jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ma Anquan | Yang Ziqing | He Qixuan | Lan Jing
Source: Translational dental research. (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bioinformatics and AI in dental biomaterials: Applications in drug delivery for tissue engineering Dental biomaterials play a vital role in restorative dentistry and tissue regeneration, but their design and clinical translation remain challenging due to complex material–tissue interactions, patient-specific variability, and lengthy trial-and-error development cycles. Artificial intelligence (AI) and bioinformatics have emerged as complementary approaches to address these challenges. This review highlights the synergistic roles of AI and bioinformatics in advancing dental biomaterials, focusing on two major application domains: drug delivery systems and tissue engineering. Key methodologies such as predictive modeling and biomaterial informatics are explored for accelerating the design of novel materials with optimized drug release profiles and scaffold architectures. In parallel, multi-omics data analysis and closed-loop feedback optimization are presented as powerful strategies for post-implementation analysis, enabling deep understanding of tissue responses and iterative refinement of biomaterial performance. By covering applications at both the design and post-implementation stages, this review underscores the value of integrating AI-driven models with bioinformatics-driven data insights in guiding advanced dental biomaterial development. The convergence of these technologies holds strong potential to enable smarter, personalized, and more efficient dental biomaterials, paving the way for next-generation patient-specific dental therapies. • Integrates AI/ML and bioinformatics to accelerate dental biomaterials across polymers, metals, ceramics, and nanomaterials. • Pre-design: predictive models optimize compositions, scaffolds, and drug-release kinetics. • Post-analysis: AI-driven imaging and multi-omics decoding quantify in vivo integration, efficacy, and safety. • Personalization: disease-specific omics guide payload selection; models tailor responsive carriers to patient profiles. • Roadmap: proposes a closed-loop, data-driven workflow and dataset standardization to speed clinical translation. Workflow ; Biomaterial ; Drug delivery ; Tissue engineering ; Scaffold ; Health informatics tools ; Informatics Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Computational Drug Discovery Methods
Authors: Rossella Santonocito | Alessia Cavallaro | Nunzio Tuccitto | Giuseppe Trusso Sfrazzetto
Source: Chemical Communications (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Synthetic Fluorescent Receptor Arrays for Sensing in Complex Matrices: From Robust Chemistry to Data-Driven Readouts Sensing is moving from specific lock-and-key biorecognition toward chemically programmable, pattern-based approaches. Synthetic fluorescent receptor arrays, composed of rationally designed organic probes (e.g., rhodamines, BODIPYs, naphthalimides, carbon dots) that engage... Fluorescence ; Receptor ; Fluorescent labelling ; Carbon fibers ; Receptor-mediated endocytosis Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications
Authors: Glen R. Jones | Masami Kamigaito | James L. Grace | Greg G. Qiao | Cyrille Boyer | Joji Tanaka | Wei You | Jian Zhu | Vianna F. Jafari | Athina Anastasaki | Jiajia Li
Source: Open MIND (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Beyond Traditional RAFT Polymerization: Emerging Strategies and Future Perspectives; A Third Update Reversible addition-fragmentation chain transfer (RAFT) polymerization has undergone transformative growth since its inception in 1998, emerging as a powerful and versatile tool for precision polymer synthesis. This review highlights the latest developments in non-traditional RAFT polymerization from 2020 to 2025, capturing major innovations in activation techniques and expanding applications. Key emerging directions include the integration of RAFT into smart synthesis platforms powered by artificial intelligence, enabling high-throughput and autonomous polymer discovery, and innovations in RAFT depolymerization that support sustainable plastic recycling. With RAFT increasingly accessible to diverse materials science domains, this review provides a forward-looking perspective on the evolving capabilities and future potential of RAFT polymerization. Raft ; Transformative learning ; Reversible addition−fragmentation chain-transfer polymerization ; Perspective (graphical) ; Emerging technologies Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Synthetic Organic Chemistry Methods
Authors: Marcin Pietrasik | Anna Wilbik | Nikola Prianikov | Cristina Afanasii | Stefan Deelen | Adolfo Santamónica | Sebastian Schönleben | Mirunalini Sekar | Kim Ragaert | Sin Yong Teng
Source: Digital Chemical Engineering (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Data-driven fingerprinting plastic waste material using low-cost spectroscopy with data fusion Fusion ; Spectroscopy ; Sensor fusion ; Plastic waste ; Calibration Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Danjuma Kabir
Source: Journal of Applied Sciences and Environmental Management (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Green Synthesis of Nanoparticles Using Plant Extracts and Biopolymers Nanotechnology has transformed various scientific fields due to the distinctive properties of nanoparticles, yet conventional synthesis methods often rely on toxic chemicals, high energy, and pose environmental risks. This review explores green nanochemistry as a sustainable alternative, emphasizing plant-mediated and biopolymer-assisted synthesis. Phytochemical-rich plant extracts and natural biopolymers such as proteins and polysaccharides act as reducing, stabilizing, and capping agents, enabling controlled nanoparticle formation under mild, eco-friendly conditions. Key processes include reduction, nucleation, growth, and stabilization, influenced by factors like pH, temperature, and reactant concentration. The review covers metallic, metal oxide, and polymer-based nanoparticles, alongside characterization techniques such as UV–Vis spectroscopy, FTIR, XRD, SEM/TEM, and DLS. Applications span biomedicine (antimicrobial, anticancer, drug delivery), environmental remediation, and industrial sectors (catalysis, sensing, energy storage). While green synthesis is eco-friendly and cost-effective, challenges include biological variability, lack of standardization, and scale-up limitations. Future directions focus on integrating artificial intelligence and nanobiotechnology for optimized, reproducible, and large-scale nanoparticle production. Nanobiotechnology ; Nanoparticle ; Nanochemistry ; Characterization (materials science) ; Biomedicine ; Biomimetic materials Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Water and Wastewater Treatment
Authors: Uwe Rischer
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Material Decision Spaces - AI as a Sparring Partner in Complex Material Systems This paper explores how artificial intelligence can extend the methodological boundaries of materials science by enabling coherent, information-driven discovery processes. Rather than treating AI as a tool for optimization alone, the work introduces a framework in which human expertise and machine intelligence operate as a coupled cognitive system. The focus lies on hybrid material systems and functional coatings, where complex interactions between chemistry, structure, and performance challenge classical experimental approaches. AI-assisted pattern recognition, hypothesis generation, and test-matrix design are discussed as mechanisms to reduce experimental entropy while preserving scientific control. A central contribution of this paper is the concept of coherent AI usage: AI is not positioned as an autonomous decision maker, but as a reflective system that amplifies human reasoning, domain intuition, and experimental intent. The paper outlines methodological principles, boundary conditions, and practical implications for applying AI in materials research without compromising reproducibility, interpretability, or intellectual property integrity. By bridging material science, information theory, and human-AI co-creativity, this work proposes a scalable research paradigm for future material development under increasing complexity constraints. Bridging (networking) ; Complex system ; Human intelligence ; Cognition ; Focus (optics) ; Domain (mathematical analysis) ; Entropy (arrow of time) Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Shuo Liu | Chaochao Dun | Jeffrey J. Urban | Mark T. Swihart
Source: Advanced Science (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume-Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials. Metastability ; Nanomaterials ; Scalability ; Valence (chemistry) ; Nanocrystal ; Phase (matter) ; Material Design Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Uwe Rischer
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Material Decision Spaces - AI as a Sparring Partner in Complex Material Systems This record includes a German Translation of the paper. German version: see file "Rischer_DE_Translation_WP5_2026_Entscheidungsräume_für_reaktive_Materialsysteme This paper explores how artificial intelligence can extend the methodological boundaries of materials science by enabling coherent, information-driven discovery processes. Rather than treating AI as a tool for optimization alone, the work introduces a framework in which human expertise and machine intelligence operate as a coupled cognitive system. The focus lies on hybrid material systems and functional coatings, where complex interactions between chemistry, structure, and performance challenge classical experimental approaches. AI-assisted pattern recognition, hypothesis generation, and test-matrix design are discussed as mechanisms to reduce experimental entropy while preserving scientific control. A central contribution of this paper is the concept of coherent AI usage: AI is not positioned as an autonomous decision maker, but as a reflective system that amplifies human reasoning, domain intuition, and experimental intent. The paper outlines methodological principles, boundary conditions, and practical implications for applying AI in materials research without compromising reproducibility, interpretability, or intellectual property integrity. By bridging material science, information theory, and human-AI co-creativity, this work proposes a scalable research paradigm for future material development under increasing complexity constraints. Bridging (networking) ; German ; Complex system ; Focus (optics) ; Human intelligence ; Cognition ; Entropy (arrow of time) Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Rui Zhang
Source: Rutgers University Community Repository (Rutgers University) (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Multiscale modeling of hydrogen bonds and proton behavior in molecular and materials systems Materials research is being transformed by the growing use of data-driven and computational approaches. This ongoing digital materials revolution requires integrating theoretical insights, computational modeling, and experimental studies. Computational simulations link fundamental theory with experimental observations, provide atomistic insight into structure, dynamics, and reactivity, and support the rational design of functional materials. This thesis employs multiscale modeling to investigate diverse systems, with a focus on hydrogen bonding interactions and proton behavior in condensed phases. Short hydrogen bonds are central to this work because they occur in many chemical and biological environments, including small molecules in both crystal and solution phases, as well as in proteins and protein–ligand complexes. A rare case is the formation of short hydrogen bonds in neutral liquids, such as neat mixtures of organic acids and bases. We therefore search for and predict such systems and examine their potential relevance in materials contexts. In addition, our broader interest lies in the idea that concepts well established in one field can lead to unexpected insights in another. This motivation supports our collaborations with experimental groups on general strategies for materials design, such as a conformational preorganization strategy used in polymer chemistry. In this thesis, I present my Ph.D. work, mostly carried out in close collaboration with experimental researchers, to uncover the physical mechanisms that govern these systems and to guide the rational design of new functional materials. By linking atomistic modeling with experimental observations, the results show that hydrogen bonds serve not merely as ubiquitous interactions but as structural and dynamical motifs that govern molecular organization in acid–base mixtures through extended hydrogen bond complexes, direct functional orientation and conformational preferences in polymers, and define the reactive architecture of protein active sites, which offer significant potential for the development of novel materials. This integrative approach illustrates how theory and computation complement experiment to advance predictive understanding and design of next-generation materials. Hydrogen bond ; Multiscale modeling ; Molecule ; Work (physics) ; Molecular dynamics ; Rational design ; Computational model ; Complex system Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties ; Electrostatics and Colloid Interactions
Authors: Yuya Oaki
Source: TANSO (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Synthesis and applications of graphitic structure analogs based on amorphous conjugated polymer network This article introduces our group’s recent study on a new strategy that uses amorphous conjugated polymer networks (CPNs) to develop functional nanocarbons as structural analogs of graphene and graphene oxide. Amorphous CPNs are usually synthesized by the simultaneous random copolymerization of multiple conjugated monomers, such as redox-active molecules. In amorphous CPNs, the functional unit molecules are dispersed in the network without aggregation, whereas rigid assemblies are formed in conventional main- and side-chain polymers and metal- and covalent-organic frameworks. This new state of assembly of functional molecules has structural flexibility which is advantageous for their functionalization. Because the amorphous CPNs are obtained in stacked layers, nanosheets are obtained by exfoliation. A systematic screening of the monomers was conducted based on data provided by artificial intelligence-assisted data analysis. We have reported the electrochemical uses of the amorphous CPNs containing quinone derivatives. Their excellent electrochemical performance, such as catalytic activity for the hydrogen evolution reaction and charge capacities for supercapacitors and batteries, are due to their nanostructures of the amorphous CPNs. When the conjugated networks are expanded using monomers rich in conjugated moieties, the resultant CPNs can be considered amorphous graphene and graphene-oxide analogs that can be used to design and synthesis new nanocarbon materials. Amorphous solid ; Conjugated system ; Graphene ; Monomer ; Polymer ; Electrochemistry ; Nanostructure Covalent Organic Framework Applications ; Conducting polymers and applications ; Supercapacitor Materials and Fabrication
Authors: Shuangyan Tian | Zhibo Ren | Yuting Zheng | Yì Wáng | Xin-Yu Deng | Qi Li | Fanyang Mo | Ting Lei
Source: Nature Communications (article, 2026-04-07)
Field: Materials Science — Polymers and Plastics

Transfer-learning guided design of high-performance conjugated polymers for low-voltage electrochemical transistors Artificial intelligence-driven materials development has emerged as a powerful alternative to traditional trial-and-error methods. Despite its promise, these methods often struggle to uncover novel materials or generate actionable insights in emerging fields due to limited data availability. This challenge is particularly pronounced in electronic materials, where the intricate interplay of physical mechanisms and structure-property relationships impede progress. Here, we report a methodology combining Physical-Knowledge-Undergirded Transfer Learning for accurate property prediction with limited data, coupled with physical knowledge and artificial intelligence-driven hypothesis generation to yield scientific insights. Using this approach, we successfully identify low-voltage, high-performance organic electrochemical transistor materials and yield material design knowledge. The approach is experimentally validated through the synthesis of n-type polymers, demonstrating accurate property prediction and revealing critical structure-property relationships. We believe this approach can be applied to other emerging material systems with limited data availability and complex physical mechanisms, and accelerates the development of materials in emerging fields. Transistor ; Material Design ; Scarcity ; Property (philosophy) ; Yield (engineering) ; Conjugated system Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Ren Lin | Lihui Li
Source:  (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Valley polarization in bandgap engineered WS2xSe2(1-x) monolayers: application for artificial intelligence spintronic/valleytronic devices With the rapid and remarkable progress of artificial intelligence technologies, the demand for advanced materials and novel electronic properties has increased significantly, thereby accelerating developments in cutting - edge fields such as spintronics and valleytronics. In these areas, the efficient creation and precise control of valley - spin polarization are of crucial importance, serving as fundamental requirements for practical applications in next - generation spin - based and valley - based electronic devices. In this research, high - quality WS<sub>2x</sub>Se<sub>2(1−x)</sub> monolayers with excellent crystalline properties were successfully produced using a carefully optimized chemical vapor deposition method. Detailed structural analysis confirms that the crystal lattice of the synthesized WS<sub>2x</sub>Se<sub>2(1−x)</sub> monolayers maintains perfect C₃ rotational symmetry, and the measured degree of second harmonic generation (SHG) polarization approaches near - unity values, indicating a highly ordered and symmetric atomic arrangement. Through steady - state photoluminescence (PL) characterizations, it is further demonstrated that the valley polarization degree of WS<sub>2x</sub>Se<sub>2(1−x)</sub> monolayers shows a consistent and steady increase as the Se concentration is gradually raised . The research findings clearly indicate that the SHG response from the WS<sub>2x</sub>Se<sub>2(1−x)</sub>) monolayer shows a near - unity degree of valley polarization. At the same time, continuously tunable PL valley polarization is robustly realized within the material system. These results offer valuable and potential guidance for the synthesis strategies of atomically thin alloy material systems, which are highly suitable for integration into innovative spin - based and valley - based electronic devices, particularly in artificial intelligence applications that prioritize efficient energy usage, low - power logic operations, and high - density data storage technologies. Polarization (electrochemistry) ; Applications of artificial intelligence ; Band gap 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Chalcogenide Semiconductor Thin Films
Authors: Yanjing Liu | Wanyu Tang | Ke Liu | sen Wang | Jiayi Xu | Yuhong Guo
Source: Carbohydrate Polymers (article, 2026-03-23)
Field: Chemistry — Organic Chemistry

Quaternized polysaccharides: A focused review on molecular engineering, antimicrobial mechanisms, and advanced applications Antimicrobial ; Rational design ; Alkyl ; Antibiotic resistance ; Cationic polymerization ; Chemical modification Antimicrobial agents and applications ; Hydrogels: synthesis, properties, applications ; Biopolymer Synthesis and Applications