jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Liu | Siru Ouyang | Xianrui Zhong | Jiawei Han | Huimin Zhao
Source: ArXiv.org (preprint, 2025-08-01)
Field: Materials Science — Materials Chemistry

FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models Large language models (LLMs) have gained significant attention in chemistry. However, most existing datasets center on molecular-level property prediction and overlook the role of fine-grained functional group (FG) information. Incorporating FG-level data can provide valuable prior knowledge that links molecular structures with textual descriptions, which can be used to build more interpretable, structure-aware LLMs for reasoning on molecule-related tasks. Moreover, LLMs can learn from such fine-grained information to uncover hidden relationships between specific functional groups and molecular properties, thereby advancing molecular design and drug discovery. Here, we introduce FGBench, a dataset comprising 625K molecular property reasoning problems with functional group information. Functional groups are precisely annotated and localized within the molecule, which ensures the dataset's interoperability thereby facilitating further multimodal applications. FGBench includes both regression and classification tasks on 245 different functional groups across three categories for molecular property reasoning: (1) single functional group impacts, (2) multiple functional group interactions, and (3) direct molecular comparisons. In the benchmark of state-of-the-art LLMs on 7K curated data, the results indicate that current LLMs struggle with FG-level property reasoning, highlighting the need to enhance reasoning capabilities in LLMs for chemistry tasks. We anticipate that the methodology employed in FGBench to construct datasets with functional group-level information will serve as a foundational framework for generating new question-answer pairs, enabling LLMs to better understand fine-grained molecular structure-property relationships. The dataset and evaluation code are available at https://github.com/xuanliugit/FGBench. Property (philosophy) ; Benchmark (surveying) ; Construct (python library) ; Interoperability ; Meaning (existential) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Adrian Ehrenhofer | Thomas Wallmersperger | Gianaurelio Cuniberti
Source: ArXiv.org (article, 2025-07-19)
Field: Materials Science — Materials Chemistry

What do Large Language Models know about materials? Large Language Models (LLMs) are increasingly applied in the fields of mechanical engineering and materials science. As models that establish connections through the interface of language, LLMs can be applied for step-wise reasoning through the Processing-Structure-Property-Performance chain of material science and engineering. Current LLMs are built for adequately representing a dataset, which is the most part of the accessible internet. However, the internet mostly contains non-scientific content. If LLMs should be applied for engineering purposes, it is valuable to investigate models for their intrinsic knowledge -- here: the capacity to generate correct information about materials. In the current work, for the example of the Periodic Table of Elements, we highlight the role of vocabulary and tokenization for the uniqueness of material fingerprints, and the LLMs' capabilities of generating factually correct output of different state-of-the-art open models. This leads to a material knowledge benchmark for an informed choice, for which steps in the PSPP chain LLMs are applicable, and where specialized models are required. Vocabulary ; The Internet ; Ignorance ; Language model ; Interface (matter) ; Table (database) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Material Selection and Properties
Authors: Estefanía Fernández-Villanueva (17774277) | Patricia Pérez-Bailac (13227832) | Pablo G. Lustemberg (10009447) | Ana B. Hungría (1675891) | Laura Pascual (123506) | Renato Cataluña (22684370) | Jose A. Vidal-Moya (2233693) | Teresa Blasco (1355007) | M. Verónica Ganduglia-Pirovano (1834411) | Arturo Martínez-Arias (1795576)
Source: Figshare (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Fine-Tuning Catalysts: The Role of Support Nanomorphologyin Shaping Cu/CeO<sub>2</sub> CO-PROX Properties Understanding how oxide nanomorphology directs metal–support interactions is key to designing selective, low-cost catalysts. The preferential oxidation of CO (CO-PROX) is vital for purifying H<sub>2</sub> streams for fuel cell applications, as even trace amounts of CO strongly poison the electrode catalysts. Cu/CeO<sub>2</sub> systems provide a cost-effective alternative to noble metals, yet the influence of ceria morphology on the performance remains unclear. Here, we compare low-loaded Cu catalysts supported on CeO<sub>2</sub> nanospheres and nanocubes. Although distinct in shape, electron microscopy, low-temperature CO adsorption infrared spectroscopy, and DFT calculations reveal surface reconstructions in nanocubes that diminish structural differences between the two supports. Nevertheless, the Cu/nanosphere catalyst shows higher CO oxidation activity, while the Cu/nanocube catalyst offers superior CO<sub>2</sub> selectivity and a broader full-conversion temperature window. In situ DRIFTS and DFT spectra attribute these contrasts to stronger CO adsorption sites in the nanocube system. Copper speciation and the nature of surface carbonyls were resolved through complementary techniques, including STEM-HAADF imaging, XEDS mapping, EPR, and CO adsorption IR spectroscopy, together with DFT. These results demonstrate that subtle variations in ceria morphology steer interfacial chemistry and reaction pathways, providing design principles for next-generation Cu-based catalysts for CO-PROX and related oxidation reactions. Catalysis ; Adsorption ; Selectivity ; Oxide ; Infrared spectroscopy ; Copper ; Noble metal ; Heterogeneous catalysis ; Redox Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysts for Methane Reforming
Authors: Agada Joseph Oche | Arpan Biswas
Source: ArXiv.org (preprint, 2025-08-08)
Field: Materials Science — Materials Chemistry

Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review Large language models (LLMs) have emerged as powerful tools for knowledge-intensive tasks across domains. In materials science, to find novel materials for various energy efficient devices for various real-world applications, requires several time and cost expensive simulations and experiments. In order to tune down the uncharted material search space, minimizing the experimental cost, LLMs can play a bigger role to first provide an accelerated search of promising known material candidates. Furthermore, the integration of LLMs with domain-specific information via retrieval-augmented generation (RAG) is poised to revolutionize how researchers predict materials structures, analyze defects, discover novel compounds, and extract knowledge from literature and databases. In motivation to the potentials of LLMs and RAG in accelerating material discovery, this paper presents a broad and systematic review to examine the recent advancements in applying LLMs and RAG to key materials science problems. We survey state-of-the-art developments in crystal structure prediction, defect analysis, materials discovery, literature mining, database integration, and multi-modal retrieval, highlighting how combining LLMs with external knowledge sources enables new capabilities. We discuss the performance, limitations, and implications of these approaches, and outline future directions for leveraging LLMs to accelerate materials research and discovery for advancement in technologies in the area of electronics, optics, biomedical, and energy storage. Key (lock) ; Order (exchange) ; Systematic review ; Energy (signal processing) ; Materials processing Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Xiaoqing Liu | Yangshuai Wang | Teng Zhao
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

Beyond Adam: Disentangling Optimizer Effects in the Fine-Tuning of Atomistic Foundation Models Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability across chemical spaces. Although fine-tuning is essential for adapting these pretrained models to specific target systems, the influence of the optimization algorithm on this process remains insufficiently characterized. In this work, we perform a rigorous benchmark of seven first-order optimizers, including Adam, AdamW, RAdam, SGD, LAMB, Ranger, and ScheduleFree, for the fine-tuning of foundation models across molecular, crystalline, and liquid regimes. We evaluate these algorithms based on energy and force accuracy for both in-distribution and out-of-distribution configurations, as well as their impact on downstream physical properties such as elastic moduli, phonon spectra, and interfacial dynamics. We interpret these empirical results through a preconditioning framework that views each optimizer as a data-dependent linear transformation of the gradient. This analysis clarifies how different update rules impose specific spectral filters on the effective loss Hessian. Across all regimes, AdamW and ScheduleFree achieve superior curvature conditioning and force accuracy, whereas stochastic gradient descent exhibits slow convergence and instability. Furthermore, we demonstrate that a brief second-order refinement stage reduces residual anisotropy in the loss landscape and enhances the fidelity of physical observables without increasing inference costs. These findings provide conceptual insight and practical guidance for selecting and designing optimizers to ensure the stable and efficient fine-tuning of universal interatomic potentials. Inference ; Benchmark (surveying) ; Foundation (evidence) ; Process (computing) ; Convergence (economics) ; Observable ; Curvature ; Transformation (genetics) ; Interatomic potential ; Noise (video) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Tongwei Wu | Shiyu Du | Yiming Zhang | Honggang Li
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models Navigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations such as data sparsity and cold-start issues. This study presents a novel recommendation model that integrates domain-specific knowledge graphs with large language models (LLMs) to enhance recommendation accuracy in alloy material selection. A knowledge graph for alloys is developed, encapsulating technical material data and relationships to improve retrieval and recommendation outcomes. LLMs are employed for label clustering and natural language-based instruction-following to craft user profiles and enhance data representation. Two graph enhancement strategies, integrated with attention mechanisms, effectively capture user preferences. Experimental results on a ferroalloy dataset demonstrate the model’s superior performance compared to baseline methods, significantly addressing data sparsity while offering personalized, accurate recommendations. This research bridges the gap between knowledge graphs and LLMs in recommendation systems, contributing a flexible, intelligent solution to streamline material selection processes. Bridging (networking) ; Computer science ; Selection (genetic algorithm) ; Alloy ; Natural language processing ; Knowledge management ; Artificial intelligence ; Materials science ; Metallurgy ; Computer security Machine Learning in Materials Science
Authors: Jing‐Hang Wu | Ran Shi | Xiao Zhou | Liang Zhang | Kong Y. Chen | Han‐Qing Yu | Yuen Wu
Source: Angewandte Chemie (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Empowering Chemistry Experts with Large Language Models for Literature Interpretation in Single‐Atom Catalysis Toward Advanced Oxidation Abstract Large language models (LLMs) hold considerable promise for large‐scale data extraction from scientific literatures for catalyst design and practical optimization. Yet, turning such outputs into reliable, formalized chemical knowledge would heavily rely on domain expertise rather than end‐to‐end automation. Herein, we present a human‐in‐the‐loop workflow integrating LLM‐facilitated structured data extraction with iterative, expert‐guided curation and analysis. As a proof of concept, we take single‐atom catalysts (SACs) for advanced oxidation processes (AOPs) as an example, enabling efficient data extraction, rigorous curation, and statistically driven interpretation. Thus, we uncover the key correlations among metal types, coordination environments, reaction substances, and catalytic performance, providing deeper mechanism insights into SAC‐driven AOPs. In contrast to fully automated, end‐to‐end models, our approach relies on human‐driven optimization at multiple stages, and underscores human insight as central to understand LLM outputs. By introducing human‐driven prompt refinement, model comparison, and expert‐led analysis, our method ensures that human cognition remains central to interpreting LLM outputs and converting structured data into reliable scientific knowledge. Our work addresses the limitations inherent in fully automated, end‐to‐end methodologies and effectively bridges the gap between structured outputs and catalytically meaningful insights. Workflow ; Interpretation (philosophy) ; Domain (mathematical analysis) ; Key (lock) ; Domain knowledge ; Mechanism (biology) ; Data extraction Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Yung-Jin Weng | Liqiang Gao | Li Zhu | Jian Huang
Source: ArXiv.org (preprint, 2025-09-14)
Field: Materials Science — Materials Chemistry

MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis Recently, large language models (LLMs) have achieved remarkable breakthroughs in general domains such as programming and writing, and have demonstrated strong potential in various scientific research scenarios. However, the capabilities of AI models in the highly specialized field of materials characterization and analysis have not yet been systematically or sufficiently validated. To address this gap, we present MatQnA, the first multi-modal benchmark dataset specifically designed for material characterization techniques. MatQnA includes ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), etc. We employ a hybrid approach combining LLMs with human-in-the-loop validation to construct high-quality question-answer pairs, integrating both multiple-choice and subjective questions. Our preliminary evaluation results show that the most advanced multi-modal AI models (e.g., GPT-4.1, Claude 4, Gemini 2.5, and Doubao Vision Pro 32K) have already achieved nearly 90% accuracy on objective questions in materials data interpretation and analysis tasks, demonstrating strong potential for applications in materials characterization and analysis. The MatQnA dataset is publicly available at https://huggingface.co/datasets/richardhzgg/matQnA. Characterization (materials science) ; Benchmark (surveying) ; Field (mathematics) ; Construct (python library) ; Mainstream ; Interpretation (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Qi Ou | Hongshuai Wang | Muni Zhuang | Shangqian Chen | Lele Liu | Ning Wang | Zhifeng Gao
Source: arXiv (Cornell University) (preprint, 2025-01-17)
Field: Materials Science — Materials Chemistry

High-Accuracy Physical Property Prediction for Organics via Molecular Representation Learning: Bridging Data to Discovery The ongoing energy crisis has underscored the urgent need for energy-efficient materials with high energy utilization efficiency, prompting a surge in research into organic compounds due to their environmental compatibility, cost-effective processing, and versatile modifiability. To address the high experimental costs and time-consuming nature of traditional trial-and-error methods in the discovery of highly functional organic compounds, we apply the 3D transformer-based molecular representation learning algorithm to construct a pre-trained model using 60 million semi-empirically optimized structures of small organic molecules, namely, Org-Mol, which is then fine-tuned with public experimental data to obtain prediction models for various physical properties. Despite the pre-training process relying solely on single molecular coordinates, the fine-tuned models achieves high accuracy (with $R^2$ values for the test set exceeding 0.95). These fine-tuned models are applied in a high-throughput screening process to identify novel immersion coolants among millions of automatically constructed ester molecules, resulting in the experimental validation of two promising candidates. This work not only demonstrates the potential of Org-Mol in predicting bulk properties for organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials. Bridging (networking) ; Property (philosophy) ; Computer science ; Representation (politics) ; Physical property ; Data mining ; Artificial intelligence ; Machine learning ; Data science ; Materials science ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jiaqing Xie (1646089) | Weida Wang (772695) | Ben Gao (16936521) | Zhuo Yang (314040) | Haiyuan Wan (22551570) | Shufei Zhang (6386759) | Tianfan Fu (18845938) | Yuqiang Li (2016439)
Source: Figshare (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

QCBench: EvaluatingLarge Language Models on Domain-SpecificQuantitative Chemistry Quantitative chemistry is central to modern chemical research, yet the ability of large language models (LLMs) to perform its rigorous, step-by-step calculations remains underexplored. To fill this gap, we propose QCBench, a <b>Q</b>uantitative <b>C</b>hemistry oriented benchmark comprising 350 computational chemistry problems across 7 chemistry subfields, which contains analytical chemistry, bio-organic chemistry, general chemistry, inorganic chemistry, physical chemistry, polymer chemistry and quantum chemistry. To systematically evaluate the mathematical reasoning abilities of large language models (LLMs), they are categorized into three tiers: easy, medium, and difficult. Each problem, rooted in realistic chemical scenarios, is structured to prevent heuristic shortcuts and demand explicit numerical reasoning. QCBench enables fine-grained diagnosis of computational weaknesses, reveals model-specific limitations across difficulty levels, and lays the groundwork for future improvements such as domain-adaptive fine-tuning or multimodal integration. Evaluations on 24 LLMs demonstrate a consistent performance degradation with increasing task complexity, highlighting the current gap between language fluency and scientific computation accuracy. Code for QCBench is available at https://github.com/jiaqingxie/QCBench. Heuristic ; Benchmark (surveying) ; Computation ; Computational model ; Fluency ; Task (project management) ; Mathematical model ; Modeling language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Chun-Yen Wu | Guo-Hao Jiang | Yu-Che Kan | Yu-Chun Huang | Chi-Cheng Chiu | Chien-Chung Shih | Yan-Cheng Lin
Source: Journal of Materials Chemistry C (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Fine-tuning the backbone coplanarity and energy level of diketopyrrolopyrrole-based conjugated polymers for single-walled carbon nanotube sorting and field-effect transistor applications Three DPP polymers are applied to wrap and sort the semiconducting carbon nanotubes. The chemical structures, energy level alignments, and the field-effect transistor device comprising the semiconducting carbon nanotube channel are presented. Carbon nanotube ; Coplanarity ; Conjugated system ; Polymer ; Carbon nanotube field-effect transistor ; Transistor ; Sorting ; Carbon fibers Carbon Nanotubes in Composites ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Vishal Dey | Xiao Hu | Xia Ning
Source: arXiv (Cornell University) (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability and generalizability to novel optimization tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable out-of-domain generalizability to novel tasks. To demonstrate LLMs' potential for molecule optimization, we introduce MuMOInstruct, the first high-quality instruction-tuning dataset specifically focused on complex multi-property molecule optimization tasks. Leveraging MuMOInstruct, we develop GeLLMOs, a series of instruction-tuned LLMs for molecule optimization. Extensive evaluations across 5 in-domain and 5 out-of-domain tasks demonstrate that GeLLMOs consistently outperform state-of-the-art baselines. GeLLMOs also exhibit outstanding zero-shot generalization to unseen tasks, significantly outperforming powerful closed-source LLMs. Such strong generalizability demonstrates the tremendous potential of GeLLMOs as foundational models for molecule optimization, thereby tackling novel optimization tasks without resource-intensive retraining. MuMOInstruct, models, and code are accessible through https://github.com/ninglab/GeLLMO. Property (philosophy) ; Molecule ; Pure mathematics ; Mathematics ; Physics ; Computer science ; Mathematical economics ; Combinatorics ; Philosophy ; Quantum mechanics ; Epistemology Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods