jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Salij | R. Seaton Ullberg | Megan C. Davis | M. J. Cawkwell | Christopher J. Snyder | Cristina García–Cardona | Ivana Matanović | Wilton J. M. Kort-Kamp
Source: arXiv (Cornell University) (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Generative Chemical Language Models for Energetic Materials Discovery The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements. Generative grammar ; Chemical space ; Foundation (evidence) ; Generative model ; Constructed language ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Runze Wang | Zijie Xing | Xingyue Liu | Mingqi Yang | Che He | Yanming Shen
Source: Information Processing & Management (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

From graphs to tokens: Substructure-aware molecular representation for large language models Representation (politics) ; Natural language ; Class (philosophy) ; Feature (linguistics) ; Abstraction ; Language model Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ryoma Yamamoto | Akira Takahashi | Kei Terayama | Yu Kumagai | Fumiyasu Oba
Source: ArXiv.org (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

ZEBRA-Prop: A Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties), is presented here as an extension of LLM-Prop. In contrast to LLM-Prop, which requires task-specific fine-tuning of the LLM, ZEBRA-Prop eliminates fine-tuning, thereby reducing computational cost and enabling rapid model training. The framework employs MatTPUSciBERT, an LLM specialized for materials science, to enhance predictive capability. Multiple textual embeddings are incorporated through a learnable weighting mechanism, which alleviates the context-length constraints inherent in LLM-Prop and facilitates effective integration of diverse textual representations. Evaluation is conducted using two datasets: the TextEdge dataset (approximately 140,000 entries) and an in-house dataset (approximately 2,000 entries) derived from the Materials Project database, with physical properties obtained from first-principles calculations. The predictive performance of ZEBRA-Prop is close to that of LLM-Prop, while the training time is reduced by approximately 95%. The performance improvements are attributable to three principal factors: domain-specific LLM utilization, diversified textual descriptions, and systematic text preprocessing. ZEBRA-Prop constitutes a scalable and computationally efficient framework for materials property prediction and supports accelerated materials discovery, particularly under limited computational resources. Property (philosophy) ; Weighting ; Scalability ; Regression ; Predictive modelling ; Contrast (vision) ; Regression analysis ; Extension (predicate logic) Machine Learning in Materials Science ; Material Selection and Properties ; X-ray Diffraction in Crystallography
Authors: Krishna Garg | Dhruv Kumar | Khantesh Agrawal
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Are Large Language Models (LLMs) Good at Research Ideation? Evidence from Chemical Engineering Quality (philosophy) ; Work (physics) ; Natural language ; Component (thermodynamics) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Computational and Text Analysis Methods
Authors: Ryoma Yamamoto | Akira Takahashi | Kei Terayama | Yu Kumagai | Fumiyasu Oba
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

ZEBRA-Prop: A Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties), is presented here as an extension of LLM-Prop. In contrast to LLM-Prop, which requires task-specific fine-tuning of the LLM, ZEBRA-Prop eliminates fine-tuning, thereby reducing computational cost and enabling rapid model training. The framework employs MatTPUSciBERT, an LLM specialized for materials science, to enhance predictive capability. Multiple textual embeddings are incorporated through a learnable weighting mechanism, which alleviates the context-length constraints inherent in LLM-Prop and facilitates effective integration of diverse textual representations. Evaluation is conducted using two datasets: the TextEdge dataset (approximately 140,000 entries) and an in-house dataset (approximately 2,000 entries) derived from the Materials Project database, with physical properties obtained from first-principles calculations. The predictive performance of ZEBRA-Prop is close to that of LLM-Prop, while the training time is reduced by approximately 95%. The performance improvements are attributable to three principal factors: domain-specific LLM utilization, diversified textual descriptions, and systematic text preprocessing. ZEBRA-Prop constitutes a scalable and computationally efficient framework for materials property prediction and supports accelerated materials discovery, particularly under limited computational resources. Property (philosophy) ; Weighting ; Scalability ; Regression ; Predictive modelling ; Contrast (vision) ; Regression analysis ; Extension (predicate logic) Machine Learning in Materials Science ; Material Selection and Properties ; X-ray Diffraction in Crystallography
Authors: Sharmil N. Anjirwala | Radha R. Rajput | Patel
Source: ChemistrySelect (article, 2026-03-01)
Field: Materials Science — Polymers and Plastics

High‐Performance Polymers: Synthetic Strategies and Pendant Group Engineering for Advanced Electronic, Membrane, and Functional Material Applications ABSTRACT The integration of functional heterocyclic moieties into polymer frameworks has led to the creation of advanced materials with exceptional thermal, mechanical, electronic, and optical characteristics. This review consolidates systematic developments in the synthesis of high‐performance polymers featuring heterocyclic architectures and pendant functionalities. Various approaches, such as radical polymerization, ring opening metathesis polymerization, reversible addition–fragmentation chain transfer, oxidative electropolymerization, and metal‐catalyzed polycondensation, have enabled the design of structurally diverse polymer frameworks. Methodologies involving the formation of polyazomethine, Schiff base, and thermally rearranged polyimides contribute to the evolution of smart, stimuli‐responsive polymeric platforms. Synthetic post‐polymerization methods such as Click chemistry, grafting, azo coupling, nucleophilic substitution, cycloaddition, and thiol‐ene addition, allowing for fine‐tuning of solubility, thermal stability, and photophysical response. Moreover, a special focus is given to pendant groups such as imidazole, benimidazole, triazole and quarternized pyridinium, coumarin, carbazole, rylene diimides, and fullerenes, which are employed for applications in optoelectronics, membrane fuel cells, and photovoltaic devices. Polymers bearing amphoteric nitrogen, bulky aromatic side chains, and conjugated donor–acceptor segments demonstrate enhanced proton conductivity and dielectric behavior. The foremost discussion emphasizes the structural relationship and effect of the pendant group that drives substantial innovation across multiple domains in advanced material science. Side chain ; Polymer ; Metathesis ; Conjugated system ; Functional group ; Thermal stability ; Ring (chemistry) ; Polymerization ; Functional polymers Synthesis and properties of polymers ; Organic Electronics and Photovoltaics ; Dielectric materials and actuators
Authors: Dinglong Yang | Chengyu Lu | Yixiong Feng | Weiming Mao | Kang Wang | Luzhao Sun | Yan Wang | Bingtao Hu | Jianrong Tan | Zhongfan Liu | Xiuju Song
Source: ACS Nano (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

GrapheneChat: A Large Language Model for Enhancing Graphene Research Graphene has garnered significant multidisciplinary interest for its exceptional properties and wide-ranging applications in materials science, engineering, physics, energy storage, and electronics. However, integrating the vast and heterogeneous body of knowledge into cohesive interdisciplinary research remains significantly challenging, requiring highly specialized expertise, rigorous experimental design, and efficient literature knowledge retrieval. To address these issues, GrapheneChat was developed as the first fine-tuned large language model (LLM) specifically designed for graphene research. Trained on comprehensive data sets of monographs and scholarly articles, GrapheneChat employs a two-stage strategy of supervised fine-tuning (SFT) and direct preference optimization (DPO) to achieve enhanced domain-specific reasoning and experimental design. By integrating a retrieval-augmented generation (RAG) framework, the model delivers literature-grounded and reference-supported responses for knowledge retrieval. Quantitative evaluations using the newly developed GrapheneBench demonstrate that GrapheneChat achieves an impressive accuracy of 91%, comparable to state-of-the-art models like GPT-4, while requiring fewer computational resources. As an intelligent research assistant, GrapheneChat not only facilitates interdisciplinary innovation but also establishes a paradigm for building domain-specific LLMs that enhance expert productivity in literature mining. Multidisciplinary approach ; Graphene ; Preference ; Productivity ; Efficient energy use Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Waqas Khalid | Dmitry I. Ignatov | Radu Timofte
Source: arXiv (Cornell University) (preprint, 2026-01-06)
Field: Materials Science — Materials Chemistry

From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles. The model synthesizes PyTorch convolutional networks which are validated, evaluated via low-fidelity performance signals (single-epoch accuracy), and filtered using a MinHash-Jaccard criterion to prevent structural redundancy. High-performing, novel architectures are converted into prompt-code pairs for iterative fine-tuning via parameter-efficient LoRA adaptation, initialized from the LEMUR dataset. Across cycles, the LLM internalizes empirical architectural priors, becoming a robust generator. The valid generation rate stabilizes at 50.6 percent (peaking at 74.5 percent), while mean first-epoch accuracy rises from 28.06 percent to 50.99 percent, and the fraction of candidates exceeding 40 percent accuracy grows from 2.04 percent to 96.81 percent. Analyses confirm the model moves beyond replicating existing motifs, synthesizing 455 high-performing architectures absent from the original corpus. By grounding code synthesis in execution feedback, this work provides a scalable blueprint for transforming stochastic generators into autonomous, performance-driven neural designers, establishing that LLMs can internalize empirical, non-textual rewards to transcend their training data. Scalability ; Blueprint ; Code (set theory) ; Memorization ; Language model ; Emulation ; LOOM ; Artificial neural network ; Code generation Machine Learning in Materials Science ; Software Engineering Research ; Topic Modeling
Authors: Amit Kumar Verma | Himani Pandey | Akhilesh Rawat | D. Negi | Surendra K. Puri
Source: Mini-Reviews in Organic Chemistry (article, 2026-01-19)
Field: Chemistry — Organic Chemistry

Indole-Based [3+2] Annulations: Strategies for Complexity Construction -A Literature Review Abstract: Indole-based organic compounds have attracted widespread interest due to their phenomenal chemical characteristics. Among these, the [3+2] annulation is a highly valuable reaction for generating complexity and valuable synthons in organic synthesis. The [3+2] annulation of the indole ring opens new avenues for synthesizing a wide range of functionalised indole derivatives. Further, these derivatives are highly valued in synthetic and medicinal chemistry due to their unique structural features and broad chemical reactivity. The indole scaffold is commonly found in natural products and pharmaceuticals, making it a privileged structure in drug design. Among the various synthetic approaches, the [3+2] annulation reactions have emerged as a powerful method for building complex annulated indole derivatives. Notwithstanding these reactions, which typically involve a 1,3-dipolar species and the electron-rich indole core, enable rapid construction of five-membered fused ring systems and introduce diverse functional groups in a single step. This strategy not only enhances molecular complexity but also facilitates access to bioactive intermediates with desirable medicinal applications. Indole-based [3+2] annulated compounds exhibit a wide range of biological activities, including antibacterial, antifungal, antioxidant, anticancer, and anti-inflammatory effects. These properties can be fine-tuned by modifying the substituents on the indole ring, making them attractive candidates for therapeutic development. The broad spectrum of biological activities and its potential for further functionalization motivate scientists and researchers to explore, develop novel methodologies and identify new lead compounds to foster drug discovery developments. Ongoing research focuses on developing new annulation protocols, optimizing conditions, and designing novel catalysts to expand the diversity of indolebased libraries. These compounds show promise not only as pharmaceutical leads but also as valuable tools in biological research and agrochemicals. Annulation ; Indole test ; Drug discovery ; Synthon ; Ring (chemistry) ; Broad spectrum Catalytic C–H Functionalization Methods ; Synthesis of Indole Derivatives ; Catalytic Alkyne Reactions
Authors: Joao Tanepau | Sylvain CAILLOL | Patrick Lacroix-Desmazes | Frédéric Lamaty | Xavier Bantreil | Julien Pinaud
Source: European Polymer Journal (article, 2026-02-24)
Field: Chemistry — Organic Chemistry

Mechanochemical ATRP: An asset for the bulk copolymerization of solid and liquid monomers Mechanochemistry ; Dispersity ; Copolymer ; Monomer ; Polymer ; Polymerization Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing ; Polymer crystallization and properties
Authors: Suzilene V. Santos | Crystian W. C. Silva | Pedro H. Britto-Costa | Thiago Lopes | Sávio F. Silva | Cleidilane S. Costa | Larissa Otubo | C. M. Rivaldo-Gómez | A. W. Carbonari | G. A. Cabrera-Pasca
Source: Langmuir (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Atmosphere-Controlled Synthesis of Hierarchical Cu/Cu <sub>2</sub> O/CuO Microtube Architectures Decorated with High-Density CuO Nanowires from Recycled E-Waste The design of hierarchical copper oxide microstructures with tailored nanomorphologies is essential for next-generation electrocatalytic, sensing, and nanoelectronic applications. Here, we report an atmosphere-controlled thermal oxidation route for the sustainable synthesis of hollow Cu/Cu<sub>2</sub>O/CuO microtubes decorated with a dense array of vertically aligned CuO nanowires. The synthesis uses recycled copper microwires from electronic waste (e-waste) coated with a polyurethane (PU) polymer. Comparative analysis under ambient air and high-purity synthetic air reveals that the thermal degradation of the polymeric coating forms a carbon-rich layer, crucial for regulating asymmetric cation transport and inducing mass transport that drives void formation via the Kirkendall effect. This mechanism transforms the solid microwire into a concentric hollow microtube structure. Critically, oxidation under synthetic air promotes the extensive growth of long CuO nanowires (up to 20 μm), guided by anisotropic diffusion along twin boundaries and sustained by a strong chemical potential gradient. These findings establish atmosphere control as a powerful strategy to fine-tune the multiscale architecture of sustainable metal oxide nanostructures from e-waste precursors, opening pathways for the scalable production of multifunctional materials. Nanowire ; Nanorod ; Nanoparticle ; Nanostructure ; Coupling (piping) Copper-based nanomaterials and applications ; Nanomaterials and Printing Technologies ; Supercapacitor Materials and Fabrication
Authors: Xia Li | Soumya Mukherjee | Michael J. Zaworotko
Source: Advanced Materials (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Crystal Engineering of Reticular Materials for Gas‐ and Liquid‐Phase Hydrocarbon Separation Crystal engineering focuses upon the design, properties, and applications of crystals, whereas reticular chemistry involves linking molecular building blocks to create network structures. The intersection of these areas is evident in the number of systematic studies of structure/function relationships concerning porous coordination networks (PCNs) and covalent organic frameworks (COFs). PCNs and COFs are inherently modular in nature and therefore amenable to systematic fine-tuning of both pore size and chemistry in a manner that is infeasible for other classes of porous solid. This review highlights how this exquisite control over pore size and chemistry has enabled the development of a new generation of physisorbents that are effective in the context of industrially relevant hydrocarbon (HC) separations. The motivation behind such reticular sorbents is the need to replace today's energy-intensive HC separation methods with more sustainable alternatives. Physisorbents are attractive in this context as they can offer the high selectivity needed for trace removal of impurities along with relatively low energy of recycling. This review details how crystal engineering strategies offer precise control of pore size and chemistry to enable HC selectivity to reach hitherto unprecedented levels. Nevertheless, despite these property advances, challenges remain to be addressed before commercial adoption becomes feasible. Context (archaeology) ; Crystal engineering ; Reticular connective tissue ; Crystal chemistry ; Porosity ; Crystal (programming language) ; Intersection (aeronautics) ; Selectivity Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Xia Li | Mukherjee | Michael J. Zaworotko
Source: University of Limerick Institutional Repository (University of Limerick) (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Crystal engineering of reticular materials for gas-and liquid-phase hydrocarbon separation Crystal engineering focuses upon the design, properties, and applications of crystals, whereas reticular chemistry involves linking molecular building blocks to create network structures. The intersection of these areas is evident in the number of systematic studies of structure/function relationships concerning porous coordination networks (PCNs) and covalent organic frameworks (COFs). PCNs and COFs are inherently modular in nature and therefore amenable to systematic fine-tuning of both pore size and chemistry in a manner that is infeasible for other classes of porous solid. This review highlights how this exquisite control over pore size and chemistry has enabled the development of a new generation of physisorbents that are effective in the context of industrially relevant hydrocarbon (HC) separations. The motivation behind such reticular sorbents is the need to replace today's energy-intensive HC separation methods with more sustainable alternatives. Physisorbents are attractive in this context as they can offer the high selectivity needed for trace removal of impurities along with relatively low energy of recycling. This review details how crystal engineering strategies offer precise control of pore size and chemistry to enable HC selectivity to reach hitherto unprecedented levels. Nevertheless, despite these property advances, challenges remain to be addressed before commercial adoption becomes feasible. Context (archaeology) ; Crystal engineering ; Reticular connective tissue ; Crystal chemistry ; Porosity ; Crystal (programming language) ; Intersection (aeronautics) Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
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 (Cornell University) (preprint, 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