jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hikaru Abe | Kazushige Sasaki | Chiharu Sakashita | Shunsuke Watanabe | Hidetoshi Noda | Takumi Watanabe | Masakatsu Shibasaki
Source: Organic Letters (article, 2025-12-04)
Field: Chemistry — Organic Chemistry

Catalytic Asymmetric Benzoin Condensation for the Synthesis of Natural Products and Their Analogs Catalytic enantioselective benzoin condensation conditions that are potentially useful for the total synthesis of cycloimidamicins and structure-activity relationship studies of these natural products were developed. A triazole-based NHC catalyst promoted the transformation to afford the cyclization products. To make the reactions applicable to substrates with complex structural features, a Lewis acidic additive, B(OMe)Et<sub>2</sub>, was used, but fine-tuning of the base and solvent conditions was required to attain a good chemical yield and excellent enantioselectivity. These conditions showed a broad substrate scope and were not limited to substrates useful for the synthesis of cycloimidamicins and their related compounds. This study also clearly demonstrates the utility of N-heterocyclic carbene organocatalysts that have not been reported to exhibit perfect enantioselectivity in intramolecular benzoin condensation. Benzoin ; Catalysis ; Enantioselective synthesis ; Yield (engineering) ; Substrate (aquarium) ; Intramolecular force ; Carbene ; Condensation N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Cyclopropane Reaction Mechanisms ; Asymmetric Synthesis and Catalysis
Authors: Singh, Manish | Kaifl, Md | Akthar, Md Nawed | Gupta, Nakul | Sagar, Preet
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-31)
Field: Chemistry — Organic Chemistry

Role of Heterocycles in Drug Discovery: An Overview Heterocycles represent one of the most important classes of chemical scaffolds in modern drug discovery. Their widespread presence in natural products and synthetic drugs underscores their versatility and biological significance. More than half of all approved small-molecule drugs feature at least one heterocyclic ring, reflecting their ability to fine-tune pharmacological and physicochemical properties. In this review, we highlight the contribution of different classes of heterocycles including five- and six-membered rings, non-aromatic systems, and fused heterocycles to medicinal chemistry. Their applications across therapeutic areas such as oncology, infectious diseases, central nervous system (CNS) disorders, and antiviral therapy are discussed. Finally, we outline recent advances in rational drug design, computational methods, and emerging trends such as targeted therapy, nanotechnology, and personalized medicine, emphasizing the continued importance of heterocycles in shaping the future of therapeutics. Drug ; Drug discovery ; Feature (linguistics) ; Drug design Synthesis and Reactivity of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Pyrroles
Authors: Manish Pratap Singh | Md Kaifl | Md Nawed Akthar | Nakul Gupta | Preet Sagar
Source: World Journal of Advanced Research and Reviews (article, 2025-10-29)
Field: Chemistry — Organic Chemistry

Role of Heterocycles in Drug Discovery: An Overview Heterocycles represent one of the most important classes of chemical scaffolds in modern drug discovery. Their widespread presence in natural products and synthetic drugs underscores their versatility and biological significance. More than half of all approved small-molecule drugs feature at least one heterocyclic ring, reflecting their ability to fine-tune pharmacological and physicochemical properties. In this review, we highlight the contribution of different classes of heterocycles including five- and six-membered rings, non-aromatic systems, and fused heterocycles to medicinal chemistry. Their applications across therapeutic areas such as oncology, infectious diseases, central nervous system (CNS) disorders, and antiviral therapy are discussed. Finally, we outline recent advances in rational drug design, computational methods, and emerging trends such as targeted therapy, nanotechnology, and personalized medicine, emphasizing the continued importance of heterocycles in shaping the future of therapeutics. Drug ; Drug discovery ; Feature (linguistics) ; Drug design Synthesis and Reactivity of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Pyrroles
Authors: Weichen Dai | Zhiming Dai | Zhijie Huang | Ying Pan | X Li | Xi Li | Yi Zhou | Ji Qi | Jiang Wu
Source: ArXiv.org (preprint, 2025-03-28)
Field: Materials Science — Materials Chemistry

RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveraging structured scientific data (e.g., chemical molecular properties in databases) that encapsulate centuries of accumulated scientific expertise. These structured datasets hold strategic significance for advancing AI for Science yet current approaches merely treat them as auxiliary supplements to unstructured text. This study pioneers a systematic investigation into enhancing LLMs with structured scientific data, using chemical molecular science as a testbed. We investigate the impact of incorporating molecular property data on LLM across distinct training phases, including continual pre-training, supervised fine-tuning, and reinforcement learning. Notably, to address the inherent limitation of numerical insensitivity in large models, we propose an innovative methodology termed "Reinforcement Learning with Database Feedback" (RLDBF). Experimental evaluations demonstrate the efficacy of the proposed approach, with the model exhibiting remarkable generalization capabilities on previously unseen data and other chemical tasks. The results substantiate the potential of our method in advancing the field of structured scientific data processing within LLMs. Reinforcement learning ; Generalization ; Field (mathematics) ; Property (philosophy) ; Unstructured data ; Sociology of scientific knowledge Machine Learning in Materials Science ; Topic Modeling ; Biomedical Text Mining and Ontologies
Authors: Jia Liu | Long Yu | Xiaoyun Yi | Xin Li | Ziqing Liu | He Wang | Linwei Yang | Xin Yang | Suhua Wang
Source: Inorganic Chemistry Frontiers (article, 2025-11-12)
Field: Chemistry — Inorganic Chemistry

Polarity-enhanced cationic π-conjugation in Mn( <scp>ii</scp> )-based metal halide luminescent materials: boosting quantum yield and enabling anti-counterfeiting Low-dimensional lead-free Mn( ii )-based organic–inorganic metal halides are promising luminescent materials, yet molecular-level fine-tuning of organic structures to achieve high quantum yield and functional red-light emission remains challenging. Halide ; Quantum yield ; Luminescence ; Cationic polymerization ; Yield (engineering) ; Boosting (machine learning) ; Metal Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials ; Perovskite Materials and Applications
Authors: Guojiang Zhao | Zixiang Lu | Ge, Yutang | S. X. Li | Zheng Xing Cheng | Haitao Lin | Lirong Wu | Hanchen Xia | Hengxing Cai | Wenxuan Guo | Hongshuai Wang | Mingjun Xu | Siyu Zhu | Guolin Ke | Linfeng Zhang | Zhifeng Gao
Source: ArXiv.org (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs Large Language Models (LLMs) have shown impressive performance across various domains, but their ability to perform molecular reasoning remains underexplored. Existing methods mostly rely on general-purpose prompting, which lacks domain-specific molecular semantics, or fine-tuning, which faces challenges in interpretability and reasoning depth, often leading to structural and textual hallucinations. To address these issues, we introduce MolReasoner, a two-stage framework that transitions LLMs from memorization to high-fidelity chemical reasoning. In the Mol-SFT stage, knowledge-enhanced Chain-of-Thought (CoT) data provides a strong foundation, while the Mol-RL stage refines reasoning using a novel, task-adaptive reward system to mitigate hallucinations. Extensive evaluations demonstrate that MolReasoner significantly outperforms a wide range of strong baselines in both molecule generation and captioning tasks. Further analyses highlight the framework's synergistic design and its ability to produce more interpretable outputs. Our work presents a principled and effective new approach for advancing high-fidelity molecular reasoning. Interpretability ; Memorization ; Closed captioning ; Range (aeronautics) ; Work (physics) ; Analogical reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Timur Yumalin | R. B. Salikhov | Timur Salikhov
Source: Izvestia Ufimskogo Nauchnogo Tsentra RAN (article, 2025-03-26)
Field: Materials Science — Polymers and Plastics

DEVELOPMENT AND RESEARCH OF MATERIALS BASED ON CARBON NANOTUBES AND POLYANILINES FOR THE CREATION OF NEW ELECTRONIC DEVICES The paper presents studies aimed at developing new materials based on carbon nanotubes and polymers for use in modern electronics. Carbon nanotubes, due to their unique electrical, mechanical and photophysical properties, have great potential for creating optoelectronic devices and thin-film transistors. The effect of the structure and mor phology of materials on their functional properties, such as electrical conductivity and photoconductivity, was stud ied. Experiments demonstrated a significant improvement in performance when using polyaniline and carbon nano tube copolymers. The synthesis resulted in obtaining thin films with high charge carrier mobility and resistance to external influences. These materials are promising for use in organic electronics, including field-effect transistors and new-generation photodetectors. The presented results highlight the possibilities of fine-tuning the properties of thin films based on carbon nanotubes and polymers to create devices with specified characteristics. Carbon nanotube ; Nanotechnology ; Materials science ; Electronic materials ; Carbon fibers ; Engineering physics ; Engineering ; Composite material Conducting polymers and applications
Authors: Yidan Cheng (13540063) | Wenlong Huang (692950) | Yutao Chen (347841) | Weihuan Kong (11051553) | Shuai Zhang (115662) | Jiahui Huang (794375) | Yingjie Zhang (381556) | Yang Yang (45629) | Shangtao Chen (11816288) | Haobing Wang (1584778)
Source: Figshare (article, 2025-07-09)
Field: Chemistry — Organic Chemistry

Synthesis ofFunctional Metallocene Polyethylene throughMasking Free Copolymerization of Polar Monomer with Ethylene Metallocene polyethylene (mPE) is a high-performance polyolefin that is renowned for its exceptional material properties. Although polar functionalization of mPE can improve surface characteristics and expand its applications, conventional synthesis methods face challenges due to the poisoning effect of polar monomers on metallocene catalysts. In this study, we present a direct metallocene-catalyzed copolymerization of ethylene with oxygen- and nitrogen-containing polar monomers bypassing the need for masking reagents. Integrated experimental and computational analyses reveal that heteroatom interactions with the metallocene center do not deactivate the catalyst; rather, they fine-tune copolymerization behavior, influencing monomer insertion rates, catalytic activity, and molecular weight. Using titanocene [Ti(Cp)<sub>2</sub>Cl<sub>2</sub>], we synthesized ethylene/anisyl propylene (AP) copolymers with adjustable polar monomer incorporation (0–32 mol %). Meanwhile, a zirconocene catalyst [Zr(CpMe<sub>5</sub>)<sub>2</sub>Me<sub>2</sub>] produced high-molecular-weight functional plastics (e.g., 4.5 mol % AP, <i>M</i><sub>n</sub> = 370 kDa). The resulting polar mPE copolymers exhibited tailored surface and mechanical properties, highlighting their potential as advanced polyolefin-based adhesives and engineered plastic materials. Copolymer ; Metallocene ; Monomer ; Polyethylene ; Polyolefin ; Ethylene ; Polymer ; Post-metallocene catalyst Organometallic Complex Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Polymer crystallization and properties
Authors: Wataru Takahara | Yuichi Yamaguchi | Mai Ogano | Fuga Kakami | Yosuke Harashima | Tomoaki Takayama | Shogo Takasuka | Akihiko Kudo | Mikiya Fujii
Source: ChemRxiv (preprint, 2025-08-11)
Field: Materials Science — Materials Chemistry

Materials Dual-Source Knowledge Retrieval-Augmented Generation for Local Large Language Models in Photocatalysts Computer science ; Workflow ; Correctness ; Interpretability ; Domain (mathematical analysis) ; Dual (grammatical number) ; Data science ; Information retrieval ; Artificial intelligence ; Programming language ; Database Machine Learning in Materials Science ; Topic Modeling
Authors: S. Srinivasan | Biraj Jyoti Borah | Vittal B. Gudimetla
Source: Acta Crystallographica Section E Crystallographic Communications (article, 2025-09-11)
Field: Chemistry — Organic Chemistry

Synthesis and crystal structure of <i>N</i>,<i>N</i>′-(1,4-phenylenedimethylidyne)bis(2-phenylbenzenamine) This work reports the synthesis and structural aspects of the title compund, a conjugated 1,4-phenyl-enedi-imine, C<sub>32</sub>H<sub>24</sub>N<sub>2</sub>, which is encumbered with two steric biphenyl moieties. The compound possesses an inversion center and crystallizes in monoclinic space group <i>P</i>2<sub>1</sub>/<i>n</i>. The presence of biphenyl units across the conjugated di-imine results in a non-planar structure, where the two biphenyl units are twisted away from the plane of the central phenyl ring. The single-crystal XRD (SC-XRD) study of this mol-ecule provides insight into its mol-ecular conformation, bond parameters, and inter-molecular inter-actions, which are essential for understanding its potential as a key building block for the construction of inter-esting supra-molecular structures and fine-tuning their properties. Monoclinic crystal system ; Biphenyl ; Conjugated system ; Crystal structure ; Steric effects ; Group (periodic table) ; Molecule Structural and Chemical Analysis of Organic and Inorganic Compounds ; Crystal structures of chemical compounds ; Chemical Reactions and Mechanisms
Authors: Lize Bynens | Paola Mantegazza | Adam Marks | Yeongmin Park | Arwin Goossens | Stefania Moro | Tyler J. Quill | Garrett LeCroy | Christina Cheng | Arianna Magni | Laurence Lutsen | Jochen Vanderspikken | Simon E. F. Spencer | Koen Vandewal | Alberto Salleo | Giovanni Costantini | Wouter Maes
Source: ChemRxiv (preprint, 2025-09-04)
Field: Materials Science — Polymers and Plastics

Revealing the Full Potential of Mixed Ionic-Electronic Semiconductors – Avoiding Homocoupling to Boost Electrochemical Transistor Performance Organic electrochemical transistors (OECTs) enable the transduction of ionic signals into electronic outputs, positioning them as ideal candidates for next-generation sensing and (bio)signal processing applications. Recent years have witnessed the development of various OECT channel materials, affording insights on structural fine-tuning to achieve optimal performance and/or stability. However, homocouplings, commonly present in alternating conjugated polymers, have largely been overlooked. This study investigates the effect of homocoupling on OECT materials by employing two synthesis methods – standard Stille polymerization and an alternative symmetric approach – to create the p-type enhancement-mode benchmark polymer pgBTTT. The impact of homocoupling, and its absence, is studied by comparing the bulk properties of the two polymers and evaluating their respective OECT metrics. The new, homocoupling-free polymer exhibits a notably improved OECT performance (μC*), mainly due to an increase in electronic mobility (μ), which is attributed to both the perfectly alternating structure and its higher molar mass. Transistor ; Semiconductor ; Ionic bonding ; Electrochemistry ; Materials science ; Optoelectronics ; Nanotechnology ; Electronic engineering ; Electrical engineering ; Voltage ; Chemistry ; Engineering ; Ion ; Electrode ; Physical chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Fuel Cells and Related Materials
Authors: Wenkai Pan | Anastasia Grigoryeva | Corrado Bacchiocchi | Pablo Ballester | Luca Valgimigli | Manuel Petroselli
Source: Organic Letters (article, 2025-12-12)
Field: Chemistry — Spectroscopy

Enzyme-like Hydrogen Atom Transfer (HAT) Based Endo-Functionalized Molecular Receptors We report a series of endo-functionalized molecular receptors based on a calix[4]pyrrole scaffold, which exhibit Hydrogen Atom Transfer (HAT) reactivity towards free-radicals (e.g., peroxyl radicals). The chemical structure and shape of the molecular receptors affect the affinity towards free-radicals, while host-guest interactions are used to fine-tune the HAT reactivity, mimicking enzyme modulation in biological systems. Receptor ; Reactivity (psychology) ; Molecule ; Hydrogen atom ; Atom (system on chip) ; Hydrogen ; Hydrogen molecule Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Wenkai Pan (19924836) | Anastasia Grigoryeva (22836436) | Corrado Bacchiocchi (19555111) | Pablo Ballester (836089) | Luca Valgimigli (1490041) | Manuel Petroselli (6191762)
Source: Figshare (article, 2025-12-12)
Field: Chemistry — Inorganic Chemistry

Enzyme-likeHydrogen Atom Transfer (HAT) Based Endo-FunctionalizedMolecular Receptors We report a series of endo-functionalized molecular receptors based on a calix[4]pyrrole scaffold, which exhibit Hydrogen Atom Transfer (HAT) reactivity towards free-radicals (e.g., peroxyl radicals). The chemical structure and shape of the molecular receptors affect the affinity towards free-radicals, while host-guest interactions are used to fine-tune the HAT reactivity, mimicking enzyme modulation in biological systems. Receptor ; Hydrogen atom ; Reactivity (psychology) ; Molecule ; Hydrogen molecule ; Hydrogen ; Atom (system on chip) Metal-Catalyzed Oxygenation Mechanisms ; Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection
Authors: Guoqiang Kuang | Yabing Wang | M. Zhu | Hongbin Yin | Yijie Tao | Yafei Guo | Shiguo Zhang
Source: Dyes and Pigments (article, 2025-09-02)
Field: Materials Science — Polymers and Plastics

Synthesis of donor-acceptor conjugated polymers containing indaceno[1,2-b:5,6-b’]dithiophene and benzothiadiazo-[3,4-g]-quinoxaline for electrochromism Quinoxaline ; Electrochromism ; Conjugated system ; Polymer ; Acceptor ; Chemistry ; Polymer chemistry ; Combinatorial chemistry ; Organic chemistry Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Organic Electronics and Photovoltaics
Authors: Xiangru Wang | Zekun Jiang | Heng Yang | Cheng Tan | Xingying Lan | Chunming Xu | Tianhang Zhou
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Socrates-Mol: Self-Oriented Cognitive Reasoning through Autonomous Trial-and-Error with Empirical-Bayesian Screening for Molecules Molecular property prediction is fundamental to chemical engineering applications such as solvent screening. We present Socrates-Mol, a framework that transforms language models into empirical Bayesian reasoners through context engineering, addressing cold start problems without model fine-tuning. The system implements a reflective-prediction cycle where initial outputs serve as priors, retrieved molecular cases provide evidence, and refined predictions form posteriors, extracting reusable chemical rules from sparse data. We introduce ranking tasks aligned with industrial screening priorities and employ cross-model self-consistency across five language models to reduce variance. Experiments on amine solvent LogP prediction reveal task-dependent patterns: regression achieves 72% MAE reduction and 112% R-squared improvement through self-consistency, while ranking tasks show limited gains due to systematic multi-model biases. The framework reduces deployment costs by over 70% compared to full fine-tuning, providing a scalable solution for molecular property prediction while elucidating the task-adaptive nature of self-consistency mechanisms. Property (philosophy) ; Context (archaeology) ; Scalability ; Ranking (information retrieval) ; Bayesian probability ; Reduction (mathematics) ; Software deployment ; Bayesian inference ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kamal Choudhary (2813026)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

MicroscopyGPT: Generating Atomic-Structure Captionsfrom Microscopy Images of 2D Materials with Vision-Language Transformers Determining complete atomic structures directly from microscopy images remains a long-standing challenge in materials science. MicroscopyGPT is a vision-language model (VLM) that leverages multimodal generative pretrained transformers to predict full atomic configurations, including lattice parameters, element types, and atomic coordinates, from scanning transmission electron microscopy (STEM) images. The model is trained on a chemically and structurally diverse data set of simulated STEM images generated using the AtomVision tool and the JARVIS-DFT as well as the C2DB two-dimensional (2D) materials databases. The training set for fine-tuning comprises approximately 5000 2D materials, enabling the model to learn complex mappings from image features to crystallographic representations. I fine-tune the 11-billion-parameter LLaMA model, allowing efficient training on resource-constrained hardware. The rise of VLMs and the growth of materials data sets offer a major opportunity for microscopy-based analysis. This work highlights the potential of automated structure reconstruction from microscopy, with broad implications for materials discovery, nanotechnology, and catalysis. Transformer ; Training set ; Microscopy ; Transmission electron microscopy ; Generative grammar ; Atomic force microscopy ; Generative model ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques