jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatemeh Shahhosseini | Arash Marioriyad | Ali Momen | Mahdieh Soleymani Baghshah | Mohammad Hossein Rohban | Shaghayegh Haghjooy Javanmard
Source: arXiv (Cornell University) (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language models (LLMs) have enabled the generation of coherent and plausible scientific ideas, yet the nature and limits of their creative capabilities remain poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, focusing on how different approaches trade off novelty and scientific validity. We organize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we adopt two complementary creativity frameworks: Boden taxonomy to characterize the expected level of creative novelty, and Rhodes 4Ps framework to analyze the aspects or sources of creativity emphasized by each method. By aligning methodological developments with cognitive creativity frameworks, this survey clarifies the evaluation landscape and identifies key challenges and directions for reliable and systematic LLM-based scientific discovery. Novelty ; Creativity ; Ideation ; Scientific modelling ; Taxonomy (biology) ; Creativity technique ; Scientific literature ; Sociology of scientific knowledge Machine Learning in Materials Science ; Topic Modeling ; Creativity in Education and Neuroscience
Authors: Xin Li | Zhengwei Huang | Sheng QUAN | Cheng Peng | Xiaoming Ma
Source: npj Computational Materials (article, 2025-07-19)
Field: Materials Science — Materials Chemistry

SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction Small Language Models offer an efficient alternative for structured information extraction. We present SLM-MATRIX, a multi-path collaborative reasoning and verification framework based on SLMs, designed to extract material names, numerical values, and physical units from materials science literature. The framework integrates three complementary reasoning paths: a multi-agent collaborative path, a generator–discriminator path, and a dual cross-verification path. SLM-MATRIX achieves an accuracy of 92.85% on the BulkModulus dataset and reaches 77.68% accuracy on the MatSynTriplet dataset, both outperforming conventional methods and single-path models. Moreover, experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability. Ablation studies evaluate the effects of agent number, Mixture-of-Agents (MoA) depth, and discriminator design on overall performance. Overall, SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks. Computer science ; Extraction (chemistry) ; Trajectory ; Matrix (chemical analysis) ; Data extraction ; Natural language processing ; Artificial intelligence ; Chromatography ; Chemistry Machine Learning in Materials Science ; Semantic Web and Ontologies ; Catalysis and Hydrodesulfurization Studies
Authors: Dong Han | Zizheng Ai | Ping Cai | Shuangfang Lu | Jianpeng Chen | Zihao Ye | Shuzhou Sun | Ben Gao | Liang Ge | Weida Wang | Xiangxin Zhou | Xihui Liu | Ming‐Jai Su | Wanli Ouyang | Lei Bai | D.C. Zhou | Tao Xu | Yuqiang Li | Shufei Zhang
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Here, we introduce ChemBOMAS: a large language model (LLM)-enhanced multi-agent system that accelerates BO through synergistic data- and knowledge-driven strategies. Firstly, the data-driven strategy involves an 8B-scale LLM regressor fine-tuned on a mere 1% labeled samples for pseudo-data generation, robustly initializing the optimization process. Secondly, the knowledge-driven strategy employs a hybrid Retrieval-Augmented Generation approach to guide LLM in dividing the search space while mitigating LLM hallucinations. An Upper Confidence Bound algorithm then identifies high-potential subspaces within this established partition. Across the LLM-refined subspaces and supported by LLM-generated data, BO achieves the improvement of effectiveness and efficiency. Comprehensive evaluations across multiple scientific benchmarks demonstrate that ChemBOMAS set a new state-of-the-art, accelerating optimization efficiency by up to 5-fold compared to baseline methods. Linear subspace ; Bayesian optimization ; Set (abstract data type) ; Initialization ; Bayesian probability ; Branch and bound ; Baseline (sea) ; Upper and lower bounds Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Sophia Rupprecht | Qinghe Gao | Tanuj Karia | Artur M. Schweidtmann
Source: Current Opinion in Chemical Engineering (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Multi-agent systems for chemical engineering: a review and perspective Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MASs within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows. Workflow ; Perspective (graphical) ; Foundation (evidence) ; Sociology of scientific knowledge ; Chemical process Machine Learning in Materials Science ; Multi-Agent Systems and Negotiation ; Process Optimization and Integration
Authors: David O. Obada | Shittu B. Akinpelu | Simeon A. Abolade | Mkpe O. Kekung | Emmanuel Okafor | Syam Kumar R | Aniekan Magnus Ukpong | Akinlolu Akande
Source: Crystals (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning for Photocatalytic Materials Design and Discovery Traditionally, the development and optimisation of photocatalytic materials have relied on experimental approaches and density functional theory (DFT) calculations. Although these methods have driven significant scientific progress, they are increasingly constrained by high computational costs, lengthy development cycles, and limited scalability. In recent years, machine learning (ML) has emerged as a powerful and sustainable alternative, offering a data-driven framework that accelerates materials discovery through rapid and accurate property prediction. This review highlights the essential components of the ML workflow data collection, feature engineering, model selection, and validation while exploring its application in predicting photocatalytic properties. It further discusses recent advances in forecasting key characteristics such as band edge positions, charge carrier mobility, and surface reactivity using both supervised and unsupervised ML techniques. Persistent challenges, including data scarcity, model interpretability, and generalisability, are also addressed, alongside potential strategies to improve the robustness and reliability of ML-driven materials design. By combining high prediction accuracy with superior computational efficiency, ML holds the potential to revolutionise high-throughput screening and guide the systematic development of next-generation photocatalysts. Robustness (evolution) ; Workflow ; Photocatalysis ; Reliability (semiconductor) ; Model validation ; Experimental data ; Deep learning ; Supervised learning Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Xuexue Pan | Yuanlin Xie | Chujun Li | Yangyang He | Yitong Zhang | Yilin Wang | Zhichun Li | Pai Peng | Jun Wang
Source: Journal of Electronic Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials With the urgent demand for high-performance energy storage materials in the global energy transition, traditional experimental trial and error methods are difficult to meet the rapid research and development needs owing to long cycles and high costs. In recent years, the deep integration of computational materials science and artificial intelligence (AI) technology has provided revolutionary tools for the rational design and performance optimization of energy storage materials. This article systematically reviews the progress of research on energy storage material computation and AI systems. At the traditional method level, quantum mechanics computation (such as VASP, Quantum ESPRESSO), molecular dynamics (such as LAMMPS, GROMACS), and high-throughput computing platforms (such as Materials Project) have achieved accurate predictions of material electronic structure, interface dynamics, and high-throughput screening. At the AI-driven level, generative models (GNoME, 3D-GPT), graph neural networks (MEGNet, CGCNN), and experimental computational closed–loop systems (such as the autonomous driving laboratory A-Lab) have significantly accelerated the discovery and reverse design of new materials. Further focusing on the integration trend of multi-scale modeling and AI, physical information-driven AI models (DPMD, PINNs) and cross-scale integration platforms (ASE, MedeA) are driving the collaborative improvement of material simulation accuracy and efficiency. However, data scarcity, computational bottlenecks caused by multi-physics coupling, and barriers to tool industrialization remain current challenges. In the future, sustainable design paradigms, open-source ecological construction, and human-machine collaboration models will lead the research and development of energy storage materials into the era of “digital priority.” This article aims to provide a technical roadmap reference for interdisciplinary research and call for collaboration between academia and industry to overcome key bottlenecks and accelerate the innovation breakthrough and large-scale application of energy storage materials. Computational model ; Computation ; Efficient energy use ; Interface (matter) ; Energy storage ; Convergence (economics) ; Computer data storage ; Energy consumption ; Quantum computer Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Nanoporous metals and alloys
Authors: David Balcells
Source: ChemRxiv (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

Co-Intelligent Design of Catalysis Research with Large Language Models: Hype or Reality? Large language models are making a strong impact on broad fields of science, pushing the efforts to minimize hallucinations and to support specialized language. Co-intelligence, the synergy between human and artificial intelligences, can have a strong potential in research through the use of large language models. This Viewpoint focused on the question: Can large language models collaborate with chemists in the design of catalysis research? In a conversation based on a few prompts giving minimal guidance, ChatGPT-4o proved its co-design abilities at different levels of detail, making a major contribution to the definition of a research project concept and its workflows. Relevant flaws were also observed, highlighting the importance of involving critical thinking from highly-educated researchers. The assessment of the results by ChatGPT-4o itself and other large language models, including DeepSeek and Gemini, was also explored. Computer science ; Catalysis ; Knowledge management ; Process management ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Haote Li | Sumon Sarkar | Wenxin Lu | Patrick Loftus | Tianyin Qiu | Yu Shee | Abbigayle E. Cuomo | John-Paul Webster | H. Ray Kelly | Vidhyadhar Manee | Sanil Sreekumar | Frédéric G. Buono | Robert H. Crabtree | Timothy R. Newhouse | Víctor S. Batista
Source: ChemRxiv (preprint, 2025-01-31)
Field: Materials Science — Materials Chemistry

Collective Intelligence of Specialized Language Models Guides Realization of de novo Chemical Synthesis While hundreds of thousands of new chemical reactions are reported annually, efficient use of this vast collection of synthetic knowledge remains a persistent challenge in modern chemistry. Recent applications of large language models (LLMs) have shown promise, but systems that reliably work for de novo compounds and molecular transformations have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-Driven Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols. In contrast to existing approaches relying on agentic models, MOSAIC leverages the open-source Llama3.1-8B-instruct architecture. By training 2,489 specialized chemical experts on Voronoi-clustered reaction spaces, we establish a scalable paradigm that delivers reproducible and human-readable experimental protocols for complex syntheses. Experimental validation demonstrates MOSAIC's ability to predict and execute previously unreported transformations, including challenging reactions via Buchwald-Hartwig amination, Suzuki coupling, and olefin metathesis. We validate this approach through the successful synthesis of over 35 novel compounds spanning pharmaceuticals, materials, agrochemicals, and cosmetics. This framework establishes a new relationship between computational and experimental chemistry, providing a foundation for accelerated chemical discovery across disciplines. Computer science ; Scalability ; Data science ; Realization (probability) ; Artificial intelligence ; Nanotechnology ; Biochemical engineering ; Chemistry ; Engineering ; Database Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Jiyan He | Haoxiang Guan | Weitao Feng | Yaosen Min | Jingwei Yi | Kunsheng Tang | Shuai Cheng Li | Jie Zhang | Kejiang Chen | Wenbo Zhou | Xing Xie | Weiming Zhang | Nenghai Yu | Shuxin Zheng
Source: AI for Science (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Controlling risks of AI in chemical science with agents Controlling risks of AI in chemical science with agents, He, Jiyan, Guan, Haoxiang, Feng, Weitao, Min, Yaosen, Yi, Jingwei, Tang, Kunsheng, Li, Shuai, Zhang, Jie, Chen, Kejiang, Zhou, Wenbo, Xie, Xing, Zhang, Weiming, Yu, Nenghai, Zheng, Shuxin Computer science ; Risk analysis (engineering) ; Business Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Chiku Parida | Diptendu Roy | Juan Maria García Lastra | Arghya Bhowmik
Source: Batteries & Supercaps (article, 2025-09-14)
Field: Materials Science — Materials Chemistry

Mining Chemical Space with Generative Models for Battery Materials Generative models represent a powerful new paradigm for accelerating the discovery of novel materials across vast chemical space. To evaluate the viability of deploying generalized crystal generative models for application specific discovery tasks, here Li‐ion battery (LIB) materials are chosen as a case study. The pretrained MatterGen model is used to generate diverse crystalline structures, conditioned for stability and tested for uniqueness and novelty for promising Li‐containing compositions. An unsupervised clustering analysis is performed using atomic neighborhood fingerprints to compare the distribution of generated structures against the training dataset and materials project (MP) data in the chemical space. The multitired workflow for LIB materials combines a universal crystal generative model with foundational machine learning potential to identify the most promising stable (close to convex hull with respect to MP data) candidates for final density functional theory‐based stability calculations. Open circuit voltage (OCV) and specific capacity calculations on selected stable materials highlight their potential as LIB materials. Among 91 identified Li‐containing stable ( eV/atom above MP convex hull) materials, two novel cathode materials are identified useful for LIB, considering average OCV, OCV at highest and lowest state of charge, and the specific capacity. Stability (learning theory) ; Generative grammar ; Cluster analysis ; Generative model ; Battery (electricity) ; Chemical space ; Workflow ; Unsupervised learning ; Novelty detection ; Convex hull Machine Learning in Materials Science ; Extraction and Separation Processes ; X-ray Diffraction in Crystallography
Authors: Dhanush Patil | Jie Tian | Kun Jiang | Clarissa C. Westover | Sri Vaishnavi Thummalapalli | Arunachalam Ramanathan | Arpita Shome | Natalie Crutchfield | M. Taylor Sobczak | Sean Lee | Hongyue Sun | Elizabeth J. Brisbois | Hitesh Handa | Emily M. Wilts | Xianqiao Wang | Kenan Song
Source: Advanced Science (article, 2025-11-29)
Field: Materials Science — Polymers and Plastics

AI‐Directed 3D Printing of Hierarchical Polyurethane Foams Hierarchical porous materials enable next-generation protective, thermal, and biomedical devices by leveraging multiscale architectures with tunable mechanical and thermal properties. Current 3D printing techniques mainly yield periodic lattices and support limited polymer types, restricting patterning possibilities and scalability. Stochastic foam architectures, with heterogeneous and interconnected pores, mimic biological structures for improved energy dissipation and functional adaptability. However, scalable additive manufacturing of such foams remains scarcely explored. Here, a direct ink writing (DIW) strategy couples static mixer-enabled reactive extrusion and in situ polymerization to manufacture stochastic polyurethane (PU) foams at ambient conditions, eliminating post-processing. Precise control over pore size (0.2 µm to 1.2 mm), porosity (65-95%), and open-cell architecture delivers thermal conductivities down to 0.067 W m<sup>-1</sup> K<sup>-1</sup> and elastic recovery exceeding 90% after 5000 cycles. A multi-agent artificial intelligence framework enables the patterning of spatially organized, bioinspired motifs into print-ready CAD geometries, resulting in architecturally structured foams with tailored anisotropy and spatial thermal management. Flow-rate modulation further tunes morphology, optimizing the balance between stiffness and damping. This manufacturing platform integrates stochastic pore formation, AI-guided patterning, and mechanical-thermal optimization to realize scalable, customizable materials for impact protection, wearable thermotherapy, and adaptive healthcare, advancing digital manufacturing, smart materials, and personalized function. 3D printing ; Porosity ; Morphing ; Thermal ; Stiffness ; Polymer ; Inkwell ; Smart polymer ; Scalability Polymer composites and self-healing ; Polymer Foaming and Composites ; Pickering emulsions and particle stabilization
Authors: Junjie Fu | Chunjun Qin | Rui Zhou | Zhaohong Deng | Jing Hu | Peter H. Seeberger | Biao Yu | Jian Yin
Source: CCS Chemistry (article, 2025-11-13)
Field: Chemistry — Organic Chemistry

Artificial Intelligence for Predicting Stereoselectivity in Glycosylation Chemistry Glycosylation reactions are the cornerstone of carbohydrate synthesis, yet stereoselectivity has remained a long-standing challenge and a classic black box problem due to complex mechanistic ambiguities and a multitude of influencing factors. To move beyond laborious trial-and-error, early efforts established predictive frameworks based on empirical parameters and computational chemistry, laying a crucial foundation. This review examines the recent emergence of artificial intelligence (AI) as a data-driven paradigm to overcome this unpredictability. We highlight pioneering studies that employ diverse machine learning strategies, including applying transfer learning to address data scarcity, using random forest models with quantum chemical or empirical descriptors on curated datasets, and leveraging interpretable decision trees to predict outcomes from large-scale literature data. Beyond mere prediction, we showcase how AI is transitioning into a tool for discovery, identifying novel stereocontrol principles and guiding methodology development through active learning approaches like Bayesian optimization. Future progress, however, depends on overcoming key barriers in data standardization, advanced algorithms, and closed-loop automation. Addressing these has the potential to transform glycosylation from an intricate art into a predictive science, making the complexities of the glyco-world increasingly tractable. Glycosylation ; Stereoselectivity ; Applications of artificial intelligence ; Key (lock) ; Training set Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Catalysis for Biomass Conversion
Authors: Gary Tom | Edwin Yu | Naruki Yoshikawa | Kjell Jorner | Alán Aspuru‐Guzik
Source: PNAS Nexus (article, 2025-10-14)
Field: Chemistry — Physical and Theoretical Chemistry

Stereochemistry-aware string-based molecular generation This study investigates the impact of incorporating stereochemical information, a crucial aspect of computational drug discovery and materials design, in molecular generative modeling. We present a detailed comparison of stereochemistry-aware and conventionally stereochemistry-unaware string-based generative approaches, utilizing both genetic algorithms and reinforcement learning-based techniques. To evaluate these models, we introduce novel benchmarks specifically designed to assess the importance of stereochemistry-aware generative modeling. Our results demonstrate that stereochemistry-aware models generally perform on par with or surpass conventional algorithms across various stereochemistry-sensitive tasks. However, we also observe that in scenarios where stereochemistry plays a less critical role, stereochemistry-aware models may face challenges due to the increased complexity of the chemical space they must navigate. This work provides insights into the trade-offs involved in incorporating stereochemical information in molecular generative models and offers guidance for selecting appropriate approaches based on specific application requirements. Generative grammar ; Chemical space ; Generative model ; Reinforcement learning ; Drug discovery ; Computational model Various Chemistry Research Topics ; Chemical Synthesis and Analysis ; Supramolecular Chemistry and Complexes