jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prashant Singh
Source: Advanced Functional Materials (article, 2025-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets Abstract An electronic‐structure‐centered perspective is presented on permanent‐magnet (PM) design, highlighting two key levers, that is, saturation magnetization ( M s ), governed by 3d ‐band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin‐orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT‐based MAE/ J ij extraction, atomistic‐spin and micromagnetic modeling, and high‐throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic‐structure accuracy for small MAE (sensitive to functional choice, Hubbard U , and many‐body effects), ii) finite‐temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC‐weighted and orbital‐resolved fingerprints). Deep dives into the electronic‐structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT‐ and quantum‐mechanics‐based descriptors for discovery. Unbiased, structure‐driven exploration, coupled with high‐throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply‐chain risks, on future needs of designing “critical‐element‐free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare‐earth‐lean or rare‐earth free, high‐performance, sustainable PMs. Magnet ; Magnetocrystalline anisotropy ; Decoupling (probability) ; Anisotropy ; Anisotropy energy ; Limiting ; Magnetic anisotropy ; Context (archaeology) ; Coupling (piping) Magnetic Properties of Alloys ; Heusler alloys: electronic and magnetic properties ; Magnetism in coordination complexes
Authors: Song, Zhangde | Lu, Jieyu | Du, Yuanqi | Yu, Botao | Pruyn, Thomas M. | Huang, Yue | Guo, Kehan | Luo, Xiuzhe | Qu, Yuanhao | Qu, Yi | Wang, Yinkai | Wang, Haorui | Guo, Jeff | Gan, Jingru | Shojaee, Parshin | Luo, Di | Bran, Andres M | Li, Gen | Zhao, Qiyuan | Luo, Shao-Xiong Lennon | Zhang, Yuxuan | Zou, Xiang | Zhao, Wanru | Zhang, Yifan F. | Zhang, Wucheng | Zheng, Shunan | Zhang, Saiyang | Khan, Sartaaj Takrim | Rajabi-Kochi, Mahyar | Paradi-Maropakis, Samantha | Baltoiu, Tony | Xie, Fengyu | Chen, Tianyang | Huang, Kexin | Luo, Weiliang | Fang, Meijing | Yang, Xin | Cheng, Lixue | He, Jiajun | Hassoun, Soha | Zhang, Xiangliang | Wang, Wei | Reddy, Chandan K. | Zhang, Chao | Zheng, Zhiling | Wang, Mengdi | Cong, Le | Gomes, Carla P. | Hsieh, Chang-Yu | Nandy, Aditya | Schwaller, Philippe | Kulik, Heather J. | Jia, Haojun | Sun, Huan | Moosavi, Seyed Mohamad | Duan, Chenru
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Evaluating Large Language Models in Scientific Discovery Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasoning, hypothesis generation, and observation interpretation that drive scientific discovery. We introduce a scenario-grounded benchmark that evaluates LLMs across biology, chemistry, materials, and physics, where domain experts define research projects of genuine interest and decompose them into modular research scenarios from which vetted questions are sampled. The framework assesses models at two levels: (i) question-level accuracy on scenario-tied items and (ii) project-level performance, where models must propose testable hypotheses, design simulations or experiments, and interpret results. Applying this two-phase scientific discovery evaluation (SDE) framework to state-of-the-art LLMs reveals a consistent performance gap relative to general science benchmarks, diminishing return of scaling up model sizes and reasoning, and systematic weaknesses shared across top-tier models from different providers. Large performance variation in research scenarios leads to changing choices of the best performing model on scientific discovery projects evaluated, suggesting all current LLMs are distant to general scientific "superintelligence". Nevertheless, LLMs already demonstrate promise in a great variety of scientific discovery projects, including cases where constituent scenario scores are low, highlighting the role of guided exploration and serendipity in discovery. This SDE framework offers a reproducible benchmark for discovery-relevant evaluation of LLMs and charts practical paths to advance their development toward scientific discovery. Serendipity ; Benchmark (surveying) ; Scientific modelling ; Modular design ; Strengths and weaknesses ; Interpretation (philosophy) ; Scientific discovery ; Language model Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Computational and Text Analysis Methods
Authors: Islambek Ashyrmamatov | Su Ji Gwak | S. Jin | Ikhyeong Jun | Umit Volkan Ucak | Jay-Yoon Lee | Juyong Lee
Source: Experimental & Molecular Medicine (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

A survey on large language models in biology and chemistry Artificial intelligence (AI) is reshaping biomedical research by providing scalable computational frameworks suited to the complexity of biological systems. Central to this revolution are bio/chemical language models, including large language models, which are reconceptualizing molecular structures as a form of 'language' amenable to advanced computational techniques. Here we critically examine the role of these models in biology and chemistry, tracing their evolution from molecular representation to molecular generation and optimization. This review covers key molecular representation strategies for both biological macromolecules and small organic compounds-ranging from protein and nucleotide sequences to single-cell data, string-based chemical formats, graph-based encodings and three-dimensional point clouds-highlighting their respective advantages and inherent limitations in AI applications. The discussion further explores core model architectures, such as bidirectional encoder representations from transformers-like encoders, generative pretrained transformer-like decoders and encoder-decoder transformers, alongside their sophisticated pretraining strategies such as self-supervised learning, multitask learning and retrieval-augmented generation. Key biomedical applications, spanning protein structure and function prediction, de novo protein design, genomic analysis, molecular property prediction, de novo molecular design, reaction prediction and retrosynthesis, are explored through representative studies and emerging trends. Finally, the review considers the emerging landscape of agentic and interactive AI systems, showcasing briefly their potential to automate and accelerate scientific discovery while addressing critical technical, ethical and regulatory considerations that will shape the future trajectory of AI in biomedicine. Representation (politics) ; Key (lock) ; Function (biology) ; Generative grammar ; Scalability ; Biomedicine ; Tracing ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Hongchen Wang | Rafael Castañeda | Jay R. Werber | Yao Fehlis | Edward Kim | Jason Hattrick‐Simpers
Source: ArXiv.org (preprint, 2025-11-24)
Field: Materials Science — Materials Chemistry

Training-Free Active Learning Framework in Materials Science with Large Language Models Active learning (AL) accelerates scientific discovery by prioritizing the most informative experiments, but traditional machine learning (ML) models used in AL suffer from cold-start limitations and domain-specific feature engineering, restricting their generalizability. Large language models (LLMs) offer a new paradigm by leveraging their pretrained knowledge and universal token-based representations to propose experiments directly from text-based descriptions. Here, we introduce an LLM-based active learning framework (LLM-AL) that operates in an iterative few-shot setting and benchmark it against conventional ML models across four diverse materials science datasets. We explored two prompting strategies: one using concise numerical inputs suited for datasets with more compositional and structured features, and another using expanded descriptive text suited for datasets with more experimental and procedural features to provide additional context. Across all datasets, LLM-AL could reduce the number of experiments needed to reach top-performing candidates by over 70% and consistently outperformed traditional ML models. We found that LLM-AL performs broader and more exploratory searches while still reaching the optima with fewer iterations. We further examined the stability boundaries of LLM-AL given the inherent non-determinism of LLMs and found its performance to be broadly consistent across runs, within the variability range typically observed for traditional ML approaches. These results demonstrate that LLM-AL can serve as a generalizable alternative to conventional AL pipelines for more efficient and interpretable experiment selection and potential LLM-driven autonomous discovery. Benchmark (surveying) ; Selection (genetic algorithm) ; Active learning (machine learning) ; Stability (learning theory) ; Range (aeronautics) ; Feature (linguistics) ; Feature selection Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning and Algorithms
Authors: Chaojian Li | Zhifan Ye | Massimiliano Lupo Pasini | Jong Youl Choi | Cheng Wan | Yingyan Lin | Prasanna Balaprakash
Source: ArXiv.org (preprint, 2025-04-10)
Field: Materials Science — Materials Chemistry

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabyte-scale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling. Leverage (statistics) ; Scalability ; Scaling ; Codebase ; Multiscale modeling ; Scaling law ; Graph ; Property (philosophy) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Wenhao Dong | Qi Wang | Liping Ren | Jinjia Wei | Shaohua Shen | Jie Chen
Source: Catal (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Graph neural networks reshaping the paradigm of electrocatalyst design for green hydrogen production In response to the urgent global challenges of climate crisis and energy transition, green hydrogen has emerged as a clean energy carrier whose efficient production relies on advancements in electrocatalyst for water electrolysis. Traditional approaches to electrocatalyst design are constrained by high experimental and computational costs. Artificial intelligence, particularly graph neural networks (GNNs), offers a promising avenue to overcome this bottleneck. By automatically learning topological and geometric features from atomic graphs without the need for manual feature engineering, GNNs enable more accurate prediction of material properties and facilitate the discovery of novel structures. This article provides a systematic review of the evolution of electrocatalyst design, from traditional machine learning to the emerging GNN-driven paradigm, with a particular focus on recent advances in applying GNNs to the discovery of electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). By systematically categorizing GNN architectures (including invariant, equivariant and Transformer based models), analyzing current research findings, and offering forward-looking discussions on future applications, this review provides a comprehensive methodological framework. It aims to equip researchers with fundamental references for employing GNNs in electrocatalyst design, thereby accelerating the discovery of high-performance electrocatalytic materials and advancing green hydrogen production technology. Electrocatalyst ; Graph ; Artificial neural network ; Oxygen evolution ; Structuring ; Feature (linguistics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Zhixun Li | Bin Cao | Rui Jiao | Liang Wang | Ding Wang | Yang Liu | Dingshuo Chen | Jia Li | Qiang Liu | Yu Rong | Tong‐Yi Zhang | Jeffrey Xu Yu
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges. In recent years, the growing availability of high-quality materials data combined with rapid advances in Artificial Intelligence (AI) has opened new opportunities for accelerating materials discovery. Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. To fill this gap, this paper provides a comprehensive overview of recent progress in AI-driven materials generation. We first organize various types of materials and illustrate multiple representations of crystalline materials. We then provide a detailed summary and taxonomy of current AI-driven materials generation approaches. Furthermore, we discuss the common evaluation metrics and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future directions and challenges in this fast-growing field. The related sources can be found at https://github.com/ZhixunLEE/Awesome-AI-for-Materials-Generation. Underpinning ; Generative grammar ; Foundation (evidence) ; Taxonomy (biology) ; Benchmark (surveying) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: Shen Yuan | Shukai Gong | Hongteng Xu
Source:  (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset Over the past few years, the machine learning community has given increasing attention to chemical reaction prediction and retrosynthesis. Despite impressive achievements, the existing datasets in this field have gradually become the bottleneck of current research --- the limitation of dataset size and the lack of reaction condition information hinder the practicability of the current methods. In this study, we construct an information-enriched chemical reaction dataset called USPTO-LLM, with the help of large language models (LLMs). This dataset comprises over 247K chemical reactions extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. Experiments show that USPTO-LLM helps pre-train the existing retrosynthesis methods and the condition information in the dataset helps improve the model performance. The dataset is open-sourced at https://zenodo.org/records/14396156 and the annotation code is open-sourced at https://github.com/GONGSHUKAI/USPTO_LLM. Computer science ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Data Processing Techniques
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2025-01-06)
Field: Materials Science — Materials Chemistry

Is BigSMILES the Friend of Polymer Machine Learning? Machine learning (ML) has become a powerful tool in polymer science, with its success strongly relying on effective structural representations of polymers. While the Simplified Molecular Input Line Entry System (SMILES) is widely used due to its simplicity, it was originally designed for small molecules and struggles to capture the stochastic nature of polymers. Recently, BigSMILES has been introduced as a more compact and versatile representation of polymer structures. However, the relative performance of SMILES and BigSMILES in polymer ML tasks remains unexplored. In this study, we systematically evaluate SMILES and BigSMILES across 12 polymer-related tasks, including property prediction and inverse design, utilizing convolutional neural networks (CNNs) and large language models (LLMs). Our results show that BigSMILES enables faster training times due to its reduced token complexity, and achieves comparable or superior performance to SMILES in certain predictive tasks. Moreover, BigSMILES more accurately encodes chemical information and monomer connectivity for copolymers within LLM frameworks. This work serves as a starting point for a comprehensive evaluation of SMILES and BigSMILES in polymer ML applications, highlighting the potential of BigSMILES to streamline and accelerate polymer informatics workflows, particularly for complex systems like copolymers and polymer composites. Looking ahead, advancing polymer representations to integrate polymer chain structure, phase morphology, and processing parameters will be crucial for capturing the multifaceted relationships between polymer structure and properties, driving more accurate and efficient modeling. Computer science ; Psychology ; Cognitive science ; Artificial intelligence Machine Learning in Materials Science
Authors: Adrian Ehrenhofer | Thomas Wallmersperger | Gianaurelio Cuniberti
Source: Advanced Engineering Materials (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

What Do Large Language Models <i>Know</i> 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 (PSPP) chain of materials science and engineering. Current LLMs are built for adequately representing a dataset, which consists of a significant subset of the accessible internet. However, the internet mostly contains nonscientific content. If LLMs should be applied for engineering purposes, it is valuable to investigate models for their intrinsic knowledge: the capacity to generate correct information about materials. In the current work, for the example of the Periodic Table of Elements, 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 are highlighted. This leads to a material knowledge benchmark for an informed choice, for which steps in the PSPP chain LLMs can be applied and where specialized models are required. Vocabulary ; The Internet ; Benchmark (surveying) ; Language model ; Interface (matter) ; Chain (unit) ; Ignorance Machine Learning in Materials Science ; Material Selection and Properties ; Model Reduction and Neural Networks
Authors: Belmiro P.M. Duarte | Maria J. Moura
Source: Processes (article, 2025-06-23)
Field: Materials Science — Biomaterials

Unraveling the Degradation Kinetics of Genipin-Cross-Linked Chitosan Hydrogels via Symbolic Regression Chitosan hydrogels have gained attention in biomedical and pharmaceutical research due to their biocompatibility, biodegradability, and tunable properties. To enhance mechanical strength and to control swelling and degradation, chitosan is often cross-linked with either bio-based (e.g., genipin) or synthetic (e.g., glutaraldehyde) agents. A comprehensive understanding of the degradation mechanisms of cross-linked chitosan hydrogels is essential, as it directly impacts performance optimization, regulatory compliance, and their integration into personalized medicine. Despite extensive studies, the fundamental mechanisms governing hydrogel degradation remain partially understood. In this work, we introduce a general data-driven framework based on symbolic regression to elucidate the degradation kinetics of hydrogels. Using genipin-cross-linked chitosan hydrogels as a model system, we analyze experimental degradation data to identify governing kinetic laws. Our results suggest that degradation proceeds primarily via a surface-mediated mechanism. The proposed approach provides a robust and interpretable method for uncovering mechanistic insights and is broadly applicable to other hydrogel systems. Genipin ; Chitosan ; Kinetics ; Self-healing hydrogels ; Degradation (telecommunications) ; Chemistry ; Chemical engineering ; Computer science ; Polymer chemistry ; Biochemistry ; Engineering ; Physics ; Telecommunications Nanocomposite Films for Food Packaging ; Hydrogels: synthesis, properties, applications ; Conducting polymers and applications