jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongqing Zhou | Mei Yan | Hosoowi Lee | Juyoung Yoon
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Recent advances in small-molecule fluorescent probes for simultaneous dual detection of HClO/ClO <sup>−</sup> and analytes/microenvironment Hypochlorous acid/hypochlorite (HClO/ClO<sup>-</sup>) is one of the most crucial reactive oxygen species that exert effective antibacterial effects in living organisms. Real-time monitoring of HOCl/ClO<sup>-</sup> changes in the local regions will help to further reveal its biological functions. However, the alterations of HClO/ClO<sup>-</sup> and other analytes/microenvironment often fluctuate simultaneously in the occurrence and progress of certain diseases. That is to say, a large number of pathological processes are controlled by various biological species and microenvironment. Therefore, exploring the interactive relationship and co-regulation mechanism between HClO/ClO<sup>-</sup> and analytes/microenvironment is vital. Fluorescence imaging techniques have been broadly applied in detecting HClO/ClO<sup>-</sup> and analytes/microenvironment in biological systems due to their outstanding performance, including non-invasiveness, <i>in situ</i> capability, and high spatial-temporal resolution. Fluorescent probes have been considered as one of the most efficient instruments for studying molecular events. Herein, this review offers a comprehensive statement of dual-responsive small-molecule fluorescent probes for simultaneous monitoring of HClO/ClO<sup>-</sup> and analytes/microenvironment. Furthermore, the levels, distributions and relationship of HClO/OCl<sup>-</sup> and analytes were revealed in depth, especially latent functions in numerous diseases. In particular, this review may offer valuable references for the preparation and application of simultaneous dual-detection fluorescent probes in the future. Fluorescence ; Hypochlorous acid ; Analyte ; Mechanism (biology) ; Dual (grammatical number) Molecular Sensors and Ion Detection ; Sulfur Compounds in Biology ; Neutrophil, Myeloperoxidase and Oxidative Mechanisms
Authors: Yuta Makihara | Takashi Kubo | Tomohiko Nishiuchi
Source: ChemRxiv (article, 2026-03-16)
Field: Chemistry — Organic Chemistry

Strain-Driven Open-Shell Emergence in a Carbon Nanoring Controlling open-shell character in aromatic π-conjugated hydrocarbons is central to the development of magnetic, optoelectronic, and quantum materials. Conventional strategies rely on Clar 's sextet redistribution or steric protection to stabilize singlet diradicals, inherently limiting accessible molecular frameworks. Here we demonstrate a conceptually distinct approach in which geometric strain alone induces open-shell emergence. By embedding anthracene, normally a closed-shell aromatic unit, into carbon nanoring, bending at the 9,10-positions disrupts π-bonding and promotes diradical formation. We synthesized two nanorings, [2.4]-and [2.8]cyclo(9,10)anthrylene paraphenylene (CAPP). Structural analysis shows that [2.8]CAPP adopts a planar, closed-shell configuration, whereas enhanced strain localization in [2.4]CAPP generates bent anthracene units and an open-shell electronic structure equivalent to a cyclic Chichibabin's hydrocarbon dimer. This work establishes strain-driven electronic reorganization as a general design principle for open-shell π-systems beyond traditional stabilization paradigms. Diradical ; Steric effects ; Bent molecular geometry ; Limiting ; Electronic structure ; Anthracene ; Redistribution (election) ; Quantum ; Singlet state Synthesis and Properties of Aromatic Compounds ; Organoboron and organosilicon chemistry ; Covalent Organic Framework Applications
Authors: Shangchu Shi | Rui Yang | Ruifan Huang | Yizhen Lin | Jie Li | Xunjun He | M. -Zheng Zhang | Sergey Maksimenko
Source: Research Square (preprint, 2026-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiple Wavefront Shaping Enabled by Linear Polarization Multiplexing Reflective Metasurfaces Wavefront ; Terahertz radiation ; Polarization (electrochemistry) ; Multiplexing ; Reflection (computer programming) ; Cardinal point ; Reflection coefficient Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Random lasers and scattering media
Authors: Annika Huber | Alessandro Prescimone | Christof Sparr | Oliver S. Wenger
Source: Angewandte Chemie International Edition (article, 2026-01-27)
Field: Chemistry — Organic Chemistry

Enantiospecific Optical Sensing of Terpenes by an Aggregated Atropisomeric Platinum(II) Complex Terpenes are unfunctionalized small volatile organic compounds (VOCs) that are naturally abundant, relevant to climate change, and impose potential health risks. Herein, we report a conceptually novel approach for the enantiospecific recognition of terpenes and other VOCs based on a square planar platinum(II) complex with an atropisomeric ligand. This molecular design results in chiral aggregated nanostructures caused by weak π-π- and metal-metal interactions with characteristic UV-vis absorption bands. Distinct UV-vis absorption changes are induced by weak intermolecular interactions with the enantiomers of diverse VOCs (2-butanol, 1-phenylethanol, α-pinene, and limonene) leading to perturbations of the chiral aggregates. This allows an enantiospecific, reversible detection of VOCs by UV-vis absorption spectroscopy in nonpolar solution. The study provides a new working principle for enantiospecific recognition with artificial optoelectronic noses, which are particularly promising for determining the enantiomers of unfunctionalized volatile organics. Terpene ; Enantiomer ; Absorption (acoustics) ; Intermolecular force ; Absorption spectroscopy ; Spectroscopy ; Intermolecular interaction Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Axial and Atropisomeric Chirality Synthesis
Authors: Eric Amoateng | Ellis Mubarak Mubarak Sani | Kingsford Sarkodie Obeng Kwakye | Alexandros Pitilakis
Source: Photonics (article, 2026-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analysis and Design of a Hybrid Graphene/Vanadium-Dioxide Terahertz Metasurface with Independently Reconfigurable Reflection Phase and Magnitude A reconfigurable THz metasurface (MS) capable of independent reflection amplitude and phase modulation is designed and analyzed. The tunability is achieved in a simple few-layer structure by control over the chemical potential of a graphene monolayer patterned in square patches and over the bulk conductivity of an overlying vanadium dioxide (VO2) patch array; these impart control over the reflection phase and magnitude, respectively. To design and analyze the MS unit cell, we employ intuitive equivalent circuit and transmission line modeling, which is validated against full-wave simulations, showing good agreement in the regime of interest, i.e., on the first resonance for normal plane wave incidence. The simulated phase modulation approaches 250°, enabling binary-encoded digital metasurface designs, while the magnitude modulation spans more than 20 dB, from −3 dB almost down to perfect absorption. The flexibility of dynamic phase and amplitude control can unlock the full potential of such THz MS hybrid designs for future wireless communications (6G and beyond) and for sensing applications. Finally, the analytical modeling can be extended to polarization-dependent, anisotropic, or non-local EM responses and/or to include aspects of the multiphysical control mechanisms. Terahertz radiation ; Reflection (computer programming) ; Phase modulation ; Phase (matter) ; Modulation (music) ; Amplitude modulation ; Amplitude ; Reflection coefficient ; Stacking ; Continuous phase modulation Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Wireless Communication Technologies
Authors: Ivan Moreno | J. Carlos Basilio-Ortiz
Source: arXiv (Cornell University) (article, 2026-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Aperiodic metalenses: intrinsically near-achromatic visible focusing with identical nanocylinders Conventional metalenses control light by varying meta-atom geometry, a design strategy that inherently couples phase modulation to structural dimensions and exacerbates chromatic dispersion. Here, we break this paradigm by decoupling phase control from meta-atom geometry. We introduce an aperiodic metalens architecture composed exclusively of structurally identical dielectric nanorods, where full 2π phase coverage is achieved solely through local periodicity modulation. We theoretically demonstrate that this geometric invariance yields a linear effective-refractive-index scaling that intrinsically satisfies the dispersive condition required for near-achromatic focusing. Operating in the visible spectrum, our aperiodic designs (moderate and high numerical apertures of 0.4 and 0.8) reveal a passive suppression of chromatic aberration. Compared to conventional size-variant designs, our aperiodic approach reduces longitudinal chromatic focal shift by nearly 42% and maintains superior spectral efficiency, yielding tighter, diffraction-limited focal spots. By relying on a single, fabrication-tolerant nanostructural building block, this approach offers a highly simplified and scalable route toward next-generation broadband metasurfaces. Aperiodic graph ; Decoupling (probability) ; Chromatic scale ; Phase modulation ; Scaling ; Phase (matter) ; Broadband ; Modulation (music) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Jiangcheng Xu | Silong Zhai
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis While photocatalysis has emerged as a transformative tool in modern synthesis, AI-assisted reaction prediction faces significant challenges due to data limitations. We present PhotoCatDB - a curated, open-source database containing 26.7K photocatalytic reactions with detailed mechanistic annotations, including 9.2K multicomponent transformations. Leveraging this resource alongside 100 million molecular data points, we developed PhotoCat, a Transformer-based platform that achieves unprecedented accuracy in photocatalytic reaction prediction (82.6%), retrosynthesis (77.1%), and condition recommendation (88.5%). The platform’s capabilities were experimentally validated through the discovery of four novel photocatalytic reactions with yields up to 75.3%. This integrated approach establishes a new paradigm for data-driven innovation in photocatalysis, bridging computational prediction with experimental validation to accelerate discovery in sustainable chemistry. Photocatalysis ; Transformative learning ; Bridging (networking) ; Reaction conditions ; Resource (disambiguation) ; Sustainable production Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Radical Photochemical Reactions
Authors: Yves Grandjean | David Kreutter | Jean-Louis Reymond
Source: Open Access CRIS of the University of Bern (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Data augmentation in a triple transformer loop retrosynthesis model. Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits their usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM → P reaction. Finally, we validated the prediction by requesting a high confidence prediction (&gt;95%) for the prediction of P from SM + R by TTL transformer T3. We generated up to 5000 reactions per template, resulting in 27.5m validated fictive reactions covering the chemical space of the original USPTO dataset. To exemplify the use of this dataset, we demonstrate that a single-step retrosynthesis transformer model trained on a template equilibrated subset of 1 097 374 fictive reactions outperforms the corresponding model trained on USPTO reactions only. Retrosynthetic analysis ; Transformer ; Template ; Quadratic equation ; Autotransformer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Heewoong Noh | Gyoung S. Na | Namkyeong Lee | Chanyoung Park
Source: Open MIND (preprint, 2026-02-07)
Field: Materials Science — Materials Chemistry

MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning Material synthesis planning (MSP) remains a fundamental and underexplored bottleneck in AI-driven materials discovery, as it requires not only identifying suitable precursor materials but also designing coherent sequences of synthesis operations to realize a target material. Although several AI-based approaches have been proposed to address isolated subtasks of MSP, a unified methodology for solving the entire MSP task has yet to be established. We propose MSP-LLM, a unified LLM-based framework that formulates MSP as a structured process composed of two constituent subproblems: precursor prediction (PP) and synthesis operation prediction (SOP). Our approach introduces a discrete material class as an intermediate decision variable that organizes both tasks into a chemically consistent decision chain. For SOP, we further incorporate hierarchical precursor types as synthesis-relevant inductive biases and employ an explicit conditioning strategy that preserves precursor-related information in the autoregressive decoding state. Extensive experiments show that MSP-LLM consistently outperforms existing methods on both PP and SOP, as well as on the complete MSP task, demonstrating an effective and scalable framework for MSP that can accelerate real-world materials discovery. Bottleneck ; Class (philosophy) ; Scalability ; Task (project management) ; Process (computing) ; Unified Model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Heewoong Noh | Gyoung S. Na | Namkyeong Lee | Chanyoung Park
Source: ArXiv.org (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning Material synthesis planning (MSP) remains a fundamental and underexplored bottleneck in AI-driven materials discovery, as it requires not only identifying suitable precursor materials but also designing coherent sequences of synthesis operations to realize a target material. Although several AI-based approaches have been proposed to address isolated subtasks of MSP, a unified methodology for solving the entire MSP task has yet to be established. We propose MSP-LLM, a unified LLM-based framework that formulates MSP as a structured process composed of two constituent subproblems: precursor prediction (PP) and synthesis operation prediction (SOP). Our approach introduces a discrete material class as an intermediate decision variable that organizes both tasks into a chemically consistent decision chain. For SOP, we further incorporate hierarchical precursor types as synthesis-relevant inductive biases and employ an explicit conditioning strategy that preserves precursor-related information in the autoregressive decoding state. Extensive experiments show that MSP-LLM consistently outperforms existing methods on both PP and SOP, as well as on the complete MSP task, demonstrating an effective and scalable framework for MSP that can accelerate real-world materials discovery. Bottleneck ; Class (philosophy) ; Scalability ; Task (project management) ; Process (computing) ; Unified Model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: David K. Smith
Source: Journal of Chemical Education (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Creating a Podcast Using Generative Artificial Intelligence to Support Student Learning of Organic Reaction Mechanisms A podcast was created by Google NotebookLM artificial intelligence and applied to support learning in a Year 1 university organic chemistry course. The Generative AI (GenAI) was trained using an open access article presenting priority and selectivity rules for organic reaction mechanisms. The software performed very well in terms of background material, showing creativity. Coherent explanations of organic chemistry were also given, although some technical errors were made in chemical detail and there was some pedagogic confusion. The source material was edited and two further podcast versions created. All three podcast outputs were combined with academic curation to create a final output with minimal errors. The podcast was released to students, being advertised as AI-generated support material. Students who listened to the podcast reported it helped reinforce learning and was worth listening to. However, a number of students were concerned about ethical, societal, and sustainability concerns of using AI, and some actively chose not to engage with it. In summary, with academic curation, Google NotebookLM can make engaging scientific podcasts with limited workload. However, student attitudes to the use of AI in education must be carefully considered. Active listening ; Generative grammar ; Software ; Active learning (machine learning) ; Higher education ; Generative model Machine Learning in Materials Science ; Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Sampa Mondal | Sudipta Ghara | Sk Abulkalam Azad | Paritosh Barik | Prasenjit Sen | Nayim Sepay | Prasanta Patra | Subrata Mahanta | Laksmikanta Adak | Shubhankar Samanta
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Directing group-assisted C–H functionalization of 2-arylbenzo[ <i>f</i> ]isoquinoline: a robust route to decumbenine B analogues with photophysical applications An efficient transition metal-catalysed regioselective short synthetic route to Decumbenine B analogues is prepared from the green precursor 2-arylbenzo[<i>f</i>]isoquinoline (obtained <i>via</i> urea-promoted neat reaction). The single precursor capable of introducing -CH<sub>2</sub>OH, -X(-Br/-Cl), -SePh groups <i>via</i> Ru/Pd/Cu-catalyzed C-H functionalization. Theoretical and experimental investigations confirmed that Pd(ii) and Cu(ii) preferentially interact with the isoquinoline precursor to drive halogenation and selenation, respectively, while Ru(ii) effectively catalyzes the hydroxymethylation reaction. The protocol is extended to newly synthesized fluorescent molecules that exhibit aggregation-induced emission (AIE) and toxic compound sensing properties. Consequently, this approach assists us in accessing a diverse array of naturally occurring analogues and facilitates more efficient and effective biological studies with minimal effort. Isoquinoline ; Regioselectivity ; Surface modification ; Halogenation ; Fluorescence Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Synthesis and pharmacology of benzodiazepine derivatives
Authors: Ch. M. Seyidova | N. T. Shikhverdiyeva | H. F. Aslanova | N. T. Rahimli | N. A. Zeynalov | D. B. Tagiyev | F. C. Amiraslanova | I. V. Shikhverdiyev
Source: Chemical Papers (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Artificial intelligence in polymer chemistry: opportunities and challenges Transformative learning ; Process (computing) ; Applications of artificial intelligence ; Key (lock) ; Polymer ; Smart polymer Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Parinaz Mofazali | Minoosh Lalinia | Jeffrey D. Gross | Ali Samadi
Source: Coordination Chemistry Reviews (article, 2026-03-11)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted polymers: applications, computational approaches, and the transformative role of artificial intelligence Molecularly imprinted polymer ; Transformative learning ; Fidelity ; Polymerization ; Applications of artificial intelligence Analytical chemistry methods development ; Covalent Organic Framework Applications ; Hydrogels: synthesis, properties, applications
Authors: Jie Zheng | Yuanlv Ye
Source: Polymers (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Review of Polymer Drug Therapy for Cancer Driven by Artificial Intelligence This review systematically evaluates the interdisciplinary convergence of artificial intelligence (AI) and polymer science in cancer therapy. Beyond mere description, we provide an integrated framework spanning synthetic optimization, biocompatibility prediction, and the design of tumor microenvironment (TME)-responsive carriers. We highlight how AI algorithms (ML, DL, and RNNs) transform traditional trial-and-error methods into a data-driven paradigm, enabling precise spatiotemporal drug release and individualized pharmacokinetic modeling. Crucially, this work addresses the critical gap between computational modeling and clinical realization by providing a balanced critical analysis of current bottlenecks, including the "small data" challenge, publication bias, and regulatory hurdles. We conclude with a roadmap for AI-guided precision oncology, shifting the focus from predictive accuracy to mechanistic interpretability and prospective in vivo validation. Interpretability ; Cancer therapy ; Cancer ; Applications of artificial intelligence ; Realization (probability) ; Drug ; Clinical trial Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Radiomics and Machine Learning in Medical Imaging
Authors: Huan Liu
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Original review of quantum chemistry and 3D modeling of artificial intelligence This is the post-print. Formally published on 21/11/2023; Latest revised on 13/03/2026. Reference: Liu Huan (2023). Original review of quantum chemistry and 3D modeling of artificial intelligence. Journal of Quantum Physics and Materials Chemistry (ISSN2958-4027) . 2023 (11). https://doi.org/10.58473/JQPMC0013 All Copyrights Reserved. Quantum ; Quantum chemistry ; Quantum computer ; Applications of artificial intelligence ; Artificial life Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Physical and Chemical Molecular Interactions