jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hein Htet | Amgad Ahmed Ali Ibrahim | Yutaka Sasaki | Ryoji Asahi
Source: ArXiv.org (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

Extracting ORR Catalyst Information for Fuel Cell from Scientific Literature The oxygen reduction reaction (ORR) catalyst plays a critical role in enhancing fuel cell efficiency, making it a key focus in material science research. However, extracting structured information about ORR catalysts from vast scientific literature remains a significant challenge due to the complexity and diversity of textual data. In this study, we propose a named entity recognition (NER) and relation extraction (RE) approach using DyGIE++ with multiple pre-trained BERT variants, including MatSciBERT and PubMedBERT, to extract ORR catalyst-related information from the scientific literature, which is compiled into a fuel cell corpus for materials informatics (FC-CoMIcs). A comprehensive dataset was constructed manually by identifying 12 critical entities and two relationship types between pairs of the entities. Our methodology involves data annotation, integration, and fine-tuning of transformer-based models to enhance information extraction accuracy. We assess the impact of different BERT variants on extraction performance and investigate the effects of annotation consistency. Experimental evaluations demonstrate that the fine-tuned PubMedBERT model achieves the highest NER F1-score of 82.19% and the MatSciBERT model attains the best RE F1-score of 66.10%. Furthermore, the comparison with human annotators highlights the reliability of fine-tuned models for ORR catalyst extraction, demonstrating their potential for scalable and automated literature analysis. The results indicate that domain-specific BERT models outperform general scientific models like BlueBERT for ORR catalyst extraction. Information extraction ; Scalability ; Scientific literature ; Informatics ; Annotation ; Fuel cells ; Data extraction ; Scientific modelling Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Topic Modeling
Authors: Madhavi Kumari (22259452) | Prabha Garg (1625956)
Source: Figshare (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Enhanced ChemicalReaction Mining through CombinedModel Adaptation and Prompt Engineering Extracting structured chemical reaction information from the scientific literature remains challenging due to the complexity of chemical language and diverse reporting formats. This study presents a novel three-stage framework that combines quantized low-rank adaptation fine-tuning, entity self-grounding, and dynamic prompt engineering for enhanced chemical reaction extraction. The approach systematically addresses domain adaptation, accuracy refinement, and task optimization through progressive enhancement stages. Evaluation on 26,785 reaction descriptions demonstrates substantial improvements over existing methods, achieving F<sub>1</sub> scores of 92.62% for reactants, 90.72% for products, 89.64% for solvents, and 88.83% for reagents. Chemical identifier recognition shows strong performance, with IUPAC names achieving 93.88% recall. The framework outperforms specialized systems like ChemRxnBERT (78.7% F<sub>1</sub>) and recent LLM approaches (83.0% F<sub>1</sub>) while requiring significantly less training data compared to domain-adaptive pretraining approaches and computational resources. This methodology establishes an effective framework for chemical reaction information extraction from unstructured text, enabling applications in automated literature mining, synthesis planning, and chemical informatics. Adaptation (eye) ; Identifier ; Information extraction ; Chemical process ; Domain (mathematical analysis) ; Task (project management) ; Cheminformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Rathore, Charan Prakash | Ray, Saumi | Kumar, Dhruv
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Evaluating LLMs for Zeolite Synthesis Event Extraction (ZSEE): A Systematic Analysis of Prompting Strategies Extracting structured information from zeolite synthesis experimental procedures is critical for materials discovery, yet existing methods have not systematically evaluated Large Language Models (LLMs) for this domain-specific task. This work addresses a fundamental question: what is the efficacy of different prompting strategies when applying LLMs to scientific information extraction? We focus on four key subtasks: event type classification (identifying synthesis steps), trigger text identification (locating event mentions), argument role extraction (recognizing parameter types), and argument text extraction (extracting parameter values). We evaluate four prompting strategies - zero-shot, few-shot, event-specific, and reflection-based - across six state-of-the-art LLMs (Gemma-3-12b-it, GPT-5-mini, O4-mini, Claude-Haiku-3.5, DeepSeek reasoning and non-reasoning) using the ZSEE dataset of 1,530 annotated sentences. Results demonstrate strong performance on event type classification (80-90\% F1) but modest performance on fine-grained extraction tasks, particularly argument role and argument text extraction (50-65\% F1). GPT-5-mini exhibits extreme prompt sensitivity with 11-79\% F1 variation. Notably, advanced prompting strategies provide minimal improvements over zero-shot approaches, revealing fundamental architectural limitations. Error analysis identifies systematic hallucination, over-generalization, and inability to capture synthesis-specific nuances. Our findings demonstrate that while LLMs achieve high-level understanding, precise extraction of experimental parameters requires domain-adapted models, providing quantitative benchmarks for scientific information extraction. Argument (complex analysis) ; Event (particle physics) ; Identification (biology) ; Information extraction ; Task (project management) ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; X-ray Diffraction in Crystallography
Authors: Rathore, Charan Prakash | Ray, Saumi | Kumar, Dhruv
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Evaluating LLMs for Zeolite Synthesis Event Extraction (ZSEE): A Systematic Analysis of Prompting Strategies Extracting structured information from zeolite synthesis experimental procedures is critical for materials discovery, yet existing methods have not systematically evaluated Large Language Models (LLMs) for this domain-specific task. This work addresses a fundamental question: what is the efficacy of different prompting strategies when applying LLMs to scientific information extraction? We focus on four key subtasks: event type classification (identifying synthesis steps), trigger text identification (locating event mentions), argument role extraction (recognizing parameter types), and argument text extraction (extracting parameter values). We evaluate four prompting strategies - zero-shot, few-shot, event-specific, and reflection-based - across six state-of-the-art LLMs (Gemma-3-12b-it, GPT-5-mini, O4-mini, Claude-Haiku-3.5, DeepSeek reasoning and non-reasoning) using the ZSEE dataset of 1,530 annotated sentences. Results demonstrate strong performance on event type classification (80-90\% F1) but modest performance on fine-grained extraction tasks, particularly argument role and argument text extraction (50-65\% F1). GPT-5-mini exhibits extreme prompt sensitivity with 11-79\% F1 variation. Notably, advanced prompting strategies provide minimal improvements over zero-shot approaches, revealing fundamental architectural limitations. Error analysis identifies systematic hallucination, over-generalization, and inability to capture synthesis-specific nuances. Our findings demonstrate that while LLMs achieve high-level understanding, precise extraction of experimental parameters requires domain-adapted models, providing quantitative benchmarks for scientific information extraction. Argument (complex analysis) ; Event (particle physics) ; Identification (biology) ; Information extraction ; Task (project management) ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; X-ray Diffraction in Crystallography
Authors: Andy Xu | Rohan Desai | Lili Wang | Gabriel Hope | Ethan Ritz
Source: ArXiv.org (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising approach to improve correctness in LLMs, however, in many scientific problems, the objective is not necessarily to produce the correct answer, but instead to produce a diverse array of candidates which satisfy a set of constraints. We study this challenge in the context of materials generation. To this end, we introduce PLaID++, an LLM post-trained for stable and property-guided crystal generation. We find that performance hinges on our crystallographic representation and reward formulation. First, we introduce a compact, symmetry-informed Wyckoff text representation which improves computational efficiency and encourages generalization from physical priors. Second, we demonstrate that temperature scaling acts as an entropy regularizer which counteracts mode collapse and encourages exploration. By encoding symmetry constraints directly into text and guiding model outputs towards desirable chemical space, PLaID++ generates structures that are thermodynamically stable, unique, and novel at a $\sim$50\% greater rate than prior methods and conditionally generates structures with desired space group properties. Our work demonstrates the potential of adapting post-training techniques from natural language processing to materials design, paving the way for targeted and efficient discovery of novel materials. Correctness ; Reinforcement learning ; Language model ; Representation (politics) ; Generalization ; Context (archaeology) ; Set (abstract data type) ; Scaling Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Crystallography and molecular interactions
Authors: Jungyeon Hwang | Taehyun Park | Jinyoung Jeong | Seungpyo Kang | Junho Song | Jonghyuk Kim | Ahhyun Jeong | Woojin Shin | Jungim Han | Joonghee Won | Kyoungmin Min
Source: Materials & Design (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Data-driven curation of organometallic precursor boiling points via natural language processing The development of high-performance semiconductor devices relies on precise control of thin-film deposition techniques, notably Chemical Vapor Deposition (CVD) and Atomic Layer Deposition (ALD). Central to these processes are organometallic precursors, whose boiling points control volatility and vapor delivery conditions, thereby constraining processing windows and precursor feasibility. However, boiling point data for these precursors are scattered across unstructured literature and underrepresented in commercial databases. To address this gap, we developed a Natural Language Processing (NLP) pipeline to systematically extract, validate, and organize boiling point data from 5,442 peer-reviewed articles since 2000. Using the ChemDataExtractor toolkit, we curated 1,902 candidate data points, followed by rigorous multistage filtration to remove duplicates, non-standard nomenclature, non-organometallic compounds, and erroneous entries. The validated dataset comprises 75 organometallic precursors with boiling points from 21 °C to 181 °C. Comparative analysis with CAS Registry and SciFinder revealed that over 80 % of these data points were previously undocumented, underscoring the novelty of the dataset. This work demonstrates the utility of NLP-driven literature mining in building chemically precise databases and offers a scalable framework to accelerate precursor characterization and materials discovery in semiconductor manufacturing and ceramic thin-film applications. Boiling point ; Chemical vapor deposition ; Scalability ; Boiling ; Volatility (finance) ; Point (geometry) ; International Temperature Scale of 1990 ; Semiconductor industry Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Benjamin
W. Walls (22028774) | Suljo Linic (2087662)
Source: Figshare (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Use of Large LanguageModels for Extracting and AnalyzingData from Heterogeneous Catalysis Literature Extracting experimentally measured heterogeneous catalysis data from the text of research articles into structured databases would facilitate the rapid screening of catalysts with target properties and the development of models capable of directly predicting experimental outcomes. This text mining task has been transformed by the release of large language models (LLMs) capable of following general natural language instructions, which have made it possible to mine text without the need to train task-specific models or define comprehensive expression-matching rules. Here, we develop and share a text mining tool called CatMiner that extracts arbitrary user-specified structure–environment–property data using LLMs. It is agnostic to LLM choice, with both OpenAI GPT models and open-source Llama and DeepSeek models supported without modification. We benchmark the ability of CatMiner to rapidly extract useful data from abundant published literature by focusing on a case study of the oxidative coupling of methane. We explore how model choice and prompting strategies affect extraction quality. Key capabilities, including the use of domain knowledge, iterative prompting, and document-wide context handling are shown to be critical for effective performance. We identify situations where CatMiner struggles and suggest reporting standards for the community to make catalysis data easier to extract going forward. CatMiner enables the creation of machine-readable catalysis datasets, streamlining access to experimental insights buried in the literature. Context (archaeology) ; Benchmark (surveying) ; Task (project management) ; Domain (mathematical analysis) ; Natural language ; Data modeling ; Language model ; Experimental data Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Scientific Computing and Data Management
Authors: Siyu Yan | Zian Li | Muhan Zhang
Source: ArXiv.org (preprint, 2025-06-16)
Field: Materials Science — Materials Chemistry

GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining The pretraining-finetuning paradigm has powered major advances in domains such as natural language processing and computer vision, with representative examples including masked language modeling and next-token prediction. In molecular representation learning, however, pretraining tasks remain largely restricted to node-level denoising, which effectively captures local atomic environments but is often insufficient for encoding the global molecular structure critical to graph-level property prediction tasks such as energy estimation and molecular regression. To address this gap, we introduce GeoRecon, a graph-level pretraining framework that shifts the focus from individual atoms to the molecule as an integrated whole. GeoRecon formulates a graph-level reconstruction task: during pretraining, the model is trained to produce an informative graph representation that guides geometry reconstruction while inducing smoother and more transferable latent spaces. This encourages the learning of coherent, global structural features beyond isolated atomic details. Without relying on external supervision, GeoRecon generally improves over backbone baselines on multiple molecular benchmarks including QM9, MD17, MD22, and 3BPA, demonstrating the effectiveness of graph-level reconstruction for holistic and geometry-aware molecular embeddings. Representation (politics) ; Encoding (memory) ; Property (philosophy) ; Focus (optics) ; Molecular graph ; Graph ; Feature learning Machine Learning in Materials Science ; Medical Imaging Techniques and Applications ; Scientific Computing and Data Management
Authors: Hong Li | Haidong Wang
Source: Transactions of Nonferrous Metals Society of China (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

DNMKG: A method for constructing domain of nonferrous metals knowledge graph based on multiple corpus To address the underutilization of Chinese research materials in nonferrous metals, a method for constructing a domain of nonferrous metals knowledge graph (DNMKG) was established. Starting from a domain thesaurus, entities and relationships were mapped as resource description framework (RDF) triples to form the graph’s framework. Properties and related entities were extracted from open knowledge bases, enriching the graph. A large-scale, multi- source heterogeneous corpus of over 1×10 9 words was compiled from recent literature to further expand DNMKG. Using the knowledge graph as prior knowledge, natural language processing techniques were applied to the corpus, generating word vectors. A novel entity evaluation algorithm was used to identify and extract real domain entities, which were added to DNMKG. A prototype system was developed to visualize the knowledge graph and support human−computer interaction. Results demonstrate that DNMKG can enhance knowledge discovery and improve research efficiency in the nonferrous metals field. Graph ; Knowledge graph ; Domain (mathematical analysis) ; Nonferrous metal ; Computer science ; Artificial intelligence ; Theoretical computer science ; Materials science ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Metal Extraction and Bioleaching ; Welding Techniques and Residual Stresses
Authors: Hatcher Jasmine Hatcher-Lamarre | Jaehyeon Do | Carlos Soto | Dohyun Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Context Card–Guided Approach for Enhanced Large Language Model Information Extraction for Optimizing Isotope Production Workflow Workflow ; Information extraction ; Knowledge base ; Context (archaeology) ; Python (programming language) ; Ground truth ; Task (project management) ; Knowledge extraction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: R.M. Zahrah | Norsuria Mahmed | Mohd Natashah Norizan | I.S.M. Binti | Y. Nurfina
Source: Archives of Metallurgy and Materials (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Recent Progress in the Utilization of Silver Nanoparticle and its Derivatives as Photocatalyst: a Mini Bibliometric Study The narrow bandgaps of silver nanostructure and its derivatives make them photocatalytic-efficient. This bibliometric examines silver nanoparticle research in photocatalysis to identify its growth, research gap, and trends. The 1,941 Web of Science Core Collection publications from 2018 to 2023 were used as research data. The search used Publish or Perish, and the investigation was established on topic area with titles, abstracts, keywords, and terms co-occurrence in the study of silver-based photocatalyst. The visualisation was done with VOSviewer. The number of papers on silver nanoparticles and their derivatives as photocatalysts fluctuated, peaking in 2019. The publications focused on visible-light-irradiated photocatalysts. These findings also revealed a research gap that can be filled by studying silver derivatives including silver chloride, silver oxide, silver sulfide, and silver iodide. This bibliometric study should help researchers examine silver nanoparticles in photocatalysis. Industrial chemistry ; Photocatalysis ; Materials science ; Materials processing ; Nanoparticle ; Nanotechnology ; Process engineering ; Chemical engineering ; Biochemical engineering ; Engineering ; Chemistry ; Catalysis ; Organic chemistry Advanced Nanomaterials in Catalysis
Authors: Rupali Hulavale | Arvind Varma
Source: International Journal of Environmental Sciences (article, 2025-08-20)
Field: Materials Science — Polymers and Plastics

Optimization Of Synthesis And Characterization Of The Novel Optical And Electrical Properties Of Layered Transition Metal Doped In Semiconductor This study presents a detailed investigation into the optimization of synthesis and characterization of layered transition metal-doped semiconductors, focusing on their enhanced optical and electrical properties. CoFe₂O₄ magnetic nanoparticles were synthesized using a microwave-assisted hydrothermal method and integrated into organic semiconducting polymers MEH-PPV and P3HT through solution processing and spin coating. The resulting thin films exhibited altered energy band structures and charge transport dynamics. Optical band gaps of MEH-PPV and P3HT were reduced to 2.15 eV and 1.97 eV, respectively, indicating enhanced light absorption. PL spectra revealed red-shifted emission peaks with increased intensity in films compared to solutions, confirming molecular ordering and efficient exciton recombination. Electrical measurements showed hole mobility reduction by 40% upon CoFe doping, with hole density increasing to 2.1×10¹⁸ cm⁻³ (Device 2) compared to 1×10¹⁸ cm⁻³ (Device 1). Temperature-dependent J–V analysis from 98 K to 300 K confirmed a transition from ohmic to SCLC behavior. The optimized curing temperature for RR-P3HT films was found to be 120°C, yielding minimal background doping and improved transport. These findings provide insights into how transition metal doping, processing conditions, and temperature influence semiconductor functionality, with implications for OLEDs, OPVs, sensors, and spintronic devices. Characterization (materials science) ; Doping ; Materials science ; Semiconductor ; Transition metal ; Optoelectronics ; Nanotechnology ; Chemistry ; Catalysis ; Organic chemistry Polymer Nanocomposite Synthesis and Irradiation ; Chemical and Physical Properties of Materials ; Conducting polymers and applications
Authors: Baisen Chen | Ziwang Liu | Xiaoshuai Yang | Xiaolu Zhou | Ling Xia | Gongke Li
Source: Chemical Science (article, 2025-12-29)
Field: Chemistry — Spectroscopy

The progress of the cyclic strategy in separation and detection Analytical chemistry is undergoing a transformation from the traditional linear model to a cyclic model, driving separation and detection technologies toward greater efficiency and sustainability. The core lies in introducing the temporal dimension into the separation and detection process through the cyclic strategy. This review discusses the implementation of the cyclic strategy in separation and detection technologies, beginning with an explanation of two fundamental modes including macroscopic flow cycling and microscopic reaction cycling, along with an analysis of the energy and key device requirements. Furthermore, it explores the application of the cyclic strategy in sample preparation, chromatographic separation, electric field-driven separation, as well as in spectrum, electrochemical and mass spectrometry detection. Typical cases demonstrate that by incorporating the temporal dimension, the cyclic strategy significantly enhances separation efficiency, detection sensitivity, information dimensionality, dynamic monitoring capabilities and process sustainability within confined spaces and limited resources. Then the review outlines future challenges and potential directions for cyclic strategies in separation and detection, offering forward looking perspectives for further research. This review aims to clarify that the cyclic strategy represents not merely a collection of technical methods, but a methodological framework that leverages spatiotemporal synergy to address future challenges in complex analytical systems. Separation (statistics) ; Process (computing) ; Dimension (graph theory) ; Key (lock) ; Transformation (genetics) ; Chromatographic separation ; Separation of concerns ; Separation method Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Analytical chemistry methods development