jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ding, Rui | Ferreira, Rodrigo Pires | Chen, Yuxin | Chen, Junhong
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a locally deployable DR instance that integrates local retrieval-augmented generation with large language model reasoners, enhanced by a Deep Tree of Research (DToR) mechanism that adaptively expands and prunes research branches to maximize coverage, depth, and coherence. We systematically evaluate across 27 nanomaterials/device topics using a large language model (LLM)-as-judge rubric with five web-enabled state-of-the-art models as jurors. In addition, we conduct dry-lab validations on five representative tasks, where human experts use domain simulations (e.g., density functional theory, DFT) to verify whether DR-agent proposals are actionable. Results show that our DR agent produces reports with quality comparable to--and often exceeding--those of commercial systems (ChatGPT-5-thinking/o3/o4-mini-high Deep Research) at a substantially lower cost, while enabling on-prem integration with local data and tools. Deep learning ; Scope (computer science) ; Domain (mathematical analysis) ; Rubric ; Quality (philosophy) ; Tree (set theory) ; Language model Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Ding, Rui | Ferreira, Rodrigo Pires | Chen, Yuxin | Chen, Junhong
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a locally deployable DR instance that integrates local retrieval-augmented generation with large language model reasoners, enhanced by a Deep Tree of Research (DToR) mechanism that adaptively expands and prunes research branches to maximize coverage, depth, and coherence. We systematically evaluate across 27 nanomaterials/device topics using a large language model (LLM)-as-judge rubric with five web-enabled state-of-the-art models as jurors. In addition, we conduct dry-lab validations on five representative tasks, where human experts use domain simulations (e.g., density functional theory, DFT) to verify whether DR-agent proposals are actionable. Results show that our DR agent produces reports with quality comparable to--and often exceeding--those of commercial systems (ChatGPT-5-thinking/o3/o4-mini-high Deep Research) at a substantially lower cost, while enabling on-prem integration with local data and tools. Deep learning ; Scope (computer science) ; Domain (mathematical analysis) ; Rubric ; Quality (philosophy) ; Tree (set theory) ; Language model Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Mingyang Zhu | Yiting Lin | Zheyuan Pang | Yuzhu Zhao | Jingkun Li | Bowei Zhang | Bo Xu | Fu‐Zhen Xuan | Honglai Liu | Cheng Lian
Source: Chinese Chemical Letters (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

An automated workflow for lithium metal oxide screening assisted by large language models Workflow ; Interpretability ; Battery (electricity) ; Key (lock) ; Construct (python library) ; Lithium (medication) ; Language model Machine Learning in Materials Science ; Advancements in Battery Materials ; Extraction and Separation Processes
Authors: Jing Lin (50818) | Danfeng Zhao (6581492) | Shaopeng Lu (21590967) | Rushuo Li (11688508) | Xinmeng Xu (7365953) | Zhaokun Wang (704096) | Wenqing Li (1421971) | Yujing Ji (13198372) | Chenjun Zhang (7571291) | Lei Shi (8144) | Xu Jin (201752) | Hongyi Gao (203355) | Ge Wang (56838)
Source: Figshare (article, 2025-06-24)
Field: Chemistry — Inorganic Chemistry

Conversational Large-Language-ModelArtificial IntelligenceAgent for Accelerated Synthesis of Metal–Organic FrameworksCatalysts in Olefin Hydrogenation Metal–organic frameworks (MOFs) attract significant attention for their structural diversity and design flexibility, making them ideal candidates for catalytic applications. However, the traditional trial-and-error approach for optimizing MOF synthesis remains inefficient. In this study, we introduce the MOFsyn agent, an AI-driven framework that harnesses large language models (LLMs) for MOF synthesis optimization. This system integrates data automatic analysis, material mechanism analysis, and experimental protocol navigation by employing retrieval-augmented generation (RAG) to refine synthetic strategies based on natural language inputs. Using Ni@UiO-66(Ce) for olefin hydrogenation as a case study, the MOFsyn agent analyzed the relationship between synthesis conditions, structural characteristics, and catalytic performance, with a particular focus on the electronic structure of nickel. Through adaptive optimization, a novel stepwise reduction strategy was proposed that outperformed conventional one-pot reduction. The optimized Ni@UiO-66(Ce)-R2T1, synthesized under MOFsyn agent’s guidance, exhibited nearly twice the Ni<sup>0</sup>/Ni<sup>total</sup> ratio compared to the best-performing sample from an initial experimental set and achieved 100% conversion and selectivity for dicyclopentadiene hydrogenation under mild conditions (70 °C, 2 MPa). These results validate the accuracy and efficiency of the MOFsyn agent. This study provides an efficient tool for intelligent material synthesis, enabling researchers without programming expertise to accelerate material development. Olefin fiber ; Catalysis ; Reduction (mathematics) ; Selectivity ; Set (abstract data type) ; Protocol (science) ; Reaction conditions Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysts for Methane Reforming
Authors: Bingling Dai | Yuhang Song | Yue Zhan | Yibin Jiang | Cheng Wang
Source: Angewandte Chemie (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

From Data to Physics: An Agentic Large Language Model Solves a Competitive Adsorption Puzzle Abstract Scientific modeling often requires navigating a trade‐off between physical interpretability and empirical accuracy—a task that can take weeks of iteration, especially in systems with partial observability, structural complexity, and experimental errors. Here, we show how an agentic reasoning‐and‐coding large language model (LLM), OpenAI o3, autonomously solved a modeling challenge in surface chemistry that puzzled us for months: quantifying the competitive adsorption of carboxylic acids on metal‐organic layers (MOLs). With experimental data and a concise problem formulation, o3 rapidly formulated a physically grounded adsorption model, derived the mathematical equations, implemented the corresponding codes to fit the experimental data, revised its assumptions, and ultimately derived a competitive adsorption model with three parameters that matched experimental data across more than a dozen tested molecules. The resulting model—simple, mechanistically transparent, and quantitatively robust—incorporates both classical Langmuir competition and structural constraints such as site accessibility. Beyond addressing this particular challenge, our findings highlight a transformative shift in scientific methodology: from manual trial‐and‐error approaches to AI‐driven hypothesis generation and model refinement. This represents a new paradigm in research, wherein language models surpass the traditional roles of machine learning in data analysis and computational support, actively participating in scientific reasoning and hypothesis development. Interpretability ; Empirical modelling ; Experimental data ; Language model ; Mathematical model ; Transformative learning ; Modeling language ; Computational model Machine Learning in Materials Science ; Scientific Computing and Data Management ; CO2 Reduction Techniques and Catalysts
Authors: Harikrishna Sahu | Xiong Wei | Anagha Savit | Shivank S. Shukla | Rampi Ramprasad
Source: ArXiv.org (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

An Encoder-Decoder Foundation Chemical Language Model for Generative Polymer Design Traditional machine learning has advanced polymer discovery, yet direct generation of chemically valid and synthesizable polymers without exhaustive enumeration remains a challenge. Here we present polyT5, an encoder-decoder chemical language model based on the T5 architecture, trained to understand and generate polymer structures. polyT5 enables both property prediction and the targeted generation of polymers conditioned on desired property values. We demonstrate its utility for dielectric polymer design, seeking candidates with dielectric constant &gt;3, bandgap &gt;4 eV, and glass transition temperature &gt;400 K, alongside melt-processability and solubility requirements. From over 20,000 generated promising candidates, one was experimentally synthesized and validated, showing strong agreement with predictions. To further enhance usability, we integrated polyT5 within an agentic AI framework that couples it with a general-purpose LLM, allowing natural language interaction for property prediction and generative design. Together, these advances establish a versatile and accessible framework for accelerated polymer discovery. Generative grammar ; Polymer ; Property (philosophy) ; Foundation (evidence) ; Natural language ; Dielectric ; Constant (computer programming) ; Chemical space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zujie Xie | Zixuan Chen | Jiheng Liang | Xiangyang Yu | Ziru Yu
Source: ArXiv.org (preprint, 2025-07-29)
Field: Chemistry — Analytical Chemistry

LUMIR: an LLM-Driven Unified Agent Framework for Multi-task Infrared Spectroscopy Reasoning Infrared spectroscopy enables rapid, non destructive analysis of chemical and material properties, yet high dimensional signals and overlapping bands hinder conventional chemometric methods. Large language models (LLMs), with strong generalization and reasoning capabilities, offer new opportunities for automated spectral interpretation, but their potential in this domain remains largely untapped. This study introduces LUMIR (LLM-driven Unified agent framework for Multi-task Infrared spectroscopy Reasoning), an agent based framework designed to achieve accurate infrared spectral analysis under low data conditions. LUMIR integrates a structured literature knowledge base, automated preprocessing, feature extraction, and predictive modeling into a unified pipeline. By mining peer reviewed spectroscopy studies, it identifies validated preprocessing and feature derivation strategies, transforms spectra into low dimensional representations, and applies few-shot prompts for classification, regression, and anomaly detection. The framework was validated on diverse datasets, including the publicly available Milk near-infrared dataset, Chinese medicinal herbs, Citri Reticulatae Pericarpium(CRP) with different storage durations, an industrial wastewater COD dataset, and two additional public benchmarks, Tecator and Corn. Across these tasks, LUMIR achieved performance comparable to or surpassing established machine learning and deep learning models, particularly in resource limited settings. This work demonstrates that combining structured literature guidance with few-shot learning enables robust, scalable, and automated spectral interpretation. LUMIR establishes a new paradigm for applying LLMs to infrared spectroscopy, offering high accuracy with minimal labeled data and broad applicability across scientific and industrial domains. Preprocessor ; Feature (linguistics) ; Generalization ; Domain (mathematical analysis) ; Spectroscopy ; Infrared spectroscopy ; Resource (disambiguation) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Huang, Zhiyuan | Yang, Baichuan | He, Zikun | Wu, Yanhong | Hongyu, Fang | Liu, Zhenhe | Dongsheng, Lin | Su, Bing
Source: ArXiv.org (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry Chemical reasoning inherently integrates visual, textual, and symbolic modalities, yet existing benchmarks rarely capture this complexity, often relying on simple image-text pairs with limited chemical semantics. As a result, the actual ability of Multimodal Large Language Models (MLLMs) to process and integrate chemically meaningful information across modalities remains unclear. We introduce \textbf{ChemVTS-Bench}, a domain-authentic benchmark designed to systematically evaluate the Visual-Textual-Symbolic (VTS) reasoning abilities of MLLMs. ChemVTS-Bench contains diverse and challenging chemical problems spanning organic molecules, inorganic materials, and 3D crystal structures, with each task presented in three complementary input modes: (1) visual-only, (2) visual-text hybrid, and (3) SMILES-based symbolic input. This design enables fine-grained analysis of modality-dependent reasoning behaviors and cross-modal integration. To ensure rigorous and reproducible evaluation, we further develop an automated agent-based workflow that standardizes inference, verifies answers, and diagnoses failure modes. Extensive experiments on state-of-the-art MLLMs reveal that visual-only inputs remain challenging, structural chemistry is the hardest domain, and multimodal fusion mitigates but does not eliminate visual, knowledge-based, or logical errors, highlighting ChemVTS-Bench as a rigorous, domain-faithful testbed for advancing multimodal chemical reasoning. All data and code will be released to support future research. Workflow ; Benchmark (surveying) ; Modalities ; Process (computing) ; Testbed ; Task (project management) ; Simple (philosophy) Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Computational Drug Discovery Methods
Authors: Huang, Zhiyuan | Yang, Baichuan | He, Zikun | Wu, Yanhong | Hongyu, Fang | Liu, Zhenhe | Dongsheng, Lin | Su, Bing
Source: arXiv (Cornell University) (preprint, 2025-11-22)
Field: Materials Science — Materials Chemistry

ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry Chemical reasoning inherently integrates visual, textual, and symbolic modalities, yet existing benchmarks rarely capture this complexity, often relying on simple image-text pairs with limited chemical semantics. As a result, the actual ability of Multimodal Large Language Models (MLLMs) to process and integrate chemically meaningful information across modalities remains unclear. We introduce \textbf{ChemVTS-Bench}, a domain-authentic benchmark designed to systematically evaluate the Visual-Textual-Symbolic (VTS) reasoning abilities of MLLMs. ChemVTS-Bench contains diverse and challenging chemical problems spanning organic molecules, inorganic materials, and 3D crystal structures, with each task presented in three complementary input modes: (1) visual-only, (2) visual-text hybrid, and (3) SMILES-based symbolic input. This design enables fine-grained analysis of modality-dependent reasoning behaviors and cross-modal integration. To ensure rigorous and reproducible evaluation, we further develop an automated agent-based workflow that standardizes inference, verifies answers, and diagnoses failure modes. Extensive experiments on state-of-the-art MLLMs reveal that visual-only inputs remain challenging, structural chemistry is the hardest domain, and multimodal fusion mitigates but does not eliminate visual, knowledge-based, or logical errors, highlighting ChemVTS-Bench as a rigorous, domain-faithful testbed for advancing multimodal chemical reasoning. All data and code will be released to support future research. Workflow ; Benchmark (surveying) ; Modalities ; Process (computing) ; Testbed ; Task (project management) ; Simple (philosophy) Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Computational Drug Discovery Methods
Authors: Zujie Xie | Zixuan Chen | Jiheng Liang | Xiangyang Yu | Ziru Yu
Source: Analytica Chimica Acta (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

LUMIR: an LLM-driven unified agent framework for multi-task infrared spectroscopy reasoning Robustness (evolution) ; Workflow ; Preprocessor ; Anomaly detection ; Feature (linguistics) ; Data pre-processing ; Generalization ; Chemometrics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Yifei Zhu | Jiahui Zhang | B. Huang | Zhenggang Lan
Source: ArXiv.org (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Bridging Visual Intuition and Chemical Expertise: An Autonomous Analysis Framework for Nonadiabatic Dynamics Simulations via Mentor-Engineer-Student Collaboration Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a vision-driven framework that leverages the complementary strengths of two state-of-the-art large language models to establish a "virtual research collective." This collective operates through a "Mentor-Engineer-Student" paradigm that mimics the collaborative intelligence of a professional chemistry laboratory. Within this ecosystem, the Mentor provides physical intuition through visual reasoning, while the Engineer adaptively constructs analysis scripts, and the Student executes the pipeline and manages the data and results. VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary. This systematic approach identifies reaction channels and key nuclear motions while generating professional academic reports. By bridging visual insight with chemical expertise, VisU establishes a new paradigm for human-AI collaboration in the analysis of excited-state dynamics simulation results, significantly reducing dependence on manual interpretation and enabling more intuitive, scalable mechanistic discovery. Workflow ; Intuition ; Bridging (networking) ; Scalability ; Visualization ; Formalism (music) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: Yifei Zhu | Jiahui Zhang | B. Huang | Zhenggang Lan
Source: arXiv (Cornell University) (preprint, 2025-12-30)
Field: Materials Science — Materials Chemistry

Bridging Visual Intuition and Chemical Expertise: An Autonomous Analysis Framework for Nonadiabatic Dynamics Simulations via Mentor-Engineer-Student Collaboration Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a vision-driven framework that leverages the complementary strengths of two state-of-the-art large language models to establish a "virtual research collective." This collective operates through a "Mentor-Engineer-Student" paradigm that mimics the collaborative intelligence of a professional chemistry laboratory. Within this ecosystem, the Mentor provides physical intuition through visual reasoning, while the Engineer adaptively constructs analysis scripts, and the Student executes the pipeline and manages the data and results. VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary. This systematic approach identifies reaction channels and key nuclear motions while generating professional academic reports. By bridging visual insight with chemical expertise, VisU establishes a new paradigm for human-AI collaboration in the analysis of excited-state dynamics simulation results, significantly reducing dependence on manual interpretation and enabling more intuitive, scalable mechanistic discovery. Workflow ; Intuition ; Bridging (networking) ; Scalability ; Visualization ; Formalism (music) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: Guanghen Liu | Songge Yang | Yu Zhong
Source: arXiv (Cornell University) (preprint, 2025-12-28)
Field: Materials Science — Materials Chemistry

Masgent: An AI-assisted Materials Simulation Agent Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners. Workflow ; Scripting language ; Protocol (science) ; Simulation modeling ; Modeling and simulation ; Software Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Guanghen Liu | Songge Yang | Yu Zhong
Source: ArXiv.org (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Masgent: An AI-assisted Materials Simulation Agent Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners. Workflow ; Scripting language ; Protocol (science) ; Simulation modeling ; Modeling and simulation ; Software Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials