jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Azanu Mirolgn Mequanenit | Eyerusalem Alebachew Nibret | Pilar Herrero-Martín | Rodrigo Martı́nez-Béjar
Source: Electronics (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

A Deep Reinforcement Learning-Enhanced Multi-Agent System for Ontology-Based Health Management in Nanotechnology This study provides a novel approach to the field of prognostics and health management (PHM) in nanotechnology: multi-agent systems integrated with ontology-based knowledge representation and Deep Reinforcement Learning (DRL). This framework has agents acting in a network-like manner, where every agent investigates a particular subject of nanotechnology design and lifecycle management for an interdependent and multifaceted problem-solving approach. Ontologies give the framework a semantic dimension, which allows for the precise and context-dependent interpretation of data. These permit observations attuned towards understanding the behaviors of nanomaterials, performance limitations, and failure mechanisms. On the other hand, having a DRL-integrated module permits agents to provide dynamic adaptation to changing operational contexts, datasets, and user scenarios while continuously calibrating their decisions for better accuracy and efficiency. Preliminary evaluations based on expert-reviewed test cases demonstrated a 95% task success rate and a decision-making accuracy of 96%, indicating the system’s strong potential in handling complex nanotechnology scenarios. These results show good robustness and adaptability to certain PHM problems, such as predictive maintenance of nanodevices, lifespan optimization of nanomaterials, and risk assessment in complex environments. This study introduces a novel integration of Multi-Agent Systems (MAS), ontology-driven reasoning, and DRL, enabling dynamic cross-ontology collaboration and online learning capabilities. These features allow the system to adapt to evolving user needs and heterogeneous knowledge domains in nanotechnology. Prognostics ; Adaptability ; Reinforcement learning ; Robustness (evolution) ; Interdependence ; Flexibility (engineering) ; Adaptation (eye) Nanoparticles: synthesis and applications ; Biomedical Text Mining and Ontologies ; Molecular Communication and Nanonetworks
Authors: Zhuang Fu | Alex Manley | Mohammad Alian
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

gem5 Co-Pilot: AI Assistant Agent for Architectural Design Space Exploration Generative AI is increasing the productivity of software and hardware development across many application domains. In this work, we utilize the power of Large Language Models (LLMs) to develop a co-pilot agent for assisting gem5 users with automating design space exploration. Computer architecture design space exploration is complex and time-consuming, given that numerous parameter settings and simulation statistics must be analyzed before improving the current design. The emergence of LLMs has significantly accelerated the analysis of long-text data as well as smart decision making, two key functions in a successful design space exploration task. In this project, we first build gem5 Co-Pilot, an AI agent assistant for gem5, which comes with a webpage-GUI for smooth user interaction, agent automation, and result summarization. We also implemented a language for design space exploration, as well as a Design Space Database (DSDB). With DSDB, gem5 Co-Pilot effectively implements a Retrieval Augmented Generation system for gem5 design space exploration. We experiment on cost-constraint optimization with four cost ranges and compare our results with two baseline models. Results show that gem5 Co-Pilot can quickly identify optimal parameters for specific design constraints based on performance and cost, with limited user interaction. Design space exploration ; Key (lock) ; Space (punctuation) ; Space exploration ; Software ; Architecture ; Baseline (sea) Machine Learning in Materials Science ; Artificial Intelligence in Games ; Model-Driven Software Engineering Techniques
Authors: Konstantinos T Kotsis
Source: International Journal of Advanced Multidisciplinary Research and Studies (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

AI-Driven Transformation of High-Resolution X-ray Diffraction: Data Infrastructures, Reproducibility, and Educational Implications The integration of artificial intelligence (AI) into experimental physics is reshaping the infrastructures, methodologies, and educational practices that support scientific inquiry. High-resolution X-ray diffraction (HRXRD) provides a compelling case study, as its intricate geometries, multidimensional parameter spaces, and large data volumes make it both a driver and beneficiary of AI-based innovation. This paper examines the transformations emerging from AI-enabled HRXRD across three interconnected domains. First, we analyze how standardized, FAIR-compliant data infrastructures—including metadata frameworks, interoperability standards, and real-time streaming systems—enable the effective deployment of AI algorithms during experiments. Second, we address the challenges that algorithmic decision-making poses for reproducibility and transparency, emphasizing the need for systematic logging, model sharing, and explainable AI (XAI) approaches to maintain scientific rigor. Third, we explore the educational opportunities arising from these developments, ranging from virtual laboratories and remote experimentation to the cultivation of data-intensive and critical AI literacy. Taking together, these dimensions outline a pathway toward a new experimental culture in which human expertise and algorithmic intelligence interact transparently and reproducibly. The paper argues that aligning AI integration with open data practices and pedagogical innovation can strengthen both the epistemology and the teaching of contemporary experimental physics. Interoperability ; Metadata ; Software deployment ; Data sharing ; Transformation (genetics) ; Beneficiary ; Unification Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Aditya Vatsavai | Yongtao Liu | Rama K. Vasudevan | G. Narasimha
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Band excitation piezo-response spectroscopy (BEPS) data on lead titanate (PTO) samples. Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure-property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure-property relationships in materials science. Property (philosophy) ; Lead (geology) ; Kernel (algebra) ; Spectroscopy ; Sampling (signal processing) ; Excitation ; Deep learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Song, Jiahe | Wang, Chuang | Jiang, Bowen | Wang, Yinfan | Zheng, Hao | Wei, Xingjian | Liu, Chengjin | Nie, Rui | Gao, Junyuan | Sun, Jiaxing | Wang, Yubin | Wu, Lijun | Huang, Zhenhua | Wu, Jiang | Yu, Qian | He, Conghui
Source: ArXiv.org (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed BBox and Index as Visual Prompt (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub. Parsing ; Closed captioning ; Bounding overwatch ; Process (computing) ; Construct (python library) ; Task (project management) ; Code (set theory) ; Diagram Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Topic Modeling
Authors: Yan Leng (770045) | Yi Zhong (153468) | Zhi Gu (10328379) | Peiyi Li (2326474) | Haoting Cui (21030397) | Xing Li (134367) | Yang Liu (4829) | Jiayu Wan (1487107)
Source: Figshare (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Intelligent,Personalized Scientific Assistant viaLarge Language Models for Solid-State Battery Research In response to the rapid advancements and heightened competition within solid-state battery research, the sheer volume of publications presents a significant challenge for researchers seeking comprehensive insights. This paper introduces ChatSSB, an advanced research assistant designed to bolster scientific inquiry within this dynamic field. Leveraging the Retrieval-Augmented Generation (RAG) framework, ChatSSB excels in extracting precise information from the latest research publications through an intuitive Q&A interface. Beyond its foundational capabilities, ChatSSB boasts a customizable expert knowledge database, continuously updated through a dynamic feedback mechanism. This ensures researchers have access to cutting-edge and reliable information, overcoming the limitations of outdated or incomplete literature. Furthermore, the integration of multiagent collaboration and embedded tools within RAG facilitates robust quantitative analysis, enabling efficient data collection, visualization, and interpretation. Collectively, these features empower ChatSSB to deliver precise, actionable insights, significantly accelerating innovation in solid-state battery technology and propelling it toward the next frontier of materials science. Battery (electricity) ; Big data ; Data access ; Bolster ; Competition (biology) ; Class (philosophy) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Haiteng Zhao | Junhao Shen | Yiming Zhang | Songyang Gao | Kuikun Liu | Tianyou Ma | Zheng Fan | Dahua Lin | Wenwei Zhang | Kai Chen
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assistance of formal proof systems. However, due to weak heuristics for auxiliary constructions, AI for geometry problem solving remains dominated by expert models such as AlphaGeometry 2, which rely heavily on large-scale data synthesis and search for both training and evaluation. In this work, we make the first attempt to build a medalist-level LLM agent for geometry and present InternGeometry. InternGeometry overcomes the heuristic limitations in geometry by iteratively proposing propositions and auxiliary constructions, verifying them with a symbolic engine, and reflecting on the engine's feedback to guide subsequent proposals. A dynamic memory mechanism enables InternGeometry to conduct more than two hundred interactions with the symbolic engine per problem. To further accelerate learning, we introduce Complexity-Boosting Reinforcement Learning (CBRL), which gradually increases the complexity of synthesized problems across training stages. Built on InternThinker-32B, InternGeometry solves 44 of 50 IMO geometry problems (2000-2024), exceeding the average gold medalist score (40.9), using only 13K training examples, just 0.004% of the data used by AlphaGeometry 2, demonstrating the potential of LLM agents on expert-level geometry tasks. InternGeometry can also propose novel auxiliary constructions for IMO problems that do not appear in human solutions. Heuristics ; Heuristic ; Reinforcement learning ; Boosting (machine learning) ; Computational geometry ; Algebraic geometry ; Automated theorem proving Machine Learning in Materials Science ; Polynomial and algebraic computation ; Mathematics Education and Teaching Techniques
Authors: Heng Zhang
Source: International Journal of Computational and Biological Sciences (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Microstructure Segmentation in Electron Microscopy Using Promptable Models and Boundary-Aware Losses The automated analysis of microstructure in materials science is a critical step towards accelerating the design and discovery of novel materials. Electron Microscopy (EM), specifically Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), provides high-resolution imaging essential for characterizing grain boundaries, precipitates, and defects. However, the segmentation of these features remains a formidable challenge due to intrinsic noise, low contrast, and complex topological variations inherent in EM imagery. While deep learning has revolutionized image segmentation, traditional fully supervised models often falter when generalizing across diverse material classes or require prohibitively large annotated datasets. This paper introduces a novel framework that leverages promptable foundation models adapted for the microstructural domain, integrated with a specialized boundary-aware loss function. We propose a methodology that fine-tunes Vision Transformer (ViT) encoders using lightweight adapters, enabling the model to respond to sparse user prompts such as points or bounding boxes. Furthermore, to address the vanishing gradient problem often observed at thin grain boundaries, we formulate a hybrid loss function combining region-based metrics with a differentiable distance map penalty. Our experiments demonstrate that this approach significantly outperforms standard U-Net architectures and generic segmentation models, achieving superior boundary adherence and reducing annotation time by approximately 80%. Segmentation ; Encoder ; Image segmentation ; Bounding overwatch ; Function (biology) ; Supervised learning ; Transmission electron microscopy ; Microscopy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Neural Network Applications
Authors: Haoyu Wang | Yujia Fu | Zhu Zhang | Shuo Wang | Zhibin Ren | Xiaorong Wang | Zhili Li | Chaoqun He | Bo An | Zhiyuan Liu | Maosong Sun
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

LLM$\times$MapReduce-V2: Entropy-Driven Convolutional Test-Time Scaling for Generating Long-Form Articles from Extremely Long Resources Long-form generation is crucial for a wide range of practical applications, typically categorized into short-to-long and long-to-long generation. While short-to-long generations have received considerable attention, generating long texts from extremely long resources remains relatively underexplored. The primary challenge in long-to-long generation lies in effectively integrating and analyzing relevant information from extensive inputs, which remains difficult for current large language models (LLMs). In this paper, we propose LLM$\times$MapReduce-V2, a novel test-time scaling strategy designed to enhance the ability of LLMs to process extremely long inputs. Drawing inspiration from convolutional neural networks, which iteratively integrate local features into higher-level global representations, LLM$\times$MapReduce-V2 utilizes stacked convolutional scaling layers to progressively expand the understanding of input materials. Both quantitative and qualitative experimental results demonstrate that our approach substantially enhances the ability of LLMs to process long inputs and generate coherent, informative long-form articles, outperforming several representative baselines. Both LLM$\times$MapReduce-V2 and SurveyEval are publicly available at https://github.com/thunlp/LLMxMapReduce . Process (computing) ; Convolutional neural network ; Scaling ; Range (aeronautics) ; Scale (ratio) Machine Learning in Materials Science ; Topic Modeling ; Computational and Text Analysis Methods
Authors: Yong Zhang | Eric Gyamfi | Laura N. Anderson | Stephen M. Roberts | Matt Barker
Source: ArXiv.org (preprint, 2025-02-06)
Field: Materials Science — Materials Chemistry

Disrupt Your Research Using Generative AI Powered ScienceSage Large Language Models (LLM) are disrupting science and research in different subjects and industries. Here we report a minimum-viable-product (MVP) web application called $\textbf{ScienceSage}$. It leverages generative artificial intelligence (GenAI) to help researchers disrupt the speed, magnitude and scope of product innovation. $\textbf{ScienceSage}$ enables researchers to build, store, update and query a knowledge base (KB). A KB codifies user's knowledge/information of a given domain in both vector index and knowledge graph (KG) index for efficient information retrieval and query. The knowledge/information can be extracted from user's textual documents, images, videos, audios and/or the research reports generated based on a research question and the latest relevant information on internet. The same set of KBs interconnect three functions on $\textbf{ScienceSage}$: 'Generate Research Report', 'Chat With Your Documents' and 'Chat With Anything'. We share our learning to encourage discussion and improvement of GenAI's role in scientific research. Generative grammar ; Knowledge base ; Set (abstract data type) ; Scope (computer science) ; Domain (mathematical analysis) ; Graph ; Knowledge graph ; Index (typography) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: David Dang | Susan M. Love | Matthieu Salib | Quoc Bao Dang | Samuel Rothfarb | Mysk Alnatour | Andrew Salij | Hou‐Tong Chen | Ho Wai | Lee Lee | Wilton J. M. Kort-Kamp
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences encoding materials, geometries, and lattice configurations. Analogous to molecular textual representations in chemistry, METASTRINGS provides a framework connecting human interpretability with computational design by capturing the structural hierarchy of photonic metasurfaces. Building on this representation, we develop Meta-GPT, a foundation transformer model trained on METASTRINGS and finetuned with physics-informed supervised, reinforcement, and chain-of-thought learning. Across various design tasks, the model achieves &lt;3% mean-squared spectral error and maintains &gt;98% syntactic validity, generating diverse metasurface prototypes whose experimentally measured optical responses match their target spectra. These results demonstrate that Meta-GPT can learn the compositional rules of light-matter interactions through METASTRINGS, laying a rigorous foundation for AI-driven photonics and representing an important step toward a metasurface genome project. Interpretability ; Decoding methods ; Encoding (memory) ; Generative grammar ; Hierarchy ; Photonics ; Transformer ; Embedding Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Machine Learning in Materials Science
Authors: Yao Danli | Cheng Rong
Source: Journal of Materials Science Materials in Electronics (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and microwave absorption properties of Fe3O4/activated carbon composites for naval ship stealth Reflection loss ; Microwave ; Impedance matching ; Composite number ; Bandwidth (computing) ; Wideband ; Absorption (acoustics) ; Nanoparticle Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Advanced Antenna and Metasurface Technologies
Authors: Shuhui Bi | Chang Song | Simon Song | Jinze Lv | Jian Chen | Xinyun Wang | Aimin Zhou | Hao Hao
Source: ArXiv.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

AutoSynth: Automated Workflow Optimization for High-Quality Synthetic Dataset Generation via Monte Carlo Tree Search Supervised fine-tuning (SFT) of large language models (LLMs) for specialized tasks requires high-quality datasets, but manual curation is prohibitively expensive. Synthetic data generation offers scalability, but its effectiveness relies on complex, multi-stage workflows, integrating prompt engineering and model orchestration. Existing automated workflow methods face a cold start problem: they require labeled datasets for reward modeling, which is especially problematic for subjective, open-ended tasks with no objective ground truth. We introduce AutoSynth, a framework that automates workflow discovery and optimization without reference datasets by reframing the problem as a Monte Carlo Tree Search guided by a novel dataset-free hybrid reward. This reward enables meta-learning through two LLM-as-judge components: one evaluates sample quality using dynamically generated task-specific metrics, and another assesses workflow code and prompt quality. Experiments on subjective educational tasks show that while expert-designed workflows achieve higher human preference rates (96-99% win rates vs. AutoSynth's 40-51%), models trained on AutoSynth-generated data dramatically outperform baselines (40-51% vs. 2-5%) and match or surpass expert workflows on certain metrics, suggesting discovery of quality dimensions beyond human intuition. These results are achieved while reducing human effort from 5-7 hours to just 30 minutes (&gt;90% reduction). AutoSynth tackles the cold start issue in data-centric AI, offering a scalable, cost-effective method for subjective LLM tasks. Code: https://github.com/bisz9918-maker/AutoSynth. Workflow ; Usability ; Tree (set theory) ; Monte Carlo tree search ; Quality (philosophy) ; Monte Carlo method ; Code (set theory) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and Data Classification