jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zimo Zhao | Lin Hu | Honghui Wang
Source: Materials (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Fine-Tuned Large Language Models for High-Accuracy Prediction of Band Gap and Stability in Transition Metal Sulfides This study presents a fine-tuned Large Language Model approach for predicting band gap and stability of transition metal sulfides. Our method processes textual descriptions of crystal structures directly, eliminating the need for complex feature engineering required by traditional ML and GNN approaches. Using a strategically selected dataset of 554 compounds from the Materials Project database, we fine-tuned GPT-3.5-turbo through nine consecutive iterations. Performance metrics improved significantly, with band gap prediction R<sup>2</sup> values increasing from 0.7564 to 0.9989, while stability classification achieved F1 > 0.7751. The fine-tuned model demonstrated superior generalization ability compared to both GPT-3.5 and GPT-4.0 models, maintaining high accuracy across diverse material structures. This approach is particularly valuable for new material systems with limited experimental data, as it can extract meaningful features directly from text descriptions and transfer knowledge from pre-training to domain-specific tasks without relying on extensive numerical datasets. Generalization ; Stability (learning theory) ; Computer science ; Domain (mathematical analysis) ; Feature (linguistics) ; Transition metal ; Artificial intelligence ; Algorithm ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Inorganic Chemistry and Materials
Authors: Yanbo Zhang | Sumeer Ahmad Khan | A. K. M. Firoj Mahmud | Huck Yang | Alexander Lavin | Michael Levin | Jérémy Frey | Jared Dunnmon | James H. Evans | Alan Bundy | Sašo Džeroski | Jesper Tegnér | Héctor Zenil
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with 'reasoning', mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone. Transformative learning ; Process (computing) ; Scientific progress ; Productivity ; Sociology of scientific knowledge ; Criticism ; Scientific method Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Rihm, Simon D | Kondinski, Aleksandar | Kraft, Markus
Source: Apollo (University of Cambridge) (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Product design, synthesis, and lab automation with The World Avatar Automation ; Interoperability ; Avatar ; Product (mathematics) ; Laboratory automation ; Robotics ; Robot Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Honghao Chen | Hongxuan Liu | Yishen Tew | Xiangshi Ren | Xiaojin Tang | Xiaonan Wang
Source: ChemRxiv (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Distilling Knowledge from Catalysis Literature with Long-Context LLM Agents Decades of catalysis knowledge remain locked in unstructured prose, hindering data-driven discovery. Existing text-mining tools struggle to establish the synthesis-structure-performance relationships critical for catalyst knowledge discovery, as they rarely connect synthesis protocols in one section with the resulting material properties and performance outcomes reported elsewhere. Here, we present CATDA (Corpus-aware Automated Text-to-Graph Catalyst Discovery Agent), a long-context large language model (LLM)–driven agentic framework that reads full documents and distills them into actionable, provenance–tracked knowledge graphs linking material properties, multi-step synthesis, conditions, and testing outcomes. Applied at corpus scale, CATDA extracts data with near-human fidelity (F1 = 0.983) and a 12-fold speedup over manual curation. This structured knowledge is made accessible through two synergistic applications: a DatasetAgent for exporting machine-learning-ready tables, and a CatAgent providing a conversational, citation-linked interface for interactive discovery. The high-quality dataset enabled the training of a predictive model for ethylbenzene conversion, while simultaneously exposing systemic challenges such as feature sparsity and protocol heterogeneity in the source literature. By transforming the literature into a queryable and computable resource, CATDA offers a scalable route to accelerate large-scale data analysis, quantitative modeling, and rational catalyst design paradigm. Context (archaeology) ; Knowledge management ; Computer science ; History Machine Learning in Materials Science
Authors: Boris N. Slautin | Yongtao Liu | Barakati, Kamyar | Yu Liu | Reece Emery | Seungbum Hong | Astita Dubey | Vladimir V. Shvartsman | Doru C. Lupascu | Sheryl L. Sanchez | Mahshid Ahmadi | Yunseok Kim | Evgheni Strelcov | Keith A. Brown | Philip D. Rack | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2025-01-05)
Field: Materials Science — Materials Chemistry

Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization. Frontier ; Throughput ; Computer science ; Data science ; Nanotechnology ; Computer architecture ; Materials science ; Geography ; Telecommunications ; Archaeology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Solange María de Almeida | Vijay Shelake | S. Fernandes | Hemant Khanolkar
Source: International Journal of Computer Applications (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Harnessing Generative AI for Precision Chemistry: Autonomous Molecular Design, Retrosynthetic Planning, and Validated Discovery Pipeline Generative artificial intelligence (AI) is revolutionizing chemistry by designing novel molecules,predicting reactions, and accelerating discovery.Advanced models-ranging from SMILES-based VAEs and transformers to graph neural networks and diffusion frameworks-learn from massive databases (e.g.PubChem, ChEMBL) to navigate chemical space (~1033 molecules), generate valid structures with tailored properties, and enforce chemical constraints (valency, stereochemistry).These approaches outperform traditional methods in tasks like retrosynthesis planning and molecular optimization, with experimental validation such as Al-designed inorganic crystals synthesized in the lab.Importantly, this work emphasizes sustainable molecular and materials design.Generative pipelines are being adapted to minimize environmental impact via green-by-design objectives such as reducing process mass intensity (PMI), selecting biodegradable or non-toxic alternatives, and optimizing the atom economy.Al-guided materials like ZIF-8 frameworks and MOFs have been discovered via electrochemical synthesis with significantly lower energy use and waste, demonstrating ecoefficient design.Models are also used to design solvents and catalysts with improved environmental profiles.Such sustainability-aware Al tools support greener drug and materials development by integrating life-cycle thinking directly into molecular generative workflows Pipeline (software) ; Generative grammar ; Retrosynthetic analysis ; Generative model Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Yukun Wu | Aikaterini Vriza | Doga Ozgulbas | Rafael Vescovi | J. Zhou | Zhiyang Wang | Shiyu Hu | Yuepeng Zhang | Qiaomu Yang | Anna M. Österholm | John R. Reynolds | Subramanian Sankaranarayanan | Maria K. Y. Chan | Ian Foster | Henry Chan | Jianguo Mei | Jie Xu
Source: ChemRxiv (preprint, 2025-06-12)
Field: Materials Science — Polymers and Plastics

Autonomous Synthesis and Inverse Design of Electronic Polymers with High Efficiency and Accuracy The design and synthesis of functional polymers, aimed at targeted properties through specific structures, has long been challenged by their complex and often nonlinear structure-property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 hours, Polybot autonomously synthesized electrochromic polymers with targeted, unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible electrochromic polymer informatics database has also been created to foster knowledge exchange. Inverse ; Polymer ; Computer science ; Process engineering ; Materials science ; Mathematics ; Engineering ; Composite material ; Geometry Conducting polymers and applications
Authors: Pierre-Paul De Breuck | Hai‐Chen Wang | Gian‐Marco Rignanese | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Generative AI for crystal structures: a review Abstract The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We outline architectures, representations, conditioning mechanisms, data sources, metrics, and applications, and organize existing models into a unified taxonomy. Generative grammar ; Generative model ; Crystal (programming language) ; Training set ; Generative Design Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Peter Bøggild | Timothy J. Booth | Bjarke S. Jessen | Abhay Shivayogimath | Nolan Lassaline | Stephan Hofmann | Oliver J. Burton | Kim Daasbjerg | Anders Smith | Kasper Nørgaard | Amaia Zurutuza | Terrance Barkan | Andrew J Pollard
Source: Apollo (University of Cambridge) (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Protocols and tools to enable reproducibility in 2D materials research Transparency (behavior) ; Novelty ; Reliability (semiconductor) ; Protocol (science) ; Reproducibility Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Fan Nie | Lan Feng | Haotian Ye | Weixin Liang | Pan Lu | Huaxiu Yao | Alexandre Alahi | James Zou
Source: ArXiv.org (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often impractical. Existing training-free methods, including manually or automated designed workflows, typically demand substantial human effort or yield suboptimal results. This paper proposes Weak-for-Strong Harnessing (W4S), a novel framework that customizes smaller, cost-efficient language models to design and optimize workflows for harnessing stronger models. W4S formulates workflow design as a multi-turn markov decision process and introduces reinforcement learning for agentic workflow optimization (RLAO) to train a weak meta-agent. Through iterative interaction with the environment, the meta-agent learns to design increasingly effective workflows without manual intervention. Empirical results demonstrate the superiority of W4S that our 7B meta-agent, trained with just one GPU hour, outperforms the strongest baseline by 2.9% ~ 24.6% across eleven benchmarks, successfully elevating the performance of state-of-the-art models such as GPT-3.5-Turbo and GPT-4o. Notably, W4S exhibits strong generalization capabilities across both seen and unseen tasks, offering an efficient, high-performing alternative to directly fine-tuning strong models. Workflow ; Markov decision process ; Reinforcement learning ; Process (computing) ; Generalization ; Baseline (sea) ; Training (meteorology) ; Iterative and incremental development Machine Learning in Materials Science ; Machine Learning and Data Classification ; Artificial Intelligence in Healthcare and Education
Authors: Yaotian Yang | Yiwen Tang | Y. B. Chen | Chen Xiao | Jiang Qiu | Hao Xiong | Haoyu Yin | Zhiyao Luo | Yifei Zhang | Sijia Tao | Wentao Li | Qinghua Zhang | Yongping Li | Wanli Ouyang | Bin Zhao | Xiaonan Wang | Fei Wei
Source: ArXiv.org (preprint, 2025-05-19)
Field: Materials Science — Materials Chemistry

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomic-resolution electron microscopy offers a potential source of structural data, converting these images into simulation-ready formats remains labor-intensive and error-prone, creating a bottleneck for model training and validation. We introduce AutoMat, an end-to-end, agent-assisted pipeline that automatically transforms scanning transmission electron microscopy (STEM) images into atomic crystal structures and predicts their physical properties. AutoMat combines pattern-adaptive denoising, physics-guided template retrieval, symmetry-aware atomic reconstruction, fast relaxation and property prediction via MatterSim, and coordinated orchestration across all stages. We propose the first dedicated STEM2Mat-Bench for this task and evaluate performance using lattice RMSD, formation energy MAE, and structure-matching success rate. By orchestrating external tool calls, AutoMat enables a text-only LLM to outperform vision-language models in this domain, achieving closed-loop reasoning throughout the pipeline. In large-scale experiments over 450 structure samples, AutoMat substantially outperforms existing multimodal large language models and tools. These results validate both AutoMat and STEM2Mat-Bench, marking a key step toward bridging microscopy and atomistic simulation in materials science.The code and dataset are publicly available at https://github.com/yyt-2378/AutoMat and https://huggingface.co/datasets/yaotianvector/STEM2Mat. Bottleneck ; Pipeline (software) ; Bridging (networking) ; Microscopy ; Atomic model ; Leverage (statistics) ; Lattice (music) ; Transmission electron microscopy ; Crystal structure prediction Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Dilshod Nematov | Iskandar Raufov
Source: Preprints.org (preprint, 2025-09-16)
Field: Materials Science — Materials Chemistry

The Bright Future of Materials Science with AI: Self-Driving Laboratories and Closed-Loop Discovery Next-generation autonomous laboratories that combine machine learning, robotic synthesis, and in situ characterization is rapidly emerging as one of the key directions in modern materials science. By integrating active and deep learning algorithms with multi-level datasets ranging from quantum mechanical simulations to high-throughput flow-based experiments, these platforms establish closed-loop frameworks that guide complex nanofabrication processes in real time. Such AI-driven systems lower material and energy consumption, enable the precise design of nanostructures with tailored optical, electronic, and mechanical properties, and create research opportunities that were previously difficult to realize. This review provides a comprehensive overview of self-driving laboratories (SDLs), including their architectures, algorithmic strategies, and practical demonstrations such as the optimization of perovskite nanostructures, the development of nanoparticles for targeted drug delivery, and the synthesis of quantum dots with controlled emission. It also discusses existing methodological limitations, the need for standardized data practices, and challenges related to the integration of in situ techniques, while highlighting the prospects for creating fully digital pipelines for material production, as evidenced by recent autonomous laboratory demonstrations that progressed from initial hypotheses to functional prototypes within remarkably short timeframes. Characterization (materials science) ; Key (lock) ; Quantum dot ; Field (mathematics) ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Chiku Parida | Diptendu Roy | Juan Maria Garcia Lastra | Arghya Bhowmik
Source: ChemRxiv (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

Mining Chemical Space with Generative Modelsfor 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 we choose Li-ion battery(LIB) materials 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. We performed unsupervised clustering analysis using atomic neighborhood fingerprints to compare the distribution of generated structures against the training dataset and materials project(MP) data in the chemical space. Our multi-tired workflow for LIB materials combines universal crystal generative model with foundational machine learning potential to identify most promising stable (close to convex hull with respect to MP data) candidates for final Density Functional Theory (DFT) based stability calculations. Open Circuit Voltage (OCV) and specific capacity calculations on selected stable materials highlighted their potential as LIB materials. Among 91 identified Li-containing stable (≤ 0.03 eV/atom above MP convex hull) materials, we identified two novel cathode materials useful for LIB considering average OCV, OCV at highest and lowest state of charge, and the specific capacity. Chemical space ; Stability (learning theory) ; Cluster analysis ; Battery (electricity) ; Computer science ; Convex hull ; Generative grammar ; Generative model ; Space (punctuation) ; Materials science ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Regular polygon ; Physics ; Thermodynamics ; Geometry Machine Learning in Materials Science
Authors: Nakul Rampal | Dongrong Joe Fu | Chengbin Zhao | Hanan S. Murayshid | Albatool A. Abaalkhail | Nahla Alhazmi | Majed O. Alawad | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: Digital Discovery (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

An automated evaluation agent for Q&amp;A pairs and reticular synthesis conditions QAutoEval is an automated evaluation agent for Q&amp;A datasets and reticular synthesis conditions, enabling reproducible benchmarking and transparent assessment of LLM driven workflows in reticular chemistry. Reticular connective tissue ; Workflow ; Reticular activating system ; Benchmarking ; Process (computing) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Aoni Xu | Tongao Yao | Pengfei Ou | Weijie Yang
Source: AI for Materials (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Let Materials Science Data Learn to Reason Materials science excels at collecting numbers but often loses sight of the conditions, mechanisms, and provenance that give those numbers meaning. We have vast databases, long papers, and a forest of plots, yet the bench-top questions persist: what works here, now, under these conditions and why? This Viewpoint argues for a practical posture: treat conditions as first principles, make mechanisms explicit, and attach sources and uncertainty to every claim. We outline a plain work loop, i.e., ask → retrieve → compute → validate → write back, that increases learning speed without requiring “hero” datasets or black-box models. We demonstrate how these habits are applied in practice across catalysis, solid-state batteries, and hydrogen storage, and we highlight platform design choices (e.g., DigMat Platform) that enforce them. The goal is not fireworks, but fewer wrong turns: fairer comparisons, predictions tied to the exact world they apply to, and decisions that withstand daylight. Ask price ; Work (physics) ; Sight ; Key (lock) ; Term (time) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography