jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiguang Chen | Yantao Du | Ziniu Li | Jinhao Liu | Songyao Duan | Jiarui Guo | Minghao Liu | Jiaheng Liu | Tong Yang | Ge Zhang | Libo Qin | Wanxiang Che | Wenhao Huang
Source: ArXiv.org (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand this, we propose that effective and learnable Long CoT trajectories feature stable molecular-like structures in unified view, which are formed by three interaction types: Deep-Reasoning (covalent-like), Self-Reflection (hydrogen-bond-like), and Self-Exploration (van der Waals-like). Analysis of distilled trajectories reveals these structures emerge from Long CoT fine-tuning, not keyword imitation. We introduce Effective Semantic Isomers and show that only bonds promoting fast entropy convergence support stable Long CoT learning, while structural competition impairs training. Drawing on these findings, we present Mole-Syn, a distribution-transfer-graph method that guides synthesis of effective Long CoT structures, boosting performance and RL stability across benchmarks. Boosting (machine learning) ; Entropy (arrow of time) ; Convergence (economics) ; Topology (electrical circuits) ; Feature (linguistics) ; Stability (learning theory) ; Topological entropy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Machine Learning in Healthcare
Authors: Heloísa Bremm Madalosso | Camila Guindani | Pedro Henrique Hermes de Araújo | C. SAYER
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2026-01-16)
Field: Materials Science — Biomaterials

The Role of Polymer Engineering in Achieving Desirable Properties for Tissue Engineering Applications: Bulk Modification and Bioconjugation of Aliphatic Polyesters Tissue engineering aims to restore, maintain, or improve damaged tissues through the use of polymer scaffolds that support cellular growth and regeneration. Copolymerization enables the fine-tuning of thermal, structural, and mechanical polymer properties, facilitating scaffold fabrication via techniques like electrospinning and 3D printing. Functionalization and bioconjugation approaches, including thiol-ene click chemistry, allow for targeted surface modification without altering bulk properties, improving interaction with biological environments and enhancing the specificity and functionality of polyester-based scaffolds. This review highlights the central role of polymer reaction engineering in advancing aliphatic polyesters for tissue engineering, focusing on recent innovations in synthetic strategies and functionalization techniques that expand their applicability in regenerative medicine. Bioconjugation ; Surface modification ; Polyester ; Tissue engineering ; Polymer ; Scaffold ; Electrospinning ; Surface engineering Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization
Authors: Hongguang Li | Yuhao Liu | Kaipeng Zhuang | Shuaishuai Liu | Hongzhen Li | Yiran Zhang | Jinrui Li | Nicolas Godbert
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

Self-Assembly and Nonlinear Optical Properties of N-Substituted Fulleropyrrolidine Bearing Adamantane Moiety ; Supramolecular chemistry ; Adamantane ; Photoluminescence ; Monoclinic crystal system ; Orthorhombic crystal system ; Fullerene Fullerene Chemistry and Applications ; Nonlinear Optical Materials Studies ; Synthesis and Properties of Aromatic Compounds
Authors: Yanru Feng | Mengdi Wang | Xu Wang | Jin-Kui Liu | Huizhu Wang | Jun‐Fang Gong | Hui JIANG
Source: European Polymer Journal (article, 2026-02-04)
Field: Chemistry — Organic Chemistry

Synergistic modulation of fluorine and steric effects in nickel catalysts for direct synthesis of elastic and polar-functionalized polyethylenes Catalysis ; Steric effects ; Polyethylene ; Elastomer ; Thermal stability ; Branching (polymer chemistry) Organometallic Complex Synthesis and Catalysis ; Polymer composites and self-healing ; Polymer Nanocomposites and Properties
Authors: Mohamed Hammad Elsayed | Emtenan Almukhtar Alshanqiti | Fahim A. Qaraah | Iman Ibrahim | Ahmed F. Saber | Mahmoud Abdelnaby
Source: Carbon letters (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Progress in three-dimensional graphene oxide frameworks: tailored synthesis, functional properties, and emerging applications Graphene ; Oxide ; Thermal management of electronic devices and systems ; Grafting ; Linker ; Catalysis ; Membrane Covalent Organic Framework Applications ; Graphene research and applications ; Membrane Separation Technologies
Authors: Neha Kumari | Rohit Kumar Singh | Binitendra Naath Mongal | Archana Dwivedi | Prabhat Dixit | Anjani K. Pandey
Source: ACS Omega (article, 2026-02-19)
Field: Chemistry — Organic Chemistry

Amine Tris(phenolate) Ligands as a Versatile Scaffold for Early and Late Transition Metals: Synthesis and Application Amine tris-(phenolate) (usually abbreviated as tpa) ligands have drawn much interest as ligands for early transition metals, because they create stable complexes with broad catalytic applications in oxygen transfer reactions, polymerization, and other organic/organometallic fields of chemistry. Over the last 10 years, attention has been given to synthesizing tpa ligands with steric and electronic modifications, enabling fine-tuning of selectivity and reactivity in early and late transition metal complexes. Despite initial studies predominantly focusing on early transition metal complexes with tpa ligands in a broad sense, the field has expanded to late transition metals, uncovering new opportunities in catalysis, chirality transfer, polymerization, and oxygen transfer reactions. This review emphasizes early transition metals with tpa ligands and highlights their synthesis, application, and recent advancements after 2020 to the present, while also exploring the emerging development of late transition metal complexes and their applications in catalysis and synthetic reactions. Scaffold ; Amine gas treating ; Transition (genetics) ; Transition metal ; Catalysis Organometallic Complex Synthesis and Catalysis ; Coordination Chemistry and Organometallics ; Metal complexes synthesis and properties
Authors: Anupma Thakur | Jongyoun Kim | Brian C. Wyatt | Yury Gogotsi | Babak Anasori
Source: Nature Reviews Materials (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Composition–structure–property relationships in MXenes MXenes ; Vacancy defect ; Intercalation (chemistry) ; Composition (language) ; Diversity (politics) ; Surface engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Electromagnetic wave absorption materials
Authors: Syed Rameez Naqvi | Lu Peng
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these objectives. We present MOLPAQ, a modular quantum-classical generator that assembles molecules from quantum-generated latent patches. A \b{eta}-VAE pretrained on QM9 learns a chemically aligned latent manifold; a reduced conditioner maps molecular descriptors into this space; and a parameter-efficient quantum patch generator produces entangled node embeddings that a valence-aware aggregator reconstructs into valid molecular graphs. Adversarial fine-tuning with a latent critic and chemistry-shaped reward yields 100\% RDKit validity, 99.75\% novelty, and 0.905 diversity. Beyond aggregate metrics, the pretrained quantum generator, steered by the conditioner, improves mean QED by approx. 2.3\% and increases aromatic motif incidence by approx. 10-12\% relative to a parameter-matched classical generator, highlighting its role as a compact topology-shaping operator. Modular design ; Generator (circuit theory) ; News aggregator ; Generative grammar ; Property (philosophy) ; Aggregate (composite) ; Quantum ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Syed Rameez Naqvi | Lu Peng
Source: ArXiv.org (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these objectives. We present MOLPAQ, a modular quantum-classical generator that assembles molecules from quantum-generated latent patches. A \b{eta}-VAE pretrained on QM9 learns a chemically aligned latent manifold; a reduced conditioner maps molecular descriptors into this space; and a parameter-efficient quantum patch generator produces entangled node embeddings that a valence-aware aggregator reconstructs into valid molecular graphs. Adversarial fine-tuning with a latent critic and chemistry-shaped reward yields 100\% RDKit validity, 99.75\% novelty, and 0.905 diversity. Beyond aggregate metrics, the pretrained quantum generator, steered by the conditioner, improves mean QED by approx. 2.3\% and increases aromatic motif incidence by approx. 10-12\% relative to a parameter-matched classical generator, highlighting its role as a compact topology-shaping operator. Modular design ; Generator (circuit theory) ; News aggregator ; Generative grammar ; Property (philosophy) ; Aggregate (composite) ; Quantum ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Hanbum Ko | Chanhui Lee | Ye Rin Kim | Rodrigo Hormázabal | Sehui Han | Sungbin Lim | Sungwoong Kim
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction Retrosynthesis prediction is a core task in organic synthesis that aims to predict reactants for a given product molecule. Traditionally, chemists select a plausible bond disconnection and derive corresponding reactants, which is time-consuming and requires substantial expertise. While recent advancements in molecular large language models (LLMs) have made progress, many methods either predict reactants without strategic reasoning or conduct only a generic product analysis, rather than reason explicitly about bond-disconnection strategies that logically lead to the choice of specific reactants. To overcome these limitations, we propose RetroReasoner, a retrosynthetic reasoning model that leverages chemists' strategic thinking. RetroReasoner is trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). For SFT, we introduce SyntheticRetro, a framework that generates structured disconnection rationales alongside reactant predictions. In the case of RL, we apply a round-trip accuracy as reward, where predicted reactants are passed through a forward synthesis model, and predictions are rewarded when the forward-predicted product matches the original input product. Experimental results show that RetroReasoner not only outperforms prior baselines but also generates a broader range of feasible reactant proposals, particularly in handling more challenging reaction instances. Retrosynthetic analysis ; Task (project management) ; Disconnection ; Product (mathematics) ; Range (aeronautics) ; Core (optical fiber) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Robin Dantinne | Nathan Vinx | Philippe Leclère | Damien Cossement | Claude Poleunis | Arnaud Delcorte | Damien Thiry
Source: Advanced Materials Interfaces (article, 2026-03-26)
Field: Materials Science — Surfaces, Coatings and Films

Influence of the Substrate Temperature on the Growth Mechanism of Allylamine Plasma Polymer Films ABSTRACT This work aims to investigate the impact of the substrate temperature on the growth mechanism of allylamine‐based plasma polymer films (PPF). A comprehensive study of the PPF physicochemical properties, including the mechanical properties, the glass transition temperature, the chemical composition, the cross‐linking degree and the deposition kinetics, is performed and correlated to plasma chemistry. As the substrate temperature evolves from ‐10°C to 45°C, the glass transition temperature is observed to increase from 75°C to 230°C. This evolution is correlated to an increase in cross‐linking degree, modulated by the energy density brought by ion bombardment during film growth. The energy density is, in turn, significantly influenced by the deposition kinetics which strongly depend on the thermal conditions of the substrate, as evidenced by the growth rate decrease from 5.4 to 1.6 nm/min with the substrate temperature. Similarities are observed with another allyl‐based PPF family, suggesting a comparable growth mechanism but distinct film properties attributed to differences in growth rate. Overall, this study highlights the combined role of precursor chemistry and substrate temperature as key parameters for fine tuning of plasma polymer film properties. Substrate (aquarium) ; Polymer ; Polymer substrate ; Glass transition ; Plasma ; Deposition (geology) ; Allylamine ; Kinetics Surface Modification and Superhydrophobicity ; Polymer Nanocomposite Synthesis and Irradiation ; Polymer Surface Interaction Studies
Authors: Sangmin Oh | Jinmu You | Jaesun Kim | Jiho Lee | Hyungmin An | Seungwu Han | Youngho Kang
Source: arXiv (Cornell University) (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowledge-distillation framework. The model inherits the broad generalization capability of a large multi-task foundation model, SevenNet-Omni, trained on diverse materials datasets across chemical, configurational, and computational spaces. By learning chemical representations from high-quality inference data generated by the teacher model within a unified computational framework, SevenNet-Nano achieves high accuracy and strong transferability despite its compact architecture. The model also accurately captures a wide range of interatomic interactions, enabling reliable simulations under both equilibrium and extreme conditions, including plasma etching of SiO$_2$. Comprehensive benchmarks on static and dynamical properties--such as Li-ion diffusion and liquid densities--demonstrate its broad applicability with minimal fine-tuning. Importantly, SevenNet-Nano significantly reduces computational cost, achieving over an order-of-magnitude speedup and enabling large-scale atomistic simulations involving thousands of atoms. Interatomic potential ; Scalability ; Speedup ; Inference ; Toolbox ; Generalization ; Range (aeronautics) ; Transferability ; Multiscale modeling Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Peiliang Zhang | Jingling Yuan | Jianmin Wang | Yongjun Zhu | Lin Li
Source: Knowledge-Based Systems (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Prototype learning with structural-semantic alignment for interpretable molecular relational learning Interpretability ; Representation (politics) ; Molecular tweezers ; Molecular descriptor ; Weighting ; Matching (statistics) ; Cluster analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiaobing Wang | Yuan Wu | Chenchen Zhao | Chaokun Li | Tangfeng Yao | Qiuling Song
Source: Organic Letters (article, 2026-03-17)
Field: Chemistry — Organic Chemistry

Et <sub>3</sub> SiOBpin as a Dual Reagent Enabled Photocatalytic Stereoselective Assembly of Multisubstituted Perfluoroalkyl-boryl Styrenes Repurposing waste materials that would otherwise be discarded and finding new pathways for them to participate in reactions for recycling are parts of a highly atom-economical and environmentally friendly strategy. Drawing upon this concept, we have developed a method that utilizes Et<sub>3</sub>SiOBpin, a borasiloxane typically obtained as a byproduct; limited reports have explored its potential applications in organic synthesis, to facilitate the construction of diverse multisubstituted perfluoroalkyl-boryl olefins via tetracoordinate boron species under photoinduced conditions. Despite challenges such as reaction progress, yield, and selectivity, we overcame these challenges by fine-tuning the oxygen, silicon, and alkyl groups in the borasiloxane reagent. This protocol boasts readily available starting materials, high atom economy, a broad substrate scope, and diversified valuable products and, notably, marks the first successful use of borasiloxane as a dual reagent (boron reagent and multifunctional base) in a synthetic methodology. This advancement holds significant potential to enrich and expand synthetic chemistry research. Reagent ; Stereoselectivity ; Alkyl ; Substrate (aquarium) ; Environmentally friendly ; Organic synthesis ; Tetracoordinate Radical Photochemical Reactions ; Organoboron and organosilicon chemistry ; Fluorine in Organic Chemistry
Authors: Dan Li | Yingdong Wei | Siyuan Yang | Guichun Hong | Haixia Ma | Wenqiu Su | Shengwei Guo | Yen Wei | Jinzhao Ji
Source: Nano Research (article, 2026-02-01)
Field: Chemistry — Organic Chemistry

Microwave-assisted synthesis of polymer nanoparticles with physically entrapped fluorophores Microwave irradiation has emerged as a highly advantageous alternative to conventional heating methods, with well-documented merits in the synthesis of diverse nanomaterials, including fluorescent nanomaterials. However, its potential for enabling the rational fabrication of fluorescent polymer nanoparticles (FPNs) with precisely tunable size, fluorescence wavelength and brightness, a critical requirement for their broad practical applications, remains underexplored. Here, we present a microwave-assisted strategy for the rapid preparation of FPNs incorporating physically entrapped fluorophores, using monomer emulsions pre-doped with dye molecules as precursors. By systematically modulating the concentrations of salt and surfactant, the type and loading of fluorophores, and the monomers prior to polymerization, we achieve precise control over dye-dye interactions, energy transfer processes, and the local microenvironment within the FPNs. This approach enables fine-tuning of FPN size, emission wavelength, and fluorescence brightness while endowing the nanoparticles with thermoresponsive properties. We anticipate that this facile and efficient FPNs fabrication protocol will facilitate the large-scale production and practical deployment of these functional fluorescent nanomaterials. Fluorescence ; Nanoparticle ; Polymer ; Fabrication ; Monomer ; Energy transfer Microwave-Assisted Synthesis and Applications ; Advanced Polymer Synthesis and Characterization ; Nanoparticle-Based Drug Delivery
Authors: Jiahe Song | Chuang Wang | Yinfan Wang | Hao Zheng | Rui Nie | Bowen Jiang | Xingjian Wei | Junyuan Gao | Yubin Wang | Bin Wang | Lijun Wu | Jiang Wu | Qian Yu | Conghui He
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm to automate this complex visual reasoning task, their application is fundamentally bottlenecked by the inability to align visual chemical entities with pre-trained knowledge, alongside the inherent discrepancy between token-level training and reaction-level evaluation. To address these dual challenges, this work enhances VLM-based RxnDP from two complementary perspectives: prompting representation and learning paradigms. First, we propose Identifier as Visual Prompting (IdtVP), which leverages naturally occurring molecule identifiers (e.g., bold numerals like 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution capabilities, outperforming existing prompting strategies. Second, to further optimize performance within fine-tuning paradigms, we introduce Re3-DAPO, a reinforcement learning algorithm that leverages verifiable rewards to directly optimize reaction-level metrics, thereby achieving consistent gains over standard supervised fine-tuning. Additionally, we release the ScannedRxn benchmark, comprising scanned historical reaction diagrams with real-world artifacts, to rigorously assess model robustness and out-of-distribution ability. Our contributions advance the accuracy and generalization of VLM-based reaction diagram parsing. We will release data, models, and code on GitHub. Identifier ; Reinforcement learning ; Parsing ; Robustness (evolution) ; Verifiable secret sharing ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks