crystallinity, grain orientation, free volume), which governs monomer diffusion, radical distribution, and the Trommsdorff effect, leading to spatial graft gradients and performance inconsistencies. We present a hierarchical stacking optimization framework with a Dirichlet's Process (SoDip), a hierarchical data-driven framework integrating: (1) a decoder-only Transformer (DeepSeek-R1) to encode textual process descriptors (irradiation source, grafting type, substrate manufacturer); (2) TabNet and XGBoost for modelling multimodal feature interactions; (3) Gaussian Process Regression (GPR) with Dirichlet Process Mixture Models (DPMM) for uncertainty quantification and heteroscedasticity; and (4) Bayesian Optimization for efficient exploration of high-dimensional synthesis space. A diverse dataset was curated using ChemDataExtractor 2.0 and WebPlotDigitizer, incorporating numerical and textual variables across hundreds of RIG studies. In cross-validation, SoDip achieved ~33% improvement over GPR while providing calibrated confidence intervals that identify low-reproducibility regimes. Its stacked architecture integrates sparse textual and numerical inputs of varying quality, outperforming prior models and establishing a foundation for reproducible, morphology-aware design in graft polymerization research. Dirichlet process ; Stacking ; Bayesian optimization ; Gaussian process ; Grafting ; Kriging ; Process (computing) ; Process modeling ; Soft sensor ; Bayesian probability Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Polymer Synthesis and Characterization
Authors: Amgad Ahmed Ali Ibrahim | Hein Htet | Ryoji Asahi
Source: arXiv (Cornell University) (preprint, 2025-12-25)
Field: Materials Science — Materials Chemistry

Hierarchical Stacking Optimization Using Dirichlet's Process (SoDip): Towards Accelerated Design for Graft Polymerization Radiation-induced grafting (RIG) enables precise functionalization of polymer films for ion-exchange membranes, CO2-separation membranes, and battery electrolytes by generating radicals on robust substrates to graft desired monomers. However, reproducibility remains limited due to unreported variability in base-film morphology (crystallinity, grain orientation, free volume), which governs monomer diffusion, radical distribution, and the Trommsdorff effect, leading to spatial graft gradients and performance inconsistencies. We present a hierarchical stacking optimization framework with a Dirichlet's Process (SoDip), a hierarchical data-driven framework integrating: (1) a decoder-only Transformer (DeepSeek-R1) to encode textual process descriptors (irradiation source, grafting type, substrate manufacturer); (2) TabNet and XGBoost for modelling multimodal feature interactions; (3) Gaussian Process Regression (GPR) with Dirichlet Process Mixture Models (DPMM) for uncertainty quantification and heteroscedasticity; and (4) Bayesian Optimization for efficient exploration of high-dimensional synthesis space. A diverse dataset was curated using ChemDataExtractor 2.0 and WebPlotDigitizer, incorporating numerical and textual variables across hundreds of RIG studies. In cross-validation, SoDip achieved ~33% improvement over GPR while providing calibrated confidence intervals that identify low-reproducibility regimes. Its stacked architecture integrates sparse textual and numerical inputs of varying quality, outperforming prior models and establishing a foundation for reproducible, morphology-aware design in graft polymerization research. Dirichlet process ; Stacking ; Bayesian optimization ; Gaussian process ; Grafting ; Kriging ; Process (computing) ; Process modeling ; Soft sensor ; Bayesian probability Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Polymer Synthesis and Characterization
Authors: Ziyue Jiang (14919798) | Yongtao Li (371642) | Ziyu Tang (6086036) | Dingkun Yuan (10168640) | Fawei Lin (8115413)
Source: Figshare (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Strong Metal–SupportInteractions in CatalyticOxidation of VOCs: Mechanistic Insights, Support Engineering Strategies,and Emerging Catalyst Design Paradigms Volatile organic compounds (VOCs) significantly impact air quality as photochemical smog precursors and health hazards. Catalytic oxidation is a leading VOC abatement method but suffers from catalyst deactivation due to metal sintering and competitive adsorption in complex mixtures. Strong metal–support interactions (SMSIs) provide atomic level control of interfacial electronic and geometric structures. SMSI enables bidirectional charge transfer, d band center modulation, oxygen vacancy generation, and tunable encapsulation that together promote O<sub>2</sub> activation, lower barriers, and impart thermal and chemical robustness. This review synthesizes mechanistic insights and recent progress in SMSI-enabled VOC oxidation, integrating in situ and operando probes with kinetics. Reactivity and selectivity across aromatics, alkanes, oxygenates, and chlorinated species are rationalized by SMSI-mediated tuning of adsorption and intermediate evolution. Practical levers include control of particle size and dispersion, core–shell architectures, metal loading, and support acidity or basicity. Emerging directions include single atom catalysts, high entropy alloys, and nonmetal supports. Key challenges concern the dynamic evolution of SMSI under realistic feeds and the scalable, reproducible synthesis of interfaces. Future developments combining in situ characterization with data-driven catalyst design hold promise for achieving durable, high-performance VOC abatement with reduced precious metal usage. Catalysis ; Adsorption ; Selectivity ; Metal ; Heterogeneous catalysis ; Reactivity (psychology) ; Nonmetal Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Mesoporous Materials and Catalysis
Authors: K. B. Raval | Jayanti Makasana | Lalji Baldaniya
Source: Analytical Chemistry Letters (article, 2025-05-04)
Field: Chemistry — Analytical Chemistry

The green analytical chemistry approach coupled with data-driven RP-HPLC method development for simultaneous estimation of Azelnidipine and Chlorthalidone in pharmaceutical tablet dosage form Chemistry ; Chlorthalidone ; Dosage form ; High-performance liquid chromatography ; Chromatography ; Biochemical engineering ; Pharmacology Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies