jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinghan Zhao | Anna‐Lena Hansen | Anna Dahlhaus | Nico Brandt | Michael Selzer | Arnd Koeppe | Britta Nestler | Michael Knapp | Helmut Ehrenberg
Source: Materials Today Communications (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

LISA: A Lithium-Ion Solid-State Assistant using large language models for knowledge defragmentation in battery science and beyond This work presents the development and implementation of a research assistant tool, Lithium-Ion Solid-State Assistant (LISA), based on the Retrieval-Augmented Generation (RAG) architecture. This assistant has been specifically tailored to enhance the retrieval and extraction of information from the domain of solid-state battery research. The system employs sophisticated retrieval techniques to efficiently identify the most pertinent document segments in response to researcher queries. The segments above are subsequently collated into prompts for a Large Language Model (LLM), which generates accurate, contextually enhanced responses to queries about solid-state battery-related subjects. This approach has the potential to markedly improve the accessibility and usability of a range of documentation, from project reports to complex scientific literature. The system provides researchers with a powerful tool to bridge disciplinary gaps, facilitate cross-disciplinary communication, accelerate knowledge discovery, and drive innovation in the field of solid-state batteries. A comprehensive evaluation was conducted to assess the system’s performance, with results indicating its potential to transform scientific research workflows. The system offers a robust open-source framework for future advancements in automated knowledge retrieval, understanding, and management, particularly in supporting the development of new materials. Materials science ; Lithium (medication) ; Ion ; Battery (electricity) ; Lithium-ion battery ; Solid-state ; Engineering physics ; State (computer science) ; Optoelectronics ; Computer science ; Psychology ; Thermodynamics ; Programming language ; Quantum mechanics ; Physics ; Psychiatry Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Big Data and Digital Economy
Authors: Mudassir Ur Rahman | Mohamed Farouk Elsadek | R. Roopashree | Aditya Kashyap
Source: Reviews in Inorganic Chemistry (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Advancing hydrogen storage and exploring the potential of perovskite hydrides and metal hydrides Abstract Recent notable developments concerning the hydrogen storage materials are summarized in this review, with particular emphasis placed on magnesium hydrides, titanium- and calcium-based hydrides, metal borohydrides, and perovskite-type hydrides. MgH 2 performance is greatly improved through nanostructuring and transition metal doping. Calcium hydrides, as well as titanium hydrides have very good hydrogen storage properties in addition to the potential for superconductivity. Single and bimetallic borohydrides have high hydrogen contents, achieving high performance in battery and energy application, but face challenges in regeneration and stability. Many perovskite-type hydrides, including MgX 3 H 8 (X = Sc, Ti, Zr), Li 2 CaH 4 , and Li 2 SrH 4 , as well as oxide-based hydrides like MgTiO 3 Hx, CaTiO 3 Hx, and BaYO 3 H x , are emerging as ideal hydrogen storage materials thanks to their stable crystal structure, promising thermodynamics, excellent mechanical properties, and efficient hydrogen cycling. Destabilization of the hydrogen is likely based on analysis of DFT studies of hydrogen binding enthalpy, which supports stability and negative formation enthalpies for MgX 3 H 8 and BaYO 3 H 3 as well as CaTiO 3 H 6 and storage of 4.27 wt% with desorption at ∼821.1 K further strengthens the potential of perovskite hydrides in a variety of hydrogen storage applications. Chemistry ; Perovskite (structure) ; Hydrogen storage ; Metal ; Hydride ; Hydrogen ; Inorganic chemistry ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Rohit S. K. Gadde | Sreelaya Devaguptam | Fangning Ren | Rajat Mittal | Lechen Dong | Yao Wang | Fang Liu
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chatbot-assisted quantum chemistry for explicitly solvated molecules Advanced computational chemistry software packages have transformed chemical research by leveraging quantum chemistry and molecular simulations. Despite their capabilities, the complicated design and the requirement for specialized computing hardware hinder their applications in the broad chemistry community. Here, we introduce AutoSolvateWeb, a chatbot-assisted computational platform that addresses both challenges simultaneously. This platform employs a user-friendly chatbot interface to guide non-experts through a multistep procedure involving various computational packages, enabling them to configure and execute complex quantum mechanical/molecular mechanical (QM/MM) simulations of explicitly solvated molecules. Moreover, this platform operates on cloud infrastructure, allowing researchers to run simulations without hardware configuration challenges. As a proof of concept, AutoSolvateWeb demonstrates that combining virtual agents with cloud computing can democratize access to sophisticated computational research tools. Computer science ; Interface (matter) ; Cloud computing ; Software ; Computational science ; Quantum chemical ; Chatbot ; Nanotechnology ; Chemistry ; Molecule ; World Wide Web ; Programming language ; Operating system ; Materials science Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Scientific Computing and Data Management
Authors: Uzba Dehloon | Iffat Batool | Naveed Ahmad | Aftab Afzal | Maubashera Nawaz | Ghazala Iram | Abbas Washeel Salman | Haitham K. R. Al‐Sharifi | Ateeq-ur Rehman
Source: Reviews in Inorganic Chemistry (article, 2025-01-20)
Field: Chemistry — Inorganic Chemistry

Metal-organic-frameworks (MOFs) advanced synthetic strategies and applications, including light emitting diodes, solar cells and photodetectors Abstract Several synthetic approaches, such as solvothermal, microwave-assisted, electrochemical, and mechanochemical techniques, are used in the creation of metal-organic frameworks (MOFs). The resulting MOFs can be tailored for particular purposes by utilizing the distinct benefits that each of these approaches offers in terms of managing their size, shape, and functional qualities. The most recent developments in MOF synthesis are examined in this study along with how they are being used in optoelectronic devices such as photodiodes, solar cells, and light-emitting diodes (LEDs). MOFs are potential candidates for these applications because of their special qualities, which include their capacity to host light-emitting guest molecules, promote charge transport, and improve light absorption. MOFs effectively house luminescent centers in LEDs, improving brightness and color purity. MOFs improve charge separation and light collecting efficiency in solar cells. The customizable band gaps of MOFs, which may be designed to maximize their performance in photodetection, are advantageous to photodiodes. Advances in MOFs could revolutionize future optoelectronics. Finally, MOFs are based on the ongoing development of advanced synthetic methods that allow for the fabrication of LEDs, solar cells and photodetectors at higher levels of technological innovation and application. Additionally, MOFs in photodetectors, are thought to be active material and their special capacity to interact at various wavelengths may pave the way for more sensitive and adaptable application-specific sensors in a range of areas, including high-speed communication technologies and environmental monitoring. Photodetector ; Chemistry ; Diode ; Optoelectronics ; Metal ; Metal-organic framework ; Light-emitting diode ; Nanotechnology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Sheetal Mavi | S. P. Kadian | Pradeepta Kumar Sarangi | Ashok Kumar Sahoo | Shruti Singh | Muhd Zu Azhan Yahya | Nor Mas Mira Abd Rahman
Source: Macromolecular Symposia (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

Advancements in Machine Learning and Artificial Intelligence in Polymer Science: A Comprehensive Review Abstract Technology, health care, and transport are merely some of the industries that historically rely on polymer‐based materials. In past centuries, the creation of innovative polymer materials has been dependent upon extensive experiments and error procedures that require an extensive number of resources as well as time. With the objective to explore the transformative potential of machine learning (ML) and artificial intelligence (AI) in material discovery, design, and optimization, this paper explores the integration of ML and AI in polymer‐based materials research. Researchers are able to speed the development of new polymer‐based materials with improved properties and functionalities by utilizing sophisticated algorithms and computational models. The use of ML and AI in polymer research is examined, with a focus on how these technologies may stimulate innovation and expand material science research. Artificial intelligence ; Materials science ; Polymer science ; Computer science ; Nanotechnology Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies
Authors: Chenxu Wang | Roman Boulatov
Source: Molecules (review, 2025-01-22)
Field: Materials Science — Polymers and Plastics

Autonomic Self-Healing of Polymers: Mechanisms, Applications, and Challenges Mechanical loads degrade polymers by enabling mechanochemical fragmentation of macromolecular backbones. In most polymers, this fragmentation is irreversible, and its accumulation leads to the appearance and propagation of cracks and, ultimately, fracture of the material. Self-healing describes a diverse and loosely defined collection of approaches that aim at reversing this damage. Most reported synthetic self-healing polymers are non-autonomic, i.e., they require the user to input free energy (in the form of heat, irradiation, or reagents) into the damaged material to initiate its repair. Here, we critically discuss emerging chemical approaches to autonomic self-healing that rely on regenerating the density of load-bearing, dissociatively-inert backbone bonds either after the load on a partially damaged material dissipated or continuously and in competition with the mechanochemically driven loss of backbones in the loaded material. We group the reported chemistries into three broad types whose analysis yields a set of criteria against which the potential of a prospective approach to yield practically relevant self-healing polymers can be assessed quantitatively. Our analysis suggests that the direct chain-to-chain addition in mechanically loaded unsaturated polyolefins is the most promising chemical strategy reported to date to achieve autonomic synchronous self-healing of practical significance. Self-healing ; Self-healing material ; Polymer ; Fragmentation (computing) ; Bone healing ; Materials science ; Inert ; Nanotechnology ; Composite material ; Chemistry ; Computer science ; Organic chemistry ; Surgery Polymer composites and self-healing ; Antimicrobial agents and applications ; Synthetic Organic Chemistry Methods
Authors: Yang An | B. X. Yu | Chengyuan Li | Dayiheng Liu | Fei Huang | Haoyan Huang | Jian‐Dong Jiang | Jianhong Tu | Jianwei Zhang | Jinchuan Zhou | Junyang Lin | Kai Dang | Kexin Yang | Le Yu | Li Mei | Minmin Sun | Qin Zhu | Rui Men | Tao He | Weijia Xu | Wenbiao Yin | Wenyuan Yu | Xiafei Qiu | Xingzhang Ren | Xinlong Yang | Yongping Li | Zhiying Xu | Zipeng Zhang
Source: ArXiv.org (preprint, 2025-01-26)
Field: Materials Science — Materials Chemistry

Qwen2.5-1M Technical Report We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enhanced long-context capabilities through long-context pre-training and post-training. Key techniques such as long data synthesis, progressive pre-training, and multi-stage supervised fine-tuning are employed to effectively enhance long-context performance while reducing training costs. To promote the use of long-context models among a broader user base, we present and open-source our inference framework. This framework includes a length extrapolation method that can expand the model context lengths by at least four times, or even more, without additional training. To reduce inference costs, we implement a sparse attention method along with chunked prefill optimization for deployment scenarios and a sparsity refinement method to improve precision. Additionally, we detail our optimizations in the inference engine, including kernel optimization, pipeline parallelism, and scheduling optimization, which significantly enhance overall inference performance. By leveraging our inference framework, the Qwen2.5-1M models achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million tokens of context. This framework provides an efficient and powerful solution for developing applications that require long-context processing using open-source models. The Qwen2.5-1M series currently includes the open-source models Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, as well as the API-accessed model Qwen2.5-Turbo. Evaluations show that Qwen2.5-1M models have been greatly improved in long-context tasks without compromising performance in short-context scenarios. Specifically, the Qwen2.5-14B-Instruct-1M model significantly outperforms GPT-4o-mini in long-context tasks and supports contexts eight times longer. Computer science Solidification and crystal growth phenomena
Authors: Priyanshu Bharadwaj | Gaëlle Roullin | Jeanne Leblond Chain
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Crossing the blood–brain barrier: advances in dendrimer-based nanocarriers for central nervous system delivery Dendrimer-based nanocarriers are emerging as transformative platforms to overcome the challenge of crossing the blood-brain barrier (BBB) in the treatment and diagnosis of central nervous system (CNS) disorders. Their highly branched, monodisperse architecture enables precise control over size, surface chemistry, and cargo loading, distinguishing them from conventional nanocarriers such as liposomes and polymeric nanoparticles. This review summarizes recent advances in designing dendrimers for targeted brain delivery across diverse neurological conditions. In particular, advances in dendrimer synthesis and functionalization including PEGylation, ligand conjugation, and biomimetic coatings have significantly improved BBB permeability, biocompatibility, and disease-specific targeting. Key dendrimer classes, including PAMAM, PPI, phosphorus, and peptide-based variants, exploit multiple BBB crossing mechanisms such as adsorptive-mediated, receptor- and carrier-mediated transcytosis. We then focus on therapeutic applications and clinical translation, highlighting candidates such as OP-101 and <sup>18</sup>F-OP-801, which demonstrate targeted delivery and imaging capabilities in neuroinflammatory and oncological models. Despite these advances, challenges remain in addressing dendrimer-associated toxicity, scalable manufacturing, and the heterogeneity of BBB dysfunction across disease states. Looking ahead, integrating artificial intelligence (AI) for BBB permeability prediction and the adoption of advanced biomimetic and aptamer-based targeting strategies could accelerate the development of next-generation dendrimer therapeutics. Nanocarriers ; Drug delivery ; Targeted drug delivery ; Dendrimer ; Exploit ; Biomimetic materials Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Brain Tumor Detection and Classification
Authors: Alkmini Michaloglou | Ioannis Papadimitriou | Ilias Gialampoukidis | Stefanos Vrochidis | Ioannis Kompatsiaris
Source: Archives of Computational Methods in Engineering (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Networks in Materials Modeling and Design: A Review Abstract Physics-Informed Neural Networks are neural networks which combine data-driven deep learning and physical models by incorporating physical laws into their loss function. They are promising and flexible solvers or surrogates for problems governed by partial differential equations, including forward and inverse cases, and during the last few years they have been applied to a variety of problems in physics and engineering. In this survey, we present a comprehensive analysis of PINNs while focusing on their applications in materials science. We examine their foundational form and components, expand on enhancing strategies, and explore various aspects of their practical implementations. For materials modeling, problem cases are systematically organized based on the main scope of publications found in the literature. For this survey, problems are categorized into heat transfer related problems, including multiphysics cases, materials properties and design, materials durability, stress analysis, as well as problems related to porous media and multi-phase materials. Finally, we discuss the current challenges and limitations of PINNs, their advantages, and potential future research directions. Multiphysics ; Variety (cybernetics) ; Scope (computer science) ; Artificial neural network ; Inverse problem ; Current (fluid) ; Porous medium Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Thomas Glorieux | Razieh Tavassolian | Tom Depover | Kim Verbeken
Source: Corrosion Reviews (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Cathodic protection and sulphate-reducing bacteria: a complex interaction in offshore steel structures Abstract Microbiologically influenced corrosion (MIC) caused by sulphate-reducing bacteria (SRB) is an important issue, particularly in the offshore industry where cathodic protection (CP) is commonly used to prevent the corrosion of steel structures. However, its relation with MIC remains unclarified, as the interaction between CP and microbial activity is still far from understood. Under overprotection conditions, the water reduction reaction can lead to atomic hydrogen uptake, which may result in hydrogen embrittlement (HE). This uptake is influenced by calcareous deposits, i.e. inorganic compounds formed during CP. Additionally, SRB are argued in literature to enhance hydrogen uptake. This review explores various perspectives on the interaction between microbial activity, particularly SRB, and CP, as well as their individual and combined impact on hydrogen uptake in an offshore context. Cathodic protection ; Materials science ; Metallurgy ; Submarine pipeline ; Sulfate-reducing bacteria ; Corrosion ; Electrochemistry ; Oceanography ; Sulfate ; Geology ; Chemistry ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Polyxeni Vourna | Pinelopi P. Falara | Aphrodite Ktena | Evangelos Hristoforou | Nikolaos D. Papadopoulos
Source: Sensors (article, 2025-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Barkhausen Noise Sensor: A Comprehensive Review of Recent Advances in Non-Destructive Testing and Material Characterization Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques-encompassing time-domain analysis, frequency-domain spectral methods, time-frequency transforms, and machine learning algorithms-extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15-20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Barkhausen effect ; Characterization (materials science) ; Residual stress ; Nondestructive testing ; Structural health monitoring ; Instrumentation (computer programming) ; Noise (video) ; Signal processing Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Machine Fault Diagnosis Techniques
Authors: Nazeri Abdul Rahman | Zainab Ngaini | Saba Farooq | Sabrina Chua Ai Ling | Puteri Nabilah Jefree Shahren
Source: Bioresources and Bioprocessing (review, 2025-07-29)
Field: Materials Science — Biomaterials

Sustainable lactic acid production from agricultural waste: a review of current techniques, challenges and future directions The increasing demand for lactic acid (LA), driven by its applications in biodegradable plastics, food additives, pharmaceuticals, and cosmetics, necessitates the development of sustainable production methods. Traditional processes often rely on food-based feedstock, aggravating food security concerns. This review addresses the critical gap in utilising agricultural waste as a non-food feedstock for LA production, thereby reducing waste and promoting a circular economy. Current techniques are thoroughly explored, including fed-batch, batch, and continuous fermentation procedures, as well as the utilisation of various microbial strains, including Lactobacillus species. Recent developments in genetic engineering and innovative fermentation techniques are discussed, alongside challenges such as substrate variability and contamination. By highlighting the potential of agricultural residues, such as sugarcane bagasse, rice husk and corn stover, this review provides a roadmap for future research and industrial applications to enhance sustainability and efficiency in LA production. Bagasse ; Raw material ; Agriculture ; Production (economics) ; Waste management ; Sustainability ; Food security ; Biotechnology ; Husk ; Industrial and production engineering ; Biochemical engineering ; Business ; Environmental science ; Engineering ; Pulp and paper industry ; Chemistry ; Economics biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microbial Metabolic Engineering and Bioproduction