jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bokai Liu | Pengju Liu | Yizheng Wang | Zhenkun Li | Hongqing Lv | Weizhuo Lu | Thomas Olofsson | Timon Rabczuk
Source: Composite Structures (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification Graphene-based polymer nanocomposites show great potential for thermal management, but accurately predicting their thermal conductivity remains challenging due to multiscale structural complexity and parameter uncertainty. We propose an innovative approach integrating interpretable stochastic machine learning with multiscale analysis to predict the macroscopic thermal conductivity of graphene-based polymer nanocomposites. Our bottom-up framework addresses uncertainties in meso- and macro-scale input parameters. Using Representative Volume Elements (RVEs) and Finite Element Modeling (FEM), we compute effective thermal conductivity through homogenization. Predictive modeling is powered by the XGBoost regression tree-based algorithm. To elucidate the influence of input parameters on predictions, we employ SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing insights into feature interactions and interpretability. Sensitivity analyses further quantify the impact of design parameters on material properties. This integrated method enhances prediction accuracy, reduces computational costs, and bridges data-driven and physical modeling, offering a scalable solution for designing advanced composite materials for thermal management applications. • A multi-scale framework is developed to predict the thermal conductivity of graphene- based polymer nanocomposites, integrating meso- and macro-scale analyses for comprehensive material characterization. • SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) provide actionable insights into the impact of design parameters on material performance through sensitivity analysis. • The Explainable machine learning approach bridges data-driven and physics-based modeling, minimizing reliance on complex analytical simulations in material design. • The proposed methodology significantly enhances predictive accuracy while reducing computational costs compared to traditional stochastic multi-scale modeling techniques. Thermal conductivity ; Nanocomposite ; Uncertainty quantification ; Materials science ; Polymer nanocomposite ; Polymer ; Composite material ; Multiscale modeling ; Thermal ; Conductivity ; Uncertainty analysis ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Thermodynamics ; Simulation ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Probabilistic and Robust Engineering Design
Authors: José D. Gouveia | Tiago L. P. Galvão | Kais Iben Nassar | José R. B. Gomes
Source: npj 2D Materials and Applications (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use. MXenes ; Machine learning ; Artificial intelligence ; Computer science ; Materials science ; Nanotechnology MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Ling Fei | Lei Lei | Hui Xu | Xinghua Guo | Bo Chen | Xu Han | Xun Chen | Qing Huang | Degao Wang
Source: Carbon Energy (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Ion transport behaviors in MXenes for electrochemical energy storage and conversion Abstract MXenes, an innovative class of two‐dimensional (2D) materials composed of transition‐metal carbides and/or nitrides, have garnered significant interest for their potential in energy storage and conversion applications, which is largely attributed to their modifiable surface terminations, exceptional conductivity, and favorable hydrophilic characteristics. MXenes show various ion transport behaviors in applications like electrochemical catalysis, supercapacitors, and batteries, encompassing processes like electrostatic adsorption of surface ions, redox reactions of ions, and interlayer ion shuttle. This review aims to present a summary of advancements in the comprehension of ion transport behaviors of Ti 3 C 2 T x MXenes. First, the composition, properties, and synthesis techniques of MXenes are concisely summarized. Subsequently, the discussion delves into the mechanisms of ion transport in MXenes during CO 2 reduction, water splitting, supercapacitor operation, and battery performance, elucidating the factors determining the electrochemical behaviors and efficacy. Furthermore, a compilation of strategies used to optimize ion transport behaviors in MXenes is presented. The article concludes by presenting the challenges and opportunities for these fields to facilitate the continued progress of MXenes in energy‐related technologies. MXenes ; Energy storage ; Electrochemistry ; Ion ; Electrochemical energy conversion ; Electrochemical energy storage ; Materials science ; Ion transporter ; Environmental science ; Nanotechnology ; Chemistry ; Supercapacitor ; Electrode ; Physics ; Thermodynamics ; Power (physics) MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Energy Harvesting in Wireless Networks
Authors: Reza Khalilzadeh | Milad Babazadeh‐Mamaqani | Moein Mohammadi‐Jorjafki | Hossein Roghani‐Mamaqani | Richard Hoogenboom
Source: Progress in Materials Science (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Advanced anticounterfeiting polymer inks for high-level encryption and authentication technologies Smart polymeric materials have garnered significant attention in the field of anticounterfeiting. As traditional anticounterfeiting inks are frequently copied and counterfeited over time, creating a demand for more advanced anticounterfeiting inks. This review introduces innovative encryption mechanisms and methods for advanced anticounterfeiting inks, including Förster resonance energy transfer, charge and electron transfer , aggregation-induced emission, phase separation, white light emission, double encryption, and unclonable inks. The mechanisms of chromism, luminescence emission, and preparation methods of these inks are discussed, providing insights into their potential applications in security technologies, including inks against counterfeiting and innovative encryption techniques. Polymers have gained significant attention in this field because of their versatility, the ability to integrate diverse functional properties, increasing reusability and lifetime, photostability, photo-fatigue resistant characteristics, and ease of application on various substrates. This review introduces advanced methods and mechanisms, such as dynamic encryption, double encryption, and unclonable anticounterfeiting, for combating forgery and counterfeiting to protect documents and luxury products. This is achieved through polymer-based high-security anticounterfeiting inks. Finally, the application methods of these inks on different substrates is an important challenge, for which printing has gained significant attention because of its simplicity, high rate of production, reproducibility, and precise applicability. Materials science ; Encryption ; Authentication (law) ; Nanotechnology ; Computer security ; Computer science Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Dendrimers and Hyperbranched Polymers
Authors: Alireza Shadab | Simin Farokhi | Arshia Fakouri | Neda Mohagheghzadeh | Ali Akbar Noroozi | Zahra Razavi | Arian Karimi Rouzbahani | Hamidreza Zalpoor | Mohamad Mahjoor
Source: European journal of medical research (review, 2025-02-04)
Field: Materials Science — Biomaterials

Hydrogel-based nanoparticles: revolutionizing brain tumor treatment and paving the way for future innovations Brain tumor treatment remains a significant challenge due to their high mortality and resistance to current therapies. This paper discusses the promising potential of hydrogel-based nanoparticles as innovative drug delivery systems for brain tumor therapy. Extensive characterization techniques reveal the ability of these Nano-systems to demonstrate prolonged blood circulation and targeted delivery, leading to improved survival rates. Designed with optimized physicochemical characteristics, these nanoparticles effectively cross the blood-brain barrier, circumventing a major impediment to drug delivery to the brain. By delivering drugs directly to the tumor bed, these nanoparticles enhance therapeutic outcomes and minimize adverse effects. In addition, this review investigates the techniques for characterizing, visualizing, and modifying these nanoparticles, as well as the standing challenges and promising research avenues for their clinical application. Further investigations are encouraged by this review to investigate potential advancements in hydrogel-based nanoparticle therapeutic approaches for brain tumors. This includes investigating tailored hydrogels, hybrid systems, computational modeling, and the integration of gene therapy and immunotherapy techniques. The study also addresses the need for enhanced synthesis techniques, stability, scalability, and cost-cutting measures to overcome obstacles and advance the clinical use of hydrogel-based nanoparticles in treating brain tumors. Drug delivery ; Brain tumor ; Computer science ; Nanotechnology ; Medicine ; Materials science ; Pathology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Charvi Singhvi | Gunjan Sharma | Rishi Verma | Vinod K. Paidi | Pieter Glatzel | Paul Paciok | Vashishtha B. Patel | Ojus Mohan | Vivek Polshettiwar
Source: Proceedings of the National Academy of Sciences (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Tuning the electronic structure and SMSI by integrating trimetallic sites with defective ceria for the CO <sub>2</sub> reduction reaction Heterogeneous catalysts have emerged as a potential key for closing the carbon cycle by converting carbon dioxide (CO<sub>2</sub>) into value-added chemicals. In this work, we report a highly active and stable ceria (CeO<sub>2</sub>)-based electronically tuned trimetallic catalyst for CO<sub>2</sub> to CO conversion. A unique distribution of electron density between the defective ceria support and the trimetallic nanoparticles (of Ni, Cu, Zn) was established by creating the strong metal support interaction (SMSI) between them. The catalyst showed CO productivity of 49,279 mmol g<sup>-1</sup> h<sup>-1</sup> at 650 °C. CO selectivity up to 99% and excellent stability (rate remained unchanged even after 100 h) stemmed from the synergistic interactions among Ni-Cu-Zn sites and their SMSI with the defective ceria support. High-energy-resolution fluorescence-detection X-ray absorption spectroscopy (HERFD-XAS) confirmed this SMSI, further corroborated by in situ electron energy loss spectroscopy (EELS) and density functional theory (DFT) simulations. The in situ studies (HERFD-XAS & EELS) indicated the key role of oxygen vacancies of defective CeO<sub>2</sub> during catalysis. The in situ transmission electron microscopy (TEM) imaging under catalytic conditions visualized the movement and growth of active trimetallic sites, which completely stopped once SMSI was established. In situ FTIR (supported by DFT) provided a molecular-level understanding of the formation of various reaction intermediates and their conversion into products, which followed a complex coupling of direct dissociation and redox pathway assisted by hydrogen, simultaneously on different active sites. Thus, sophisticated manipulation of electronic properties of trimetallic sites and defect dynamics significantly enhanced catalytic performance during CO<sub>2</sub> to CO conversion. Reduction (mathematics) ; Electronic structure ; Materials science ; Crystallography ; Nanotechnology ; Chemistry ; Computational chemistry ; Geometry ; Mathematics Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysts for Methane Reforming
Authors: Syed Mubashar Sabir | Ali Bin Thani | Qamar Abbas
Source: Frontiers in Bioengineering and Biotechnology (review, 2025-04-30)
Field: Materials Science — Biomaterials

Nanotechnology in cancer treatment: revolutionizing strategies against drug resistance A notable increase in cancer-related fatalities and morbidity worldwide is attributed to drug resistance. The factors contributing to drug resistance include drug efflux via ABC transporters, apoptosis evasion, epigenetic alterations, DNA repair mechanisms, and the tumor microenvironment, among others. Systemic toxicities and resistance associated with conventional cancer diagnostics and therapies have led to the development of alternative approaches, such as nanotechnology, to enhance diagnostic precision and improve therapeutic outcomes. Nanomaterial, including carbon nanotubes, dendrimers, polymeric micelles, and liposomes, have shown significant benefits in cancer diagnosis and treatment due to their unique physicochemical properties, such as biocompatibility, stability, enhanced permeability, retention characteristics, and targeted delivery. Building on these advantages, this review is conducted through comprehensive analysis of recent literature to explore the principal mechanisms of drug resistance, the potential of nanomaterials to revolutionize selective drug delivery and cancer treatment. Additionally, the strategies employed by nanomaterials to overcome drug resistance in tumors, such as efflux pump inhibition, multidrug loading, targeted delivery to the tumor microenvironment, and gene silencing therapies are discussed in detail. Furthermore, we examine the challenges associated with nanomaterials that limit their application and impede their transition to clinical use. Drug delivery ; Drug resistance ; Nanomedicine ; Efflux ; Drug ; Nanotechnology ; Cancer ; Tumor microenvironment ; Multiple drug resistance ; Medicine ; Pharmacology ; Chemistry ; Materials science ; Biology ; Internal medicine ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Zhengyi Bian | Eric Gomez | Martin Gruebele | Benjamin G. Levine | Stephan Link | Arshad Mehmood | Shuming Nie
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Bottom-up carbon dots: purification, single-particle dynamics, and electronic structure The physico-chemical properties of 'bottom-up' carbon dots synthesized from small molecules feature both generalities, such as sp<sup>2</sup>-networked carbon and core-surface energy transfer, and heterogeneities, due to the unpredictable location of heteroatoms and often non-crystalline structure. Here we focus our review on three aspects of these systems: (1) coupling characterization with bottom-up synthesis to identify and remove confounding byproducts such as small molecules or hydrogen-rich polymers; (2) single-particle characterization to obtain unambiguous information on carbon dots and highlight the distribution of properties around the ensemble average; (3) electronic structure of carbon dots and how it can help elucidate the origin of important properties such as optical absorption and fluorescence from a heterogeneous ensemble of carbon dots. Particle (ecology) ; Nanotechnology ; Top-down and bottom-up design ; Carbon fibers ; Dynamics (music) ; Materials science ; Chemical physics ; Chemistry ; Computer science ; Physics ; Composite material ; Biology Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Gurleen Kaur Sodhi | Tharuka Wijesekara | Kailash Chand Kumawat | Priyanka Adhikari | Kuldeep Joshi | Smriti Singh | Beatrice Farda | Rihab Djebaili | Enrico Sabbi | Fares Ramila | Devendra Sillu | Gustavo Santoyo | Sergio de los Santos‐Villalobos | Ajay Kumar | Marika Pellegrini | Debasis Mitra
Source: Frontiers in Microbiology (review, 2025-01-15)
Field: Materials Science — Materials Chemistry

Nanomaterials–plants–microbes interaction: plant growth promotion and stress mitigation Soil salinization, extreme climate conditions, and phytopathogens are abiotic and biotic stressors that remarkably reduce agricultural productivity. Recently, nanomaterials have gained attention as effective agents for agricultural applications to mitigate such stresses. This review aims to critically appraise the available literature on interactions involving nanomaterials, plants, and microorganisms. This review explores the role of nanomaterials in enhancing plant growth and mitigating biotic and abiotic stresses. These materials can be synthesized by microbes, plants, and algae, and they can be applied as fertilizers and stress amelioration agents. Nanomaterials facilitate nutrient uptake, improve water retention, and enhance the efficiency of active ingredient delivery. Nanomaterials strengthen plant antioxidant systems, regulate photosynthesis, and stabilize hormonal pathways. Concurrently, their antimicrobial and protective properties provide resilience against biotic stressors, including pathogens and pests, by promoting plant immune responses and optimizing microbial-plant symbiosis. The synergistic interactions of nanomaterials with beneficial microorganisms optimize plant growth under stress conditions. These materials also serve as carriers of nutrients, growth regulators, and pesticides, thus acting like "smart fertilizers. While nanotechnology offers great promise, addressing potential environmental and ecotoxicological risks associated with their use is necessary. This review outlines pathways for leveraging nanotechnology to achieve resilient, sustainable, and climate-smart agricultural systems by integrating molecular insights and practical applications. Plant growth ; Promotion (chess) ; Stress (linguistics) ; Biology ; Environmental science ; Botany ; Political science Nanoparticles: synthesis and applications
Authors: Xue Zhang | Jing Qiao | Na Wu | Gongming Xin | Shengying Yue | Xinyu Ma | Fei Pan | Zhihui Zeng | Jiurong Liu
Source: ACS Nano (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Conductive Bimetal–Organic Frameworks with Volcano-Type Fine-Tuned Dielectric Properties for Electromagnetic Wave Absorption Inherently conductive metal-organic frameworks (cMOFs) deliver significant potential for tailoring charge transport characteristics in functional applications. Herein, we synthesize a bimetallic NiCu-HHTP cMOF with precisely controllable interlayer spacing, enabling fine-tuning of the charge transport, electronic band structure, and dielectric properties. By exploiting differences in self-polarization effects between active ions, NiCu-HHTP exhibits an inverted volcano-type interlayer spacing variation through controlled modulation of ion proportions and produces volcano-type tunable dielectric properties. At an optimal Ni/Cu ratio, the bimetallic cMOFs demonstrate an optimal dielectric performance, which enables synergy between an efficient attenuation coefficient and optimal impedance matching. Consequently, Ni3Cu1-HHTP could harvest a minimum reflection loss of -70.3 dB and an effective absorption bandwidth of 5.12 GHz. This study clarifies mechanisms of microstructure-function correlations in bimetallic cMOFs and provides a protocol for precise modulation of interlayer spacing and electronic band structure in cMOF-based electromagnetic wave materials. Bimetal ; Materials science ; Electrical conductor ; Dielectric ; Absorption (acoustics) ; Electrically conductive ; Optoelectronics ; Electromagnetic radiation ; Nanotechnology ; Composite material ; Optics ZnO doping and properties ; Magnesium Oxide Properties and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ying Wang
Source: npj Computational Materials (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Application-oriented design of machine learning paradigms for battery science Abstract In the development of battery science, machine learning (ML) has been widely employed to predict material properties, monitor morphological variations, learn the underlying physical rules and simplify the material-discovery processes. However, the widespread adoption of ML in battery research has encountered limitations, such as the incomplete and unfocused databases, the low model accuracy and the difficulty in realizing experimental validation. It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the application-oriented perspective. We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies. Computer science ; Battery (electricity) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fault Detection and Control Systems
Authors: Tiantian Gao | Yufeng Wu
Source: ACS Omega (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries Solid-state electrolytes (SSEs) have attracted considerable attention for their ability to effectively suppress lithium dendrite growth and enhance the safety and life cycle of lithium-ion batteries (LIBs). However, the commercialization of SSEs has been hindered by low ionic conductivity, limited mechanical strength, and poor interfacial compatibility. Recently, machine learning (ML) has arisen as a helpful tool in SSE studies owing to its efficient data processing and pattern recognition capabilities. This paper reviews recent progress in the application of ML techniques to SSE development for LIBs. It first discusses SSE database creation strategies, then examines the strong influence of descriptor selection on the model's predictive performance of SSE properties, and then highlights the use of various ML algorithms, such as predictive models and generative models, in predicting key SSE properties, including ionic conductivity, elastic moduli, and thermodynamic stability. Additionally, we systemically analyze and compare the interpretability and evaluation metrics of the ML models. We hope this review can provide researchers with a comprehensive perspective, promote the deeper integration of ML in SSE development, and facilitate rapid next-generation SSE discovery and design. Interpretability ; Commercialization ; Generative grammar ; Key (lock) ; Selection (genetic algorithm) ; Feature selection Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research