jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohanapriya Venkataraman | Sebnem Sözcü | Jiřı́ Militký
Source: Gels (review, 2025-07-11)
Field: Chemistry — Spectroscopy

Radiative Heat Transfer Properties of Fiber–Aerogel Composites for Thermal Insulation Fiber-aerogel composites have gained significant attention as high-performance thermal insulation materials due to their unique microstructure, which suppresses conductive, convective, and radiative heat transfer. At room temperature, silica aerogels in particular exhibit ultralow thermal conductivity (<0.02 W/m·K), which is two to three times lower than that of still air (0.026 W/m·K). Their brittle skeleton and high infrared transparency, however, restrict how well they insulate, particularly at high temperatures (>300 °C). Incorporating microscale fibers into the aerogel matrix enhances mechanical strength and reduces radiative heat transfer by increasing scattering and absorption. For instance, it has been demonstrated that adding glass fibers reduces radiative heat transmission by around 40% because of increased infrared scattering. This review explores the fundamental mechanisms governing radiative heat transfer in fiber-aerogel composites, emphasizing absorption, scattering, and extinction coefficients. We discuss recent advancements in fiber-reinforced aerogels, focusing on material selection, structural modifications, and predictive heat transfer models. Recent studies indicate that incorporating fiber volume fractions as low as 10% can reduce the thermal conductivity of composites by up to 30%, without compromising their mechanical integrity. Key analytical and experimental methods for determining radiative properties, including Fourier transform infrared (FTIR) spectroscopy and numerical modeling approaches, are examined. The emissivity and transmittance of fiber-aerogel composites have been successfully measured using FTIR spectroscopy; tests show that fiber reinforcement at high temperatures reduces emissivity by about 15%. We conclude by outlining the present issues and potential avenues for future research to optimize fiber-aerogel composites for high-temperature applications, including energy-efficient buildings (where long-term thermal stability is necessary), electronics thermal management systems, and aerospace (where temperatures may surpass 1000 °C), with a focus on improving the materials' affordability and scalability for industrial applications. Aerogel ; Materials science ; Composite material ; Thermal conductivity ; Radiative transfer ; Emissivity ; Thermal insulation ; Heat transfer ; Fiber ; Fourier transform infrared spectroscopy ; Optics ; Thermodynamics Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Radiative Heat Transfer Studies
Authors: Marin Tharrault | Sabrine Ayari | Mehdi Arfaoui | Eva Desgué | Romaric Le Goff | Pascal Morfin | José M. Palomo | Michaël Rosticher | S. Jaziri | Bernard Plaçais | P. Legagneux | Francesca Carosella | Christophe Voisin | R. Ferreira | Emmanuel Baudin
Source: Physical Review Letters (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Optical Absorption in Indirect Semiconductor to Semimetal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>PtSe</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Arises from Direct Transitions PtSe_{2} is a van der Waals material transitioning from an indirect band gap semiconductor to a semimetal with increasing thickness. Its absorption threshold has been conjectured to originate from interband indirect transitions. By quantitative comparison between broadband (0.8-3.0 eV) optical absorption of high-quality exfoliated crystals and DFT ab initio simulations, we prove instead that the optical absorption arises only from direct transitions. This understanding allows us to shed light on the semiconductor-to-semimetal transition in an emblematic strongly thickness-dependent 2D material, and to explore the effect of stacking and excitons on the optical absorption. Semimetal ; Semiconductor ; Absorption (acoustics) ; van der Waals force ; Ab initio ; Exciton ; Stacking ; Band gap ; Materials science ; Physics ; Condensed matter physics ; Optoelectronics ; Optics ; Quantum mechanics ; Nuclear magnetic resonance ; Molecule 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Dae Eon Jung | Vincent J. Einck | Alex Dawicki | Victor Malgras | Lucas D. Verrastro | David Grosso | Amir Arbabi | James J. Watkins
Source: Advanced Materials (article, 2025-05-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Full Wafer Scale Manufacturing of Directly Printed TiO<sub>2</sub> Metalenses at Visible Wavelengths with Outstanding Focusing Efficiencies Highly efficient metalens arrays designed for 550 nm are directly printed using UV-assisted nanoimprint lithography (UV-NIL) and a TiO<sub>2</sub> nanoparticle (NP)-based ink on 8″ optical wafers with imprint times less than 5 min. Approximately one-thousand 4-mm metalenses are fabricated per wafer with uniform optical performance using a reusable PDMS-based elastomeric stamp. The absolute and relative focusing efficiencies are as high as 81.2% and 90.4%, respectively, matching closely with the simulated maximum efficiencies of 83% and 91% achievable with the given master design, indicating that future improvements are possible, and efficiencies are not limited by materials or process. The imprinted metalenses are free from organics due to a post-imprint calcination step and exhibit outstanding dimensional and optical stabilities. The highest efficiencies are attained using imprint formulations comprised of mixtures of 10 and 20 nm TiO<sub>2</sub> NPs, whose denser packing not only increases the refractive index (RI) of the calcined lenses up to 2.0 but also reduces the feature shrinkage relative to the master. 25 cycles of atomic layer deposition of TiO<sub>2</sub> following imprinting increase the RI up to 2.3 without changing dimensions by uniform gap filling between NPs. This work opens a path for true, full-scale additive manufacturing of metaoptics. Materials science ; Wafer ; Wavelength ; Visible spectrum ; Optoelectronics ; Scale (ratio) ; Nanotechnology ; Optics Metamaterials and Metasurfaces Applications ; Advanced Memory and Neural Computing ; Nanomaterials and Printing Technologies
Authors: Mohammad Javad Bayat | Kamran Asemi | Amin Kalhori | Masoud Babaei
Source: Computer Modeling in Engineering & Sciences (review, 2025-01-01)
Field: Materials Science — General Materials Science

Plates, Beams and Shells Reinforced by CNTs or GPLs: A Review on Their Structural Behavior and Computational Methods Since the initial observation of carbon nanotubes (CNTs) and graphene platelets (GPLs) in the 1990 and 2000s, the demand for high-performance structural applications and multifunctional materials has driven significant intere... | Find, read and cite all the research you need on Tech Science Press Materials science ; Composite material ; Reinforced concrete ; Structural engineering ; Engineering Material Properties and Applications
Authors: Indrajeet Mandal | Jitendra Soni | Mohd Zaki | Morten M. Smedskjær | Katrin Wondraczek | Lothar Wondraczek | Nitya Nand Gosvami | N. M. Anoop Krishnan
Source: Nature Communications (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Evaluating large language model agents for automation of atomic force microscopy Large language models (LLMs) are transforming laboratory automation by enabling self-driving laboratories (SDLs) that could accelerate materials research. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. Here, we show that LLM agents can automate atomic force microscopy (AFM) through our Artificially Intelligent Lab Assistant (AILA) framework. Further, we develop AFMBench-a comprehensive evaluation suite challenging LLM agents across the complete scientific workflow from experimental design to results analysis. We find that state-of-the-art LLMs struggle with basic tasks and coordination scenarios. Notably, models excelling at materials science question-answering perform poorly in laboratory settings, showing that domain knowledge does not translate to experimental capabilities. Additionally, we observe that LLM agents can deviate from instructions, a phenomenon referred to as sleepwalking, raising safety alignment concerns for SDL applications. Our ablations reveal that multi-agent frameworks significantly outperform single-agent approaches, though both remain sensitive to minor changes in instruction formatting or prompting. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments-AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. These findings establish the necessity for benchmarking and robust safety protocols before deploying LLM agents as autonomous laboratory assistants across scientific disciplines. Workflow ; Automation ; Implementation ; Benchmarking ; Disk formatting ; Suite ; Domain (mathematical analysis) ; Intuition Machine Learning in Materials Science ; Software Engineering Research ; Ferroelectric and Negative Capacitance Devices
Authors: Isabella Stewart | Markus J. Buehler
Source: Molecular Systems Design & Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular analysis and design using generative artificial intelligence <i>via</i> multi-agent modeling We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed <i>via</i> their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design. Generative grammar ; Computer science ; Gemma ; Artificial intelligence ; Test (biology) ; Ecology ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pedram Aminharati | Mohammadali Shirinbayan | Khaled Benfriha | Fodil Meraghni | Joseph Fitoussi
Source: International Journal of Hydrogen Energy (review, 2025-09-01)
Field: Materials Science — Materials Chemistry

AI-driven advances in composite materials for hydrogen storage vessels: A review International audience Hydrogen storage ; Composite number ; Materials science ; Hydrogen ; Computer science ; Environmental science ; Composite material ; Chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Simon Bekemeier | Celso R. C. Rêgo | Lin Han | Ujjal Saikia | Osamu Waseda | Markus Apel | Felix Arendt | Alexander Aschemann | Bernd Bayerlein | Robert Courant | Gordian Dziwis | Florian Fuchs | Ulrich Giese | Kurt Junghanns | Mohamed Kamal | Lukas Koschmieder | Sebastian Leineweber | Marc Luger | Marco Lukas | Jürgen Maas | Jana Mertens | Björn Mieller | Ludger Overmeyer | Norbert Pirch | Jan Reimann | Sebastian Schröck | Philipp Schulze | Jörg Schuster | Alexander Seidel | Oleg Shchyglo | Marek Sierka | Frank Silze | Simon Stier | Marvin Tegeler | Jörg F. Unger | Matthias Weber | Tilmann Hickel | Jörg Schaarschmidt
Source: Advanced Engineering Materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store's role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science. Workflow ; Interoperability ; Computer science ; Workflow technology ; Software engineering ; Domain (mathematical analysis) ; Knowledge management ; World Wide Web ; Data science ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Pratyush Tiwary | Lukas Herron | Richard John | Suemin Lee | Disha Sanwal | Ruiyu Wang
Source: Proceedings of the National Academy of Sciences (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Generative AI for computational chemistry: A roadmap to predicting emergent phenomena The recent surge in generative AI has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both generative AI and computational chemistry. It then covers widely used generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models, and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before and that generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics. Generative grammar ; Field (mathematics) ; Perspective (graphical) ; Key (lock) ; Generative Design ; Adversarial system ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Simona Premcheska | André G. Skirtach | Anna M. Kaczmarek
Source: Advanced NanoBiomed Research (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Roadmap to Nanomedical Applications: Nanotoxicology and In Vitro Guidelines for Lanthanide‐Doped Luminescence Nanothermometers Lanthanide‐based luminescence nanothermometry has demonstrated unprecedented advantages in the development of nanotheranostic platforms for potential medical applications, yet despite exponential research progress and great enthusiasm across various related disciplines, a swift bench‐to‐bedside translation seems to be still out of reach. This is predominantly related to fundamental research issues at the preclinical stage, such as complex nanomaterials design, incomplete optimization, fragmented characterization, and insufficient validation of nanothermometer performance in physiological environments. The main impediments currently are important biophysicochemical issues that must be addressed comprehensively, first and foremost in available in vitro systems, before going on to in vivo investigations. This review outlines a critical perspective, as well as a route of suggestions and underexplored aspects to address and significantly minimize the existing translational gap. Nanotoxicology ; Nanotechnology ; Luminescence ; Materials science ; Doping ; Nanoparticle ; Optoelectronics Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics ; Inhalation and Respiratory Drug Delivery
Authors: Huawei Liu | Sihui Li | Shan Zhu | Yin Hu | Xiaopeng Han | Chunsheng Shi | Fang He | He Chunnian | Biao Chen | Naiqin Zhao
Source: Advanced Energy Materials (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

How Machine Learning Has Driven the Development of Rechargeable Ion Batteries Abstract Machine learning (ML) has entered rechargeable ion battery (RIB) research. The goal is to speed progress toward carbon‐neutral energy storage. In the last decade, data‐driven workflows have joined experiments and simulations. They deliver rapid screening and structured insight for electrodes and electrolytes. Yet, despite substantial scientific advances, a coherent and mechanism‐aware framework for deploying ML throughout the RIB innovation cycle remains elusive. This review, therefore, concentrates on three interconnected dimensions of ML integration: i) overall machine‐learning process, ii) algorithm and technology selection with interpretability, and iii) application scenarios covering intercalation electrodes, conversion electrodes, and electrolyte design. To this end, design rules and workflow are distilled that can be readily adopted by both experimentalists and theorists. Moreover, the review highlights how task‐oriented ML architectures expedite material discovery, mechanistic elucidation, and process optimization in next‐generation RIB devices. Finally, persisting challenges, especially data scarcity, mechanism distortion, are outlined alongside opportunities offered by diffusion models, transformer frameworks, large language models, and autonomous laboratories. It is expected that this review will effectively guide readers in the design of next‐generation RIBs with emerging AI technologies. Workflow ; Transformer ; Battery (electricity) ; Process (computing) ; Selection (genetic algorithm) ; Mechanism (biology) ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Junwu Chen | Xu Huang | Hua Cheng | Yulian He | Philippe Schwaller
Source: Nature Communications (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

A multi-modal transformer for predicting global minimum adsorption energy The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the GMAE through density functional theory (DFT). Thus, we designed a multi-modal transformer called AdsMT to rapidly predict the GMAE based on surface graphs and adsorbate feature vectors without site-binding information. The AdsMT model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were introduced, providing a foundation for further research on the challenging GMAE prediction task. Our AdsMT framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 eV, respectively. Beyond GMAE prediction, AdsMT's cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions. Adsorption ; Computer science ; Modal ; Density functional theory ; Benchmark (surveying) ; Encoder ; Transformer ; Materials science ; Biological system ; Algorithm ; Chemistry ; Voltage ; Physics ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Aliakbar Jafari | Ahmed Al‐Ostaz | Sasan Nouranian
Source: Polymers for Advanced Technologies (article, 2025-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in Stimuli‐Responsive Materials for Electromagnetic Interference Shielding ABSTRACT The increasing proliferation of electronic devices and advanced communication networks has resulted in heightened electromagnetic interference (EMI), posing significant challenges in both technological and environmental contexts. Traditional EMI shielding materials, such as metals and composite coatings, offer limited adaptability and are unable to meet the dynamic demands of modern electronic systems. Recent advancements have introduced smart stimuli‐responsive materials for EMI shielding, which provide real‐time tunability, thereby addressing the limitations of conventional static shielding solutions. These materials leverage various mechanisms—such as compressive and tensile strains, phase transitions, shape memory effects, and responses to chemical agents, humidity, or crossover angle changes—to dynamically adjust their EMI shielding effectiveness (EMI‐SE). This review provides an in‐depth analysis of the recent progress in smart EMI shielding technologies, highlighting their tunable mechanisms, material compositions, and applications. Furthermore, it discusses the existing challenges and potential future research directions required for the advancement of this technology. By enabling dynamic adaptability to electromagnetic environments, smart EMI shielding materials present a promising solution for telecommunications, wearable electronics, aerospace, and defense sectors. Materials science ; Electromagnetic shielding ; Electromagnetic interference ; Interference (communication) ; Nanotechnology ; Composite material ; Electrical engineering ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Dielectric materials and actuators