jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengang Lu | Bing Luo | Jiubin Tan
Source: Light Advanced Manufacturing (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-long focal depth annular lithography for fabricating micro ring-shaped metasurface unit cells on highly curved substrates In recent years, metasurfaces on planar substrates have been extensively investigated and methods for their fabrication have been implemented.However, fabricating metasurfaces on highly curved surfaces remains challenging because of the difficulty in achieving precise mechanical positioning on curved geometries using current lithographic techniques.This limits applications that require finer and more accurate structures.This paper introduces a novel lithographic approach for patterning structures on curved surfaces.By leveraging the natural aberration of a convex lens to focus the beams, this approach enables the creation of adjustable ring and split-ring configurations.Ring-shaped patterns with an average structural width of 1.79 m were exposed, exceeding the resolution of previously reported annular lithography techniques by a factor of 10.Moreover, this approach offers a defocus tolerance that is 10 times greater than that of conventional direct laser writing lithography, thus reducing the influence of positional errors caused by substrate geometry.Consequently, patterns on a photoresist-coated dome were successfully exposed, marking a pioneering achievement.This study paves the way for creating ring-shaped metasurfaces and other structures on highly curved surfaces. Lithography ; Materials science ; Optics ; Ring (chemistry) ; Optoelectronics ; Nanotechnology ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics
Authors: Sen Wu | Yan Xu | Dominic J. Black | Aidan P. McKay | David B. Cordes | Róbert Pál | Eli Zysman‐Colman
Source: ChemRxiv (preprint, 2025-07-30)
Field: Chemistry — Organic Chemistry

Two-Photon Excited Circularly Polarized Luminescence of Hetero[9]helicene with Multiresonant Thermally Activated Delayed Fluorescence The development of organic semiconductors exhibiting strong CPL, 2PA, high photoluminescence quantum yield, and long-lived delayed fluorescence would be of significant value as deep tissue penetrating bioimaging agents showing high resolution. Although examples of organic CP-TADF emitters and 2PA-TADF emitters have each been documented and exploited in CP-OLEDs and in biological imaging, respectively, the design of 2PE-CPL-TADF organic compounds remains unprecedented, due to their low PL dissymmetry factor (gPL) and small 2PA σ2. In this work, we designed and synthesized two systems, DDCzBPh and DDCzBPy, consisting of two annulated MR-TADF units to a central naphthyl or quinazinyl group, respectively, which exhibit large |gPL| and σ2 values, high PL, and long-lived delayed fluorescence. We demonstrate for the first time two-photon CPL of TADF emitters, with their circularly polarized brightness above 0.1 GM, demonstrating their potential for biological imaging. Helicene ; Fluorescence ; Luminescence ; Excited state ; Two-photon excitation microscopy ; Photon ; Photochemistry ; Materials science ; Optoelectronics ; Chemistry ; Optics ; Physics ; Atomic physics ; Organic chemistry ; Molecule Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials
Authors: Angela Punzi | Tobias Ullrich | Michele Orza | Davide Mesto | Anna Moliterni | Vincent Oliéric | Sylvain Engilberge | Cinzia Giannini | Fabrizia Negri | Dirk M. Guldi | Davide Blasi | Gianluca M. Farinola
Source: Angewandte Chemie International Edition (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Twist and Shine: The Impact of Halogen Substitution on Thiele Hydrocarbon's Optical Properties In this work, two new mixed-halide Thiele hydrocarbons were synthesized and characterized to elucidate the influence of halogenation patterns on their photophysical properties, addressing the role exerted by steric constraints and electronic effects. Interestingly, their interplay governed a unique spectroscopic behavior that is driven by pronounced geometric rearrangements upon photoexcitation. Quantum-chemical calculations revealed that derivatives with limited diradical character, and correspondingly shorter exocyclic C═C bonds, are likely to adopt a folded boat-like conformation in the ground state, a structural motif not previously observed in Thiele hydrocarbons. Upon photo-excitation, these exocyclic bonds were subject to a significant elongation, facilitating mixing between the bright singly excited (SE) state and the dark, doubly excited (DE) state. These interactions enabled the formation of a zwitterionic excited state, a finding that is consistent with recent observations for halogenated derivatives. Transient absorption spectroscopy confirmed this mechanism, which provides a promising strategy for designing fluorophores with exceptionally large Stokes shifts (more than 2 eV) and tailored photophysical and electronic properties. In addition, a pronounced mechanofluorochromic response has been observed for the first time in the case of folded derivative, opening the way to the use of these species as multi-stimuli-responsive molecular materials. Excited state ; Diradical ; Steric effects ; Ultrafast laser spectroscopy ; Twist ; Halogen ; Spectroscopy ; Absorption spectroscopy ; Molecule Luminescence and Fluorescent Materials ; Crystallography and molecular interactions ; Synthesis and Properties of Aromatic Compounds
Authors: Richard Rudolf | Andrej Todorovski | Vera Lederer | Nicolás I. Neuman | Hartmut Schubert | Biprajit Sarkar
Source: Angewandte Chemie (article, 2025-01-16)
Field: Chemistry — Organic Chemistry

Ein Anionisches, Mesoionisches Carben (anMIC) und seine Umwandlung in ein Metallo MIC‐Boran: Synthese und Eigenschaften Abstract Neutrale mesoionische Carbene (MICs) basierend auf 1,2,3‐Triazolen haben einige Forschungsfelder wie der homogenen Katalyse, Elektrokatalyse und Photochemie/Photophysik nachhaltig vorangebracht. Mit dieser Arbeit präsentieren wir den ersten generellen Syntheseweg zu anionischen mesoionischen Carbenen (anMICs, 2 ) basierend auf einem 1,2,3‐Triazol und einem Borat‐Rückgrat. Das freie anMIC ist in Lösung unter inerten Bedingungen und tiefen Temperaturen stabil und kann für mehrere Wochen in Lösung gelagert werden. Die Analyse der Donor‐Eigenschaften zeigt die starke σ ‐Donor‐Fähigkeiten von anMICs, die die Donorqualitäten anderer starker Donoren wie etwa MICs, NHCs, anionischer NHCs oder N‐heterozyclischer Olefine übersteigt. Bei Raumtemperatur kann das anMIC in drei andere, gleichwohl interessante Verbindungsklassen überführt werden: Ein Amid‐koordiniertes Boran basierend auf einem MIC‐Boran ( 2BR 3 ), ein polymeres Triazolid ( 1Li ) und ein Amid‐koordiniertes metallo‐MIC‐Boran ( 3Li ). Dieses Metallo‐MIC‐Boran ist ein interessantes Synthon für die Synthese weiterer Amid‐koordinierter MIC‐Borane. Quantenchemische Rechnungen konnten herangezogen werden um diese Umwandlung von anMICs näher zu beleuchten. Somit führen wir mit diesem Bericht drei neue Klassen mesoionischer Verbindungen mit großem Potential in Teilgebieten der Chemie und benachbarten Disziplinen ein. Mesoionic ; Boranes ; Carbene ; Chemistry ; Transformation (genetics) ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Boron ; Catalysis N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; Organoboron and organosilicon chemistry
Authors: Giuseppe Emanuele Lio | Simone Zanotto
Source: Discover Materials (article, 2025-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of GST phase only pixel metasurfaces for beam steering at optical telecom wavelength Metasurfaces are pivotal in developing miniaturized beam steerers and optical elements capable of precise light manipulation. This work details the design and optimization of tunable reflective metasurfaces based on the phase-change material GeSeTe (GST) for beamforming applications at the telecom wavelength of 1550 nm. The proposed metasurface architecture integrates multi-layered gold and GST structures on a silicon substrate, engineered to achieve almost pure phase control with minimal spurious loss modulation. Utilizing rigorous coupled wave analysis (RCWA) simulations combined with a tailored optimization function, we identified key geometric parameters that maximize reflectance while ensuring a $$\pi$$ -phase delay between the amorphous and crystalline states of GST. The far-field beam steering capabilities were validated through finite element method (FEM) simulations for both binary and ternary phase modulation configurations, demonstrating precise angular control and reduced side lobes. Furthermore, a comparative evaluation of various chalcogenide PCM candidates – assessing switching energy, speed, and optical properties – confirmed GST as the most suitable option, despite its relatively higher extinction coefficient. Finally, to assess its practical viability, we have performed further extensive technical analyses, including a RCWA study of fabrication tolerance, a characterization of the device polarization dependence, and simulations for its thermal management and cross-talk of adjacent pixels. These findings pave the way for high-speed, low-energy, and finely controllable optical beam steering devices, crucial for next-generation photonic systems such as LIDAR and advanced optical transceivers. Beam steering ; Photonics ; Fabrication ; Rigorous coupled-wave analysis ; Beamforming ; Optical switch ; Wavelength ; Optical filter ; Broadband Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Advanced Antenna and Metasurface Technologies
Authors: Hyunwook Jung | Myung‐Joon Kwack | Younghyun Kim
Source: Research Square (preprint, 2025-04-21)
Field: Materials Science — Surfaces, Coatings and Films

Simulation study of a highly efficient dual-apodized SiN Grating Coupler using a metal bottom reflector on a Glass Substrate for Co-Packaged Optics Apodization ; Reflector (photography) ; Grating ; Materials science ; Optics ; Substrate (aquarium) ; Dual (grammatical number) ; Optoelectronics ; Physics ; Light source ; Geology Optical Coatings and Gratings ; Photonic and Optical Devices ; Advanced Surface Polishing Techniques
Authors: Annie M. Westerlund | Lukas M. Sigmund | Christos Kannas | Samuel Genheden | Mikhail Kabeshov
Source: ChemRxiv (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Toward lab-ready AI synthesis plans with protection strategies and route scoring Workflow ; Automation ; Quality (philosophy) ; Control (management) ; Tree (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Tatsuya Sagawa | Ryosuke Kojima
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Additional file 1 of ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data Additional file 1. Transformer ; Experimental data ; Data file Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Sagawa, Tatsuya | Kojima, Ryosuke
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Additional file 1 of ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data Additional file 1. Transformer ; Experimental data ; Data file Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: L. Zhu | Jing Wang | Jiashuang Huang | Yifan Jiang | Shu Jiang
Source: Artificial Intelligence in Health (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

M2Echem: A multilevel dual encoder-based model for predicting organic chemistry reactions Chemical reaction prediction is a vital application of artificial intelligence. While Transformer models are widely used for this task, they often overlook deeper-level semantic information. In addition, the traditional Transformer model suffers from a decline in prediction performance and shows poor generalization when faced with different representations of the same molecule. To address these challenges, we propose a dual encoder-based reaction prediction method tailored for multilevel organic chemistry. Our approach began with the introduction of synergistic dual-encoder architecture: The atomic encoder focused on inter-atomic attention weights. In contrast, the molecular encoder employed a molecular maximum dimension reduction algorithm to identify key chemical features. We then performed multilevel feature fusion by combining the outputs from both the atomic and molecular encoders. Finally, we applied an optimized contrast loss to enhance the model&amp;rsquo;s robustness. The results indicated that this method outperformed existing models across all four datasets, significantly improving generalization performance and contributing to advancements in artificial intelligence-driven drug development and research. Encoder ; Computer science ; Robustness (evolution) ; Artificial intelligence ; Generalization ; Transformer ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Theoretical computer science ; Chemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Xiao Hu (233579) | Ziqi Chen (805970) | Daniel Adu-Ampratwum (2588605) | Xia Ning (2121487)
Source: Figshare (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

log-RRIM: YieldPrediction via Local-to-Global ReactionRepresentation Learning and Interaction Modeling Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. A key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM also employs a local-to-global reaction representation learning strategy. This approach first captures detailed molecule-level information and then models and aggregates intermolecular interactions. Through this hierarchical process, log-RRIM effectively captures how different molecular fragments contribute to and influence the overall reaction yield. log-RRIM shows superior performance in our experiments, especially for medium-to-high-yielding reactions, proving its reliability as a predictor. The framework’s sophisticated modeling of reactant–reagent interactions and precise capture of molecular fragment contributions makes it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://github.com/ninglab/Yield_log_RRIM. Representation (politics) ; Feature (linguistics) ; Graph ; Yield (engineering) ; Reliability (semiconductor) ; Chemical reaction ; Key (lock) ; Reaction mechanism ; Intermolecular force Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Tuan Vinh | Tao Li | Carl Yang | Monika Raj
Source: Synlett (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Beyond Prediction: AI as a Creative Partner in Organic Synthesis Abstract Artificial intelligence (AI) has rapidly progressed from being a niche curiosity to becoming a real force in the chemical sciences. In organic synthesis, however, AI’s role is still mostly viewed in two ways: helping automate retrosynthetic planning and speeding up reaction optimization. These advances—like the retrosynthesis engine developed by Segler et al. (2018)1 and the optimization workflows from Doyle’s group (Shields et al., 2021)2—have been game-changing in specific cases. But if we see AI only in these roles, we risk overlooking its bigger potential. Retrosynthetic analysis ; Curiosity ; Workflow ; Organic synthesis ; Expert system Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Suyash Utekar | Pranav Pillai | Aditya Randive | Varsha Patil
Source:  (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

TransBioRetro: A Transformer-Based Self Correcting Beam Search Framework for Bio Retrosynthesis Prediction By utilizing enzyme-mediated modifications, bio retrosynthesis offers a reasonable, maintainable, and organically primarily based method of creating target particles as an optional to routine natural blend. This technique features a parcel of guarantee for utilize in regions like prescription drugs, strong point chemical compounds, and biofuels. In any case, problems like response versatility, vague product-precursor intuitive, and a want of excessive best biochemical response records confine its greater great appropriateness. A transformer-based totally retrosynthesis show, contrastive learning, recursive multi-step questioning, and a modern self-corrected bar appearance device are all mixed in our crossover profound studying gadget to triumph over these downsides. The strong scope of enzymatic and ordinary object modifications is made plausible via the coaching of the model on a cautiously chosen dataset that consolidates each biosynthetic and herbal responses from KEGG, MetaCyc, MetaNetX, and USPTO-NPL assets. A contrastive goal primarily based on the NT-Xent misfortune boosts the version's ability to distinguish authentic precursor-product combinations from chemically implausible ones. while the recursive multi-step retrosynthesis method breaks down target atoms into acknowledged bio-constructing portions, the self-correcting bar appearance powerfully re-ranks candidates primarily based on learnt trade embeddings amid deduction. The invalid Grins charge, top-N exactness, and BLEU score are applied to very well compare the version's execution, and all of them illustrate apparent alternatives over fashionable techniques. via bridging the hollow among AI-pushed questioning and usual retrosynthetic cause, this research progresses robotized bioretrosynthesis' versatility and steadfastness. Retrosynthetic analysis ; Scope (computer science) ; Object (grammar) ; Point (geometry) ; Crossover ; Categorical variable ; Expert system Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Microbial Natural Products and Biosynthesis
Authors: Li, Xinyi | Wang, Sai | Lin, Yutian | Wu, Yu | Yang, Yi
Source: arXiv (Cornell University) (preprint, 2025-08-14)
Field: Materials Science — Materials Chemistry

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis Retrosynthesis prediction aims to infer the reactant molecule based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing models rely on static pattern-matching paradigm, which limits their ability to perform effective logic decision-making, leading to black-box decision-making. Building on this, we propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary reasoning strengths of Large Language Models and specialized models via reinforcement learning. It outputs natural language explanations grounded in chemical logic through three components: (1) specialized models analyze the product to construct high-quality chemical decision space, (2) LLM-driven critical reasoning to generate predictions and corresponding interpretable reasoning path, and (3) reinforcement learning optimizing interpretable decision policy. Experiments show that Retro-Expert not only surpasses both LLM-based and specialized models across different metrics but also provides expert-aligned explanations that bridge the gap between AI predictions and actionable chemical insights. Retrosynthetic analysis ; Construct (python library) ; Task (project management) ; Bridge (graph theory) ; Reinforcement learning ; Product (mathematics) ; Natural language Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Thomas Duigou | Philippe Meyer | Jean‐Loup Faulon
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

RetroRules 2026: multi‑dataset combining chemistry and biochemistry for reaction templates RetroRules (https://retrorules.org) is a unique compendium of reaction templates, which are generic abstractions of biochemical transformations, supporting pathway discovery, reaction prediction and enzyme engineering. The 2026 release updates biochemical sources (MetaNetX, Rhea), newly integrates chemical sources (USPTO), expands template radii to 0–10 to control specificity, adopts implicit-hydrogen and minimal atom primitive encoding for faster, more compact rules, and enables templates from mass-imbalanced reactions increasing coverage of biochemically relevant entries. Atom–atom mapping transitions to the transformer-based rxnmapper, improving mapping accuracy. The RetroRules 2026 release comprises 1,062,364 templates from 88,425 curated source reactions, with coverage across 7,391 fourth-level EC classes. A redesigned website, an updated Online Template Generator, and an OpenAPI-defined API enable multi-criteria exploration (dataset/radius/EC), deduplicated template views, and convenient exports (dataset-specific TSV/CSV/JSON bundles). Sequence annotations are refreshed from UniProt and summarized as a normalized sequence-support score to guide ranking. Together, these RetroRules evolutions provide a cross-domain resource bridging biochemistry and organic chemistry with broader coverage, controllable specificity, and better usability for high-throughput pathway design, reaction prediction and enzyme engineering. Template ; UniProt ; Compendium ; Bridging (networking) ; Usability ; Enzyme Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Metabolic Engineering and Bioproduction