jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eleonora Romeo (15354669) | Futian You (12962512) | Haibin Ma (1308324) | Francesc Illas (1288131) | Boon Siang Yeo (1263123) | Federico Calle-Vallejo (1335819)
Source: Figshare (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Improving the Descriptionof Adsorbed Hydroxyl toMake Predictive Catalytic Activity Models Artificial intelligence may take catalytic models to a level that matches reality only when provided with high-quality data. Currently, activity plots based on the Sabatier principle are among the most extensively used tools to design materials in thermocatalysis and electrocatalysis. While salient examples attest to their usefulness, activity plots based on density functional theory calculations often fail for various reasons, one of them being the inaccurate approximation of the exchange-correlation energy for free molecules, catalysts, and adsorbed species. Here, we combine experiments and computational modeling to assess the errors in adsorbed hydroxyl (*OH), which are otherwise overly difficult to assess. Upon correcting a systematic adsorbed-phase error in *OH, our predictions match the experimental results for ethylene oxide electroreduction on transition-metal electrodes. The transferability of *OH corrections is illustrated by enhancing activity estimations of CO electrooxidation, where *OH is a key intermediate. Finally, we provide an alternative to cases in which the assessment of the adsorbed-phase errors is unfeasible. In summary, we show here how to improve electrocatalytic estimations by depurating their input data. Transferability ; Density functional theory ; Adsorption ; Catalysis ; Salient ; Key (lock) ; Energy (signal processing) ; Systematic error Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Jonas Rashidi
Source: ArXiv.org (preprint, 2025-04-27)
Field: Materials Science — Biomaterials

Optical Properties of Nanocellulose and Nanocellulose-based Composites for Optoelectronic Applications In this review, we first provided a brief overview of cellulose structures, categorizing various types of nanocellulose based on their production methods, and discussed different photonic structures such as cholesteric phases, photonic crystals, distributed Bragg reflectors, and inverse photonic structures. We then reviewed some models that predict optical properties, including refractive index, scattering, reflection, and birefringence, highlighting factors that can influence these properties, such as electric field, surface roughness, temperature, and density, among others. Following this, we explored recent advancements in predicting optical properties, particularly the use of AI-driven algorithms to enhance the prediction of optical behaviors in nanomaterials. Finally, we provided an in-depth discussion of the latest developments in nonlinear optical phenomena and chiroptical responses in nanocellulose-based materials, focusing on circularly polarized luminescence, structural colors, structural correlations, the impact of enantiomers, and more. Nanocellulose ; Photonics ; Nonlinear optical ; Optical materials ; Optical fiber ; Photonic metamaterial Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Polysaccharides and Plant Cell Walls
Authors: Morad Behandish | jean-charles stinville | Adrian J. Lew | Vijay Srinivasan
Source:  (article, 2025-11-16)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities in Credibility Assessment of Computational Models: A Case Study in Rapid Discovery and Testing of Metallic Alloys Abstract Advances in high-resolution, high-throughput measurement techniques, coupled with powerful recent developments in computational modeling, simulation, and artificial intelligence (AI), have enabled breakthroughs in rapid discovery and testing of novel materials. These possibilities raise new questions pertaining to the credibility of the underlying models for real-world use, especially when these models are adopted to inform engineering or regulatory decisions in applications that deal with human safety and life. We present a risk-informed credibility assessment framework, that has been applied in adjacent domains to support engineering and regulatory decision-making based on the use of computational (including AI) models, and demonstrate its application in the context of rapid discovery and testing of metallic alloys. The framework consists of a number of steps and best practices to define a question of interest, establish a context of use, assess the model risk, develop and execute a plan to validate and verify the model’s credibility within that context, document the results and deviations from the plan, determine adequacy of the models based on such results, and iterate over these steps until the required adequacy is achieved. We present a few case studies using a number of models developed to support rapid discovery and testing of novel metallic alloys in DARPA METALS. We highlight challenges, gaps, and opportunities for future directions that are generalizable to broader engineering applications. Credibility ; Context (archaeology) ; Computational model ; Plan (archaeology) ; Key (lock) ; Heuristics Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Shamim Ahmad Khan | Eswararao Boddepalli | Sudhakar Ajmera | Nilesh Bhosale | G. Sajiv | Anant Balakumar
Source:  (article, 2025-09-27)
Field: Materials Science — Materials Chemistry

Carbon-Based Electronics for Flexible, Sustainable, High-Performance, and Energy-Efficient Electronic Device Applications The human interaction with technology has been transformed through its evolution from bulky vacuum tubes to compact silicon based integrated circuits which power today’s computing. With continued shrinkages of electronic devices in size towards flexibility, sustainability, and energy efficiency has been rapidly desired. However, while they are highly efficient, conventional silicon-based electronics are limited mechanically, in energy consumption, as well as their harsh impact on the environment: for this reason, alternative materials have been searched for that are lighter, more flexible, and more ecofriendly. There is an urgent need for advanced 2D and 3D materials to succeed the current generation electronics based on silicon due to their exceptional electrical conductivity, mechanical flexibility and thermal stability. The combination of these properties makes them good for applications in wearable devices, energy storage systems, high performance transistors and optoelectronic technologies. These materials can potentially integrate into electronic devices to revolutionize computing, communication, and other energy applications with low power, high speed, and environmentally sustainable devices. This work studies the fundamental properties, fabrication techniques and their application fields of carbon-based electronics, and their advantages over traditional semiconductors. This study also covers issues of scalability, material consistency and industry adoption and trends in terms of AI driven design, hybrid material integration and green manufacturing. This research endeavors to elucidate the utility of carbon-based materials in modern electronics by offering a detailed analysis of carbon-based materials as relevant in driving technological progress, sustainable innovation, and the future electronics, in particular, flexible and environmentally friendly electronics. Electronics ; Flexibility (engineering) ; Environmentally friendly ; Transistor ; Wearable technology ; Integrated circuit ; Power electronics ; Work (physics) Graphene research and applications ; Supercapacitor Materials and Fabrication ; Carbon Nanotubes in Composites
Authors: Plehn, Thomas | Barragan-Yani, Daniel | Breitbarth, Eric | Requena, Guillermo | Melching, David
Source: ArXiv.org (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Progress on Data-Driven, Multi-Objective Quantum Optimization Here, we present two complementary approaches that advance quadratic unconstrained binary optimization (QUBO) toward practical use in data-driven materials design and other real-valued black-box optimization tasks. First, we introduce a simple yet powerful preprocessing scheme that, when applied to a machine-learned QUBO model, entirely removes system-level equality constraints by construction. This makes cumbersome soft-penalty terms obsolete, simplifies QUBO formulation, and substantially accelerates solution search. Second, we develop a multi-objective optimization strategy inspired by Tchebycheff scalarization that is compatible with non-convex objective landscapes and outperforms existing QUBO-based Pareto front methods. We demonstrate the effectiveness of both approaches using a simplified model of a multi-phase aluminum alloy design problem, highlighting significant gains in efficiency and solution quality. Together, these methods broaden the applicability of QUBO-based optimization and provide practical tools for data-driven materials discovery and beyond. Quadratic unconstrained binary optimization ; Simple (philosophy) ; Multi-objective optimization ; Scheme (mathematics) ; Quadratic equation ; Optimization problem ; Preprocessor ; Binary number ; Quantum annealing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Multi-Objective Optimization Algorithms
Authors: Plehn, Thomas | Barragan-Yani, Daniel | Breitbarth, Eric | Requena, Guillermo | Melching, David
Source: arXiv (Cornell University) (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Progress on Data-Driven, Multi-Objective Quantum Optimization Here, we present two complementary approaches that advance quadratic unconstrained binary optimization (QUBO) toward practical use in data-driven materials design and other real-valued black-box optimization tasks. First, we introduce a simple yet powerful preprocessing scheme that, when applied to a machine-learned QUBO model, entirely removes system-level equality constraints by construction. This makes cumbersome soft-penalty terms obsolete, simplifies QUBO formulation, and substantially accelerates solution search. Second, we develop a multi-objective optimization strategy inspired by Tchebycheff scalarization that is compatible with non-convex objective landscapes and outperforms existing QUBO-based Pareto front methods. We demonstrate the effectiveness of both approaches using a simplified model of a multi-phase aluminum alloy design problem, highlighting significant gains in efficiency and solution quality. Together, these methods broaden the applicability of QUBO-based optimization and provide practical tools for data-driven materials discovery and beyond. Quadratic unconstrained binary optimization ; Simple (philosophy) ; Multi-objective optimization ; Scheme (mathematics) ; Quadratic equation ; Optimization problem ; Preprocessor ; Binary number ; Quantum annealing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Multi-Objective Optimization Algorithms
Authors: Хао Х.
Source: CyberLeninK (CyberLeninka) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

РАСПОЗНАВАНИЕ ИМЕНОВАННЫХ СУЩНОСТЕЙ В ТУГОПЛАВКИХ ВЫСОКОЭНТРОПИЙНЫХ СПЛАВАХ С ИСПОЛЬЗОВАНИЕМ ГЛУБИННОГО ОБУЧЕНИЯ To address the challenges posed by the rapid growth of literature in the field of refractory high-entropy alloys (RHEAs) and the low efficiency of key information extraction, this paper proposes a semi-automated information extraction workflow. The method leverages a large language model for initial annotation, combined with manual review to construct a high-quality corpus. Based on this, a BERT-BiLSTM-CRF named entity recognition (NER) model is trained to automatically identify and extract information related to materials, processing, structure, and properties. The final results show that the NER model achieves an F1 score of 77% on the test set, significantly reducing manual curation costs and providing support for the construction of materials knowledge bases and data-driven research on new materials. Construct (python library) ; Field (mathematics) ; Key (lock) ; Information extraction ; Named-entity recognition ; Test (biology) ; Language model Machine Learning in Materials Science ; Computational and Text Analysis Methods ; Authorship Attribution and Profiling
Authors: Ashwin Sudhakaran | Arvind H. Jadhav
Source: Journal of Materials Science (article, 2025-05-01)
Field: Materials Science — Biomaterials

Review: harnessing engineered electrospun materials for efficient CO2 conversion into value-added products Materials science ; Solid mechanics ; Electrospinning ; Nanotechnology ; Process engineering ; Polymer ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials
Authors: Satyabrata Maji | Joohee Pradhan | Neelesh Kumar Mehra | Ashish Baldi
Source: Letters in Drug Design & Discovery (article, 2025-12-01)
Field: Materials Science — Biomaterials

Polymeric nanofibers: Quality by design: Perspectives and prospects Polymeric nanofibers, for biomedical and pharmaceutical use, compel process that requires precise control of multiple factors. A quality by design approach provides a risk-based framework to guide development, ensuring consistent fiber diameter, uniformity, strength and surface properties. Polymeric nanofibers -ultrafine fibers with diameters range 1–1000 nm process remarkable physicochemical characteristics, including high surface area to volume ratio, tunable porosity and mechanical flexibility, making them ideal for enhanced drug delivery, tissue engineering and wound healing applications. Integrating Quality by design (QbD) principles, guideline by ICH Q8 to Q11, systematically defined quality target product profile (QTPP), identify critical quality attributes (CQA), such as fibers diameter(100–500 nm) and drug release profiles and employs risk assessment tools like Ishikawa and failure mode effects analysis (FMEA) to optimized critical material attributes and process parameters. Predominant electrospinning fabrication, alongside scalable techniques like centrifugal and solution blow spinning, enables incorporation of natural/synthetic polymers and active compounds for control release, thereby improving bioavailability and patient compliance while reducing variability. Despite challenges in scale up and regulatory compliance, future prospects leverage multi-axial electrospinning, AI-driven modelling and composites to develop tailored, intelligent systems for cancer therapy, regeneration and beyond promising revolutionary pharmaceutical outcomes. Critical quality attributes ; Quality by Design ; Electrospinning ; Quality (philosophy) ; Scalability ; Leverage (statistics) ; Nanofiber ; Process (computing) ; Pharmaceutical manufacturing Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications
Authors: Alfredo Pereira | Oleksandra S. Trofymchuk
Source: Molecular Catalysis (article, 2025-10-29)
Field: Chemistry — Inorganic Chemistry

Metal-catalyzed homogeneous catalytic transfer hydrogenation: A data-driven approach Catalysis ; Homogeneous ; Solvent ; Homogeneous catalysis ; Hydrogen ; Transfer hydrogenation ; Variety (cybernetics) ; Transition metal Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion ; Machine Learning in Materials Science
Authors: Suyoung Chi | Hyun Jong Kim | Choi Kang Il | Yo Han Choi
Source:  (article, 2025-02-09)
Field: Materials Science — Materials Chemistry

Enhancement Strategies for the AI Platform for Accelerated Materials Development and Innovation AI Platform aims to accelerate materials development by integrating AI-driven modeling and data management solutions. Key enhancement strategies include expanding materials data through standardized repositories, optimizing AI models for customized material analysis, and implementing a cloud-based infrastructure to support scalable user access and secure data handling. These advancements position AI Platform as a comprehensive platform that streamlines experimental processes, reduces development time, and fosters digital innovation in materials science. Computer science Machine Learning in Materials Science
Authors: Faruk Alam | Prasurjya Saikia | Durgaprasad Kemisetti | Subhadip Mandal | Amrit Kumar Rath | Alindam Ghosh | Anand S. Dutta | Romit Bhattacharjee | Sanket Seksaria
Source: Pharmaceutical Nanotechnology (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Advancements in Carbon Nanotube-based Drug Delivery Systems: Innovations, Challenges, and Future Directions Carbon nanotubes (CNTs) have emerged as extremely promising nanocarriers for drug delivery due to their superior structural, mechanical, and electrical capabilities. This study digs into recent advances in CNT-based drug delivery systems, focusing on novel functionalization strategies, hybrid nanostructures, and customized nanocarrier designs. Functionalization using polymers, peptides, and other bioactive compounds has dramatically improved CNT solubility, biocompatibility, and precise targeting. Furthermore, hybrid nanostructures that combine CNTs with nanoparticles, liposomes, or metallic components have higher drug-loading capacities, multifunctional therapeutic effectiveness, and controlled drug-release features. CNTs may be customized in size, shape, and surface chemistry, allowing for the development of precise delivery systems that are particularly useful in cancer and complicated disease therapy. However, despite these advances, cytotoxicity, regulatory limits, and difficulty with large-scale manufacturing impede clinical translation. Sustainable methods, thorough safety assessments, and advanced technology like artificial intelligence to maximize functionality and design are all necessary to overcome these obstacles. Future research should focus on overcoming these hurdles to fully realize CNTs' potential as flexible, effective, and safe nanocarriers in drug delivery applications. Nanocarriers ; Drug delivery ; Targeted drug delivery ; Surface modification ; Carbon nanotube ; Drug Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications ; Fullerene Chemistry and Applications
Authors: V. Swaminathan | Chris Mathew | Zhaoyi Chen | Valerii V. Vashchenko | Elena V. Vashchenko | A. K. Srivastava
Source:  (article, 2025-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bistable 2x2 add/drop filter based on polarization rotator switch using helix-free ferroelectric liquid crystal with twisted alignment Efficient, high-speed, and high-bandwidth communication is critical for enabling the future of a data-driven society. The rapid growth of mobile devices, cloud computing, and AI-driven demands from data centers has created an unprecedented need for increased transmission capacity and bandwidth in optical networks. To meet these demands, wavelength division multiplexing (WDM) has become the most efficient and widely adopted solution. Ferroelectric Liquid Crystals (FLCs) are significantly faster, has no FFE compared to nematic LC and present a promising alternative for next-generation optical switches. FLCs offer various electro-optical modes, including bistable mode, Electrically Suppressed Helix mode, Deformed Helix mode, and Kerr effect in vertically aligned Deformed Helix FLC. In this work, we provide an overview of these modes and demonstrate a 2×2 bistable add/drop filter based on polarization rotation using a helix-free FLC with a twisted alignment structure. The design achieves an extinction ratio of up to 25 dB. Additionally, we show a method to achieve true bistability in helix-free FLCs, maintaining stability for over 24 hours. The long-term bistability stems from the twisted alignment structure, where the twist angle is optimized to match the molecular free energy minimum under specific boundary conditions. Photoalignment technology is used to fine-tune the anchoring energy and twist angle, resulting in a fast, bistable device that is independent of temperature, applied frequency, and cell gap. The filter operates at a threshold voltage as low as 0.5 V/μm with a response time of 32 μs at 1.5 V/μm, making it a robust solution for optical switches. Bistability ; Liquid crystal ; Optical bistability ; Extinction ratio ; Polarization (electrochemistry) ; Filter (signal processing) ; Optical switch ; Optical filter ; Bandwidth (computing) ; Helix (gastropod) Liquid Crystal Research Advancements ; Ocular Surface and Contact Lens ; Advanced Optical Imaging Technologies
Authors: Kawaljit Singh Randhawa
Source: Inorganic Chemistry Communications (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Materials with intrinsic self-lubrication and photocatalysis: A brief review Dual (grammatical number) ; Layered double hydroxides ; Design elements and principles ; Material Design ; Bifunctional ; Photocatalysis ; Materials processing ; Environmental remediation MXene and MAX Phase Materials ; Lubricants and Their Additives ; Advanced Sensor and Energy Harvesting Materials
Authors: Jinhang Li | Yanshen Zhang | Jiexin Weng | Xuan Zhang
Source: International Journal of Materials Science and Technology Studies (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Research Progress on Material Creep Experiments Creep behavior in solid propellants, characterized by time-dependent deformation under constant load, critically impacts the structural integrity and operational safety of rocket motors. This paper reviews recent advancements in creep research across three dimensions: (1) Macro-meso characterization techniques, including innovative indentation methods and high-resolution micro-CT/SEM imaging, which elucidate damage evolution mechanisms such as pore nucleation and crack propagation; (2) Multi-factor influences, revealing temperature-stress synergies, filler-matrix interactions, aging effects, and loading-rate dependencies that govern creep dynamics; (3) Cross-material experimental progress, highlighting breakthroughs in cementitious materials (20% prediction accuracy improvement), soft matter rheology (80% testing acceleration), and polymer microstructure transitions. While current studies have established comprehensive creep databases and advanced constitutive modeling, challenges remain in understanding long-term damage accumulation and multi-physics coupling under extreme conditions. Future directions emphasize in-situ multi-scale monitoring, cross-scale computational frameworks, and AI-driven predictive models for next-generation creep-resistant materials. Creep ; Materials science ; Composite material High-Velocity Impact and Material Behavior ; Innovative concrete reinforcement materials ; Innovations in Concrete and Construction Materials