jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kahkashan Rashid | Umme Omeema | A. Raza | Adeel Manzoor | Muhammad Abbas | Ghulam Mustafa | Muhammad Adnan Iqbal | Ghazala Iram | Mudassar Akram | Ateeq-ur Rehman
Source: Reviews in Inorganic Chemistry (article, 2025-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Role of metal-organic frameworks (MOFs) in electrochemical energy storage devices including batteries and supercapacitors Abstract Better energy storage systems are becoming more and more in demand as electric cars, portable electronics, and renewable energy sources become more prevalent. Supercapacitors and batteries, such as lithium-ion batteries (LIBs), sodium-ion batteries (NIBs), lithium sulfur/air batteries, and lithium selenium batteries, are major components of these technologies; yet, stability, cycle life, and energy density are some of the challenges they face. MOFs have emerged as a new material that can solve the problems with unique structural properties. Large surface area and porosity are properties of MOFs which improve the energy density, life cycle and stability of energy storage devices when MOF are used as alectrode material in these devices.This paper analyzes and focuses on the application of MOFs in supercapacitors, LIBs, and SIBs, in which it emphasizes improvement in terms of stability and performance. This review article ends with an overview of the important challenges and the prospects for future research to fully meet the promise of Metal organic frameworks in energy storage applications. Supercapacitor ; Chemistry ; Electrochemical energy storage ; Energy storage ; Metal-organic framework ; Electrochemistry ; Nanotechnology ; Electrochemical energy conversion ; Organic chemistry ; Electrode ; Materials science Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Yuan La | Zengchao Wang | Junlin Zhu | Yao Yuan | Dexuan Cai | Fanghui Yin | Ximing Gao | Liming Wang
Source: Journal of Composites Science (article, 2025-01-04)
Field: Materials Science — Polymers and Plastics

Preparation and Performance Optimization of a Two-Component Microcapsule Self-Healing Fluoro Silicone Rubber In recent years, transformer failures caused by the aging of bushing sealing materials have become increasingly common in power systems, posing significant risks to the safe and stable operation of transformers. Microencapsulated self-healing technology offers a promising solution by repairing microcracks and extending the service life of rubber sealing materials. This study developed polyurea-formaldehyde microcapsules encapsulating polydimethylsiloxane (PDMS) self-healing agents based on a Pt/PDMS curing system. A two-component microencapsulated self-healing system was further established for fluoro-silicone rubber materials. The feasibility of self-healing in these composite materials was validated through tensile property tests. Additionally, the effects of varying microcapsule contents and two-component ratios on material performance were systematically investigated, leading to the optimization of the mechanical and self-healing properties of the microcapsule/fluoro-silicone rubber composites. The results demonstrated that using SYLGARD™ 184 as the healing agent, with a two-component microcapsule ratio of 6:4 (A:B) and a microcapsule content of 10 phr, yielded a remarkable self-healing performance, achieving a healing efficiency of approximately 63%. The results of this paper can provide reference for the development of long-life rubber sealing materials. Materials science ; Self-healing ; Silicone rubber ; Composite material ; Polydimethylsiloxane ; Natural rubber ; Curing (chemistry) ; Bushing ; Ultimate tensile strength ; Composite number Polymer composites and self-healing ; Lignin and Wood Chemistry ; Corrosion Behavior and Inhibition
Authors: O. O. Oluwole | Petar Jovanović | Shadhon Chandra Mohonta | Tuan Kien Nguyen | Phillip Aitchison | Nemai Chandra Karmakar | Mainak Majumder
Source: npj 2D Materials and Applications (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Low-temperature graphitization by amine-assisted combustion of graphite oxide Scalable production of bulk graphene materials, by deoxygenation and partial graphitization of graphite oxide (GO), is considered the most mature pathway towards graphene-fortified products. We reimagined a combustion process in which a mixture of GO (the oxidant) and amino acid (high-energy fuel and reductant) undergoes solid-state reactions at relatively benign conditions (nominal temperature ~900 °C) to produce combusted reduced-graphite oxide (C-rGO) powders. The product is strewn with ordered and long graphene crystallites ( > 80 nm long), exhibits heavily graphitized (002) reflections in XRD and sharp selected area diffraction patterns, with a high C/O ratio ( > 18), and significant nitrogen doping (2.93 at%). We demonstrated that these inks can be printed into patterns on plastic substrates such as PET and roll-compressed to produce highly conductive thin films. Prototype chipless RFID tags were fabricated with a resonant microwave frequency of 3.986 GHz, capable of wireless reading from a distance of 28 mm. Combustion ; Amine gas treating ; Graphite ; Materials science ; Oxide ; Chemical engineering ; Graphite oxide ; Chemistry ; Organic chemistry ; Metallurgy Graphene research and applications ; Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites
Authors: Florence Acha | Daniel Egbebunmi | Shamsudeen Ahmadu | Aishat Ojuolape | Titus Chinedu Egbosiuba
Source: Physchem (article, 2025-11-04)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Bio-Inspired Superhydrophobic Coatings Utilizing Hierarchical Nanostructures for Self-Cleaning and Anti-Icing Surfaces Bio-inspired superhydrophobic coatings have garnered significant attention in recent years due to their potential in creating self-cleaning and anti-icing surfaces. Drawing inspiration from natural systems such as lotus leaves and insect wings, these coatings leverage hierarchical nanostructures to achieve extreme water repellency and low surface adhesion. This review explores recent advances in the design, fabrication, and functional performance of bio-inspired superhydrophobic materials, with a focus on hierarchical micro/nanostructured surfaces. We discuss the underlying mechanisms of wettability, the role of surface chemistry, and the integration of durable nanostructures for enhanced durability. Additionally, the paper discusses the latest progress in scalable manufacturing techniques, environmental adaptability, and multifunctional performance, particularly in self-cleaning and anti-icing applications. Emerging trends, such as stimuli-responsive surfaces and smart coatings, are also examined to provide a comprehensive overview of the field. This review discusses the challenges and future directions for translating laboratory-scale innovations into real-world applications, particularly in aerospace, automotive, energy, and infrastructure sectors. Leverage (statistics) ; Lotus effect ; Nanostructure ; Scalability ; Biomimetics Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Corrosion Behavior and Inhibition
Authors: Isaac W. Beaglehole | Miles J. Pemberton | Elliot H. E. Farrar | Matthew N. Grayson
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Machine Learning Transition State Geometries and Applications in Reaction Property Prediction ABSTRACT The calculation of transition state (TS) geometries is essential for understanding reaction mechanisms and rational synthetic methodology design. However, traditional methods like density functional theory are often too computationally expensive for large‐scale TS identification and are significantly slower than high‐throughput experimental screening methods. Recent advancements in machine learning (ML) offer promising alternatives, enabling the direct prediction of TS geometries, reducing the reliance on expensive quantum mechanical (QM) calculations, and affording predictions ahead of experiments. The works explored here include the broader application of ML in reaction property prediction, emphasizing how accurate TS geometries can serve as vital input data to improve model accuracy. A comprehensive review of ML methods developed to explicitly predict TS geometries is then presented, with attention to their application in downstream tasks, such as energy barrier calculations, and their use as initial structures for further optimization via QM methods. Finally, a critical evaluation of the accuracy and limitations of existing TS prediction methods is discussed, highlighting challenges that impede wider adoption and areas where further research is needed. Property (philosophy) ; State (computer science) ; Transition (genetics) ; Computer science ; Statistical physics ; Chemical physics ; Computational chemistry ; Materials science ; Physics ; Chemistry ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Jan-Lucas Uslu | A. N. Nekrasov | Alexander Hermans | Bernd Beschoten | Bastian Leibe | Lutz Waldecker | Christoph Stampfer
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MaskTerial: a foundation model for automated 2D material flake detection The detection and classification of exfoliated two-dimensional (2D) material flakes from optical microscope images can be automated using computer vision algorithms. This has the potential to increase the accuracy and objectivity of classification and the efficiency of sample fabrication, and it allows for large-scale data collection. Existing algorithms often exhibit challenges in identifying low-contrast materials and typically require large amounts of training data. Here, we present a deep learning model, called MaskTerial, that uses an instance segmentation network to reliably identify 2D material flakes. The model is extensively pre-trained using a synthetic data generator that generates realistic microscopy images from unlabeled data. This results in a model that can quickly adapt to new materials with as little as 5 to 10 images. Furthermore, an uncertainty estimation model is used to finally classify the predictions based on optical contrast. We evaluate our method on eight different datasets comprising five different 2D materials and demonstrate significant improvements over existing techniques in the detection of low-contrast materials such as hexagonal boron nitride. Segmentation ; Deep learning ; Image segmentation ; Pattern recognition (psychology) ; Image processing ; Automation ; Object detection ; Microscope ; Sample (material) Machine Learning in Materials Science ; Graphene research and applications ; Surface Roughness and Optical Measurements
Authors: Sanchit Kumar | Chaitali Mondal | Amisha Raj Singh | Ankurava Sinha
Source: Indian Journal of Pure & Applied Physics (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Structural Modification of Sol-Gel derived Nickel Ferrite and its derivatives via Rare Earth Doping and Its Impact on Electrical Properties Nickel ferrite (NiFe₂O₄), known for its exceptional magnetic behaviour and high electrical resistance, is commonly used in magnetic sensors, transformers, and high-frequency electronics. However, its inherent electrical qualities frequently require improvement for application in more demanding technological situations. This paper examines the structural and electrical evolution of nickel ferrite generated in sol-gel using rare earth (RE) ions such Gd³⁺, La³⁺, Nd³⁺, Sm³⁺, and Ho³⁺. Because of their greater ionic sizes and different electronic structures, these dopants cause significant lattice distortions, change cation site distributions, and, at higher concentrations, generate secondary non-spinel phases. These alterations greatly impact electron transport by affecting the Fe²⁺/Fe³⁺ hopping process. This allows for manipulation of resistivity and dielectric characteristics. The article also examines how critical synthesis parameters, such as solution pH and combustion agent selection, affect phase formation and microstructural uniformity. RE replacement improves the dielectric constant, loss characteristics, and electrical conductivity of these modified ferrites, highlighting their potential in applications such as electromagnetic interference (EMI) shielding, energy storage systems, and downsized electronic components. The research continues by discussing optimization tactics for dopant inclusion and synthesis conditions in order to modify functional features of Nickel ferrite family for future device integration. Doping ; Rare earth ; Sol-gel ; Ferrite (magnet) ; Materials science ; Nickel ; Metallurgy ; Composite material ; Nanotechnology ; Optoelectronics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials
Authors: Bin Cao | Zinan Zheng | Liu Yang | Longhan Zhang | Lawrence W.‐Y. Wong | Lu‐Tao Weng | Jia Li | Haoxiang Li | Tong‐Yi Zhang
Source: National Science Review (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

XQueryer: an intelligent crystal structure identifier for powder X-ray diffraction Powder X-ray diffraction (PXRD) is a widely used technique for characterizing crystal structures. With the rise of artificial intelligence (AI) in science, the rapid expansion of AI laboratories and high-throughput characterization methods has created a growing demand for intelligent PXRD pattern analysis-that is, automated crystal structure identification in integrated AI-driven facilities. Such identifiers extract structural information directly from PXRD patterns and transmit it as accurate and efficient data streams to other intelligent components involved in material synthesis, characterization and optimization. In this work, we present XQueryer, an intelligent PXRD-based structure identifier. To support its development, we constructed a comprehensive simulation database using a newly designed high-fidelity simulation method. This database contains over two million PXRD patterns simulated from 100 315 crystal structures in the Materials Project, covering diverse intrinsic sample features and a range of extrinsic diffractometer conditions. XQueryer was evaluated on more than 200 000 simulated patterns and 1003 experimental PXRD patterns. It outperformed existing models and the traditional search-match method, achieving a 28.9% accuracy improvement compared with the second-best model. Furthermore, XQueryer has been integrated with a powder X-ray diffractometer, enabling real-time determination of crystal structures in tested samples. Identifier ; Powder diffraction ; Diffractometer ; Diffraction ; Powder Diffractometer ; Characterization (materials science) ; Crystal structure X-ray Diffraction in Crystallography
Authors: Mingyu Ma | Yuqing Wang | Yanting Liu | Shasha Guo | Zheng Liu
Source: Nano Convergence (review, 2025-04-18)
Field: Materials Science — Materials Chemistry

Guided electrocatalyst design through in-situ techniques and data mining approaches Intuitive design strategies, primarily based on literature research and trial-and-error efforts, have significantly contributed to advancements in the electrocatalyst field. However, the inherently time-consuming and inconsistent nature of these methods presents substantial challenges in accelerating the discovery of high-performance electrocatalysts. To this end, guided design approaches, including in-situ experimental techniques and data mining, have emerged as powerful catalyst design and optimization tools. The former offers valuable insights into the reaction mechanisms, while the latter identifies patterns within large catalyst databases. In this review, we first present the examples using in-situ experimental techniques, emphasizing a detailed analysis of their strengths and limitations. Then, we explore advancements in data-mining-driven catalyst development, highlighting how data-driven approaches complement experimental methods to accelerate the discovery and optimization of high-performance catalysts. Finally, we discuss the current challenges and possible solutions for guided catalyst design. This review aims to provide a comprehensive understanding of current methodologies and inspire future innovations in electrocatalytic research. Computer science ; Electrocatalyst ; Data science ; Field (mathematics) ; Experimental data ; Biochemical engineering ; Complement (music) ; Risk analysis (engineering) ; Nanotechnology ; Management science ; Materials science ; Engineering ; Chemistry ; Medicine Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Juntao Zhang | Chenhui He | Yujing Ji | Zhimeng Liu | Hongyi Gao
Source: Carbon Neutralization (article, 2025-12-29)
Field: Chemistry — Inorganic Chemistry

An Explainable Stacked Machine Learning Approach for Toluene Capture in Metal‐Organic Frameworks: From Predictive Modeling to Interactive Web Platform ABSTRACT Metal‐organic frameworks (MOFs) exhibit significant potential for the adsorption of volatile organic compounds (VOCs) due to their tunable pore structures and high specific surface areas. However, identifying high‐performing MOFs within the vast structural space remains challenging, primarily due to unclear structure–performance relationships. Moreover, existing studies often overlook realistic adsorption scenarios that involve coexisting atmospheric components such as O 2 , N 2 , and water vapor, and rarely address capacity–selectivity trade‐offs or conducted systematic comparisons of model performance. Herein, we developed a data‐driven machine learning framework integrating multi‐model comparisons, stacking ensemble modeling, and interpretability analyses for predicting the adsorption performance of MOFs for airborne toluene with high accuracy. The stacking model, comprising eight complementary base models and a multilayer perceptron (MLP) as the meta‐learner, demonstrated an enhanced capability to capture complex nonlinear relationships between descriptors and performance, achieving superior predictive accuracy ( R 2 = 0.922, RMSE = 0.186) compared to the best‐performing individual model, CatBoost ( R 2 = 0.890, RMSE = 0.326). Furthermore, by incorporating SHAP, PDP, and feature interaction analyses, this study elucidated the synergistic regulatory mechanisms associated with key structural descriptors. Statistical analysis further revealed that the structural parameters of high‐performing MOFs exhibited significant convergence, with metal centers such as Cu and their open metal sites (OMS) quantitatively identified as critical performance‐enhancing factors. Finally, the stacking model was successfully deployed as an interactive web platform that enables real‐time prediction and visual interpretability of MOF performance, serving as a practical tool for the efficient screening of MOF candidates for airborne toluene adsorption. Interpretability ; Stacking ; Feature (linguistics) ; Multilayer perceptron ; Toluene ; Adsorption ; Key (lock) ; Perceptron Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Kai Liu | Zixiong Wei | Wei Gao | Poulumi Dey | Marcel H. F. Sluiter | Fei Shuang
Source: npj Computational Materials (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Heterogeneous ensemble enables a universal uncertainty metric for atomistic foundation models Abstract Universal machine-learning interatomic potentials (uMLIPs) are emerging as foundation models for atomistic simulation, offering near-ab initio accuracy at far lower cost. Their safe, broad deployment is limited by the absence of reliable, general uncertainty estimates. We present a unified, scalable uncertainty metric, U , built from a heterogeneous ensemble that reuses existing pretrained MLIPs. Across diverse chemistries and structures, U strongly tracks true prediction errors and robustly ranks configuration-level risk. Using U , we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels: for tungsten, we match full density-functional-theory (DFT) training using 4% of the DFT data; for MoNbTaW, a dataset distilled by U supports high-accuracy potential training. By filtering numerical label noise, the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data. This framework provides a practical reliability monitor and guides data selection and fine-tuning, enabling cost-efficient, accurate, and safer deployment of foundation models. Metric (unit) ; Uncertainty quantification ; Foundation (evidence) ; Scalability ; Software deployment ; Reliability (semiconductor) ; SAFER ; Selection (genetic algorithm) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications
Authors: Christophe Bajan | G. Lambard
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring the expertise of large language models in materials science and metallurgical engineering This study establishes a performance baseline for 15 LLMs on materials science tasks using the MaScQA benchmark, paving the way for AI-driven advancements in materials discovery and analysis. Metallurgy ; Science and engineering ; Engineering ; Computer science ; Materials science ; Engineering ethics Machine Learning in Materials Science
Authors: Song Xiao | Haoran Xia | Zhanyuan Li | Yifan Wang | Ju Tang | Xiaoxing Zhang | Yi Li
Source: High Voltage (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Enhancing Corrosion Resistance of Copper for C <sub>4</sub> F <sub>7</sub> N Based Eco‐Friendly Gas‐Insulated Equipment ABSTRACT Perfluoroisobutyronitrile (C 4 F 7 N) is recognised as the next‐generation dielectric medium for eco‐friendly gas‐insulated equipment (GIE). However, there is gas metal incompatibility in strong electromagnetic environments and environmentally friendly insulating gas atmospheres that endangers the operational safety and service life of GIE. Here, a solvent‐induced grain boundary reconstruction method was proposed to prepare corrosion‐resistant copper rapidly. Accordingly, the partial discharge‐induced gas–metal interaction was simulated and the corrosion resistance properties of copper were evaluated. Additionally, the gas–solid interaction mechanism was investigated and the improvement mechanism of the proposed method on the stability of the gas–solid interface was discussed. Related results provide a reference for the development and application reliability of C 4 F 7 N‐based eco‐friendly GIE. Corrosion ; Copper ; Metallurgy ; Materials science ; Environmentally friendly Corrosion Behavior and Inhibition ; Recycling and Waste Management Techniques ; Additive Manufacturing and 3D Printing Technologies