jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoel Zimmermann | Adib Bazgir | Alexander Al‐Feghali | Mehrad Ansari | Joshua D. Bocarsly | L. Catherine Brinson | Yuan Chiang | Defne Çırcı | Min–Hsueh Chiu | Nathan Daelman | Matthew L. Evans | Abhijeet Gangan | Janine George | Hassan Harb | Ghazal Khalighinejad | Sartaaj Takrim Khan | Sascha Klawohn | Magdalena Lederbauer | Soroush Mahjoubi | Bernadette Mohr | Seyed Mohamad Moosavi | Aakash Ashok Naik | Aleyna Beste Ozhan | Dieter Plessers | Aritra Roy | Fabian Schöppach | Philippe Schwaller | Carla Terboven | Katharina Ueltzen | Yue Wu | Shang Zhu | Jan Janßen | Calvin Li | Ian Foster | Ben Blaiszik
Source: ArXiv.org (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 34 total projects developed during the second annual Large Language Model Hackathon for Applications in Materials Science and Chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility. Workflow ; Scientific discovery ; Key (lock) ; Class (philosophy) ; Sociology of scientific knowledge ; Grand Challenges ; Property (philosophy) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Biomedical and Engineering Education
Authors: Zeyu Xia | Jinzhe Ma | Congjie Zheng | Shufei Zhang | Yuqiang Li | Hang Su | P. Hu | Changshui Zhang | Xingao Gong | Wanli Ouyang | Lei Bai | Dongzhan Zhou | Mao Su
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

An Agentic Framework for Autonomous Materials Computation Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery, yet their static knowledge and hallucination issues hinder autonomous research applications. Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific workflows. Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations. By embedding domain expertise, the agent ensures physically coherent multi-step workflows and consistently selects convergent, well-posed parameters, thereby enabling reliable end-to-end computational execution. A new benchmark of diverse computational tasks demonstrates that our system significantly outperforms standalone LLMs in both accuracy and robustness. This work establishes a verifiable foundation for autonomous computational experimentation and represents a key step toward fully automated scientific discovery. Workflow ; Key (lock) ; Domain (mathematical analysis) ; Benchmark (surveying) ; Automation ; Verifiable secret sharing ; Computational model Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Jaehyung Lee | Justin Ely | Karen Y C Zhang | Aparna Ajith | C.S. Campbell | Kamal Choudhary
Source: ArXiv.org (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Artificial intelligence is reshaping scientific discovery, yet its use in materials research remains limited by fragmented computational ecosystems, reproducibility challenges, and dependence on commercial large language models (LLMs). Here we introduce AGAPI (AtomGPT.org API), an open-access agentic AI platform that integrates more than eight open-source LLMs with over twenty materials-science API endpoints, unifying databases, simulation tools, and machine-learning models through a common orchestration framework. AGAPI employs an Agent-Planner-Executor-Summarizer architecture that autonomously constructs and executes multi-step workflows spanning materials data retrieval, graph neural network property prediction, machine-learning force-field optimization, tight-binding calculations, diffraction analysis, and inverse design. We demonstrate AGAPI through end-to-end workflows, including heterostructure construction, powder X-ray diffraction analysis, and semiconductor defect engineering requiring up to ten sequential operations. In addition, we evaluate AGAPI using 30+ example prompts as test cases and compare agentic predictions with and without tool access against experimental data. With more than 1,000 active users, AGAPI provides a scalable and transparent foundation for reproducible, AI-accelerated materials discovery. AGAPI-Agents codebase is available at https://github.com/atomgptlab/agapi. Workflow ; Codebase ; Scalability ; Bridging (networking) ; Orchestration ; Artificial neural network ; Asynchronous communication Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Malikussaid | Hilal Hudan Nuha | Isman Kurniawan
Source:  (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

VALID-Mol: A Systematic Framework for Validated LLM-Assisted Molecular Design Large Language Models demonstrate substantial promise for advancing scientific discovery, yet their deployment in disciplines demanding factual precision and specialized domain constraints presents significant challenges. Within molecular design for pharmaceutical development, these models can propose innovative molecular modifications but frequently generate chemically infeasible structures. We introduce VALID-Mol, a comprehensive framework that integrates chemical validation with LLM-driven molecular design, achieving an improvement in valid chemical structure generation from 3% to 83%. Our methodology synthesizes systematic prompt optimization, automated chemical verification, and domain-adapted fine-tuning to ensure dependable generation of synthesizable molecules with enhanced properties. Our contribution extends beyond implementation details to provide a transferable methodology for scientifically-constrained LLM applications with measurable reliability enhancements. Computational analyses indicate our framework generates promising synthesis candidates with up to 17-fold predicted improvements in target binding affinity while preserving synthetic feasibility. Reliability (semiconductor) ; Software deployment ; Domain (mathematical analysis) ; Model validation ; Component (thermodynamics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Tacke, Marius | Busch, Matthias | Bali, Kartik | Abdolazizi, Kian Philipp | Linka, Kevin | Cyron, Christian | Aydin, Roland
Source: TUHH Open Research (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Constitutive scientific generative agent (CSGA): leveraging large language models for automated constitutive model discovery Interpretability ; Generative grammar ; Language model ; Generative model ; Constitutive equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
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.org (article, 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: Hanzheng Li | Xi Fang | Yixuan Li | Chaozheng Huang | Junjie Wang | Xi Wang | Hongzhe Bai | Bojun Hao | Shenyu Lin | Huiqi Liang | Linfeng Zhang | Guolin Ke
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature The integration of Multimodal Large Language Models (MLLMs) into chemistry promises to revolutionize scientific discovery, yet their ability to comprehend the dense, graphical language of reactions within authentic literature remains underexplored. Here, we introduce RxnBench, a multi-tiered benchmark designed to rigorously evaluate MLLMs on chemical reaction understanding from scientific PDFs. RxnBench comprises two tasks: Single-Figure QA (SF-QA), which tests fine-grained visual perception and mechanistic reasoning using 1,525 questions derived from 305 curated reaction schemes, and Full-Document QA (FD-QA), which challenges models to synthesize information from 108 articles, requiring cross-modal integration of text, schemes, and tables. Our evaluation of MLLMs reveals a critical capability gap: while models excel at extracting explicit text, they struggle with deep chemical logic and precise structural recognition. Notably, models with inference-time reasoning significantly outperform standard architectures, yet none achieve 50\% accuracy on FD-QA. These findings underscore the urgent need for domain-specific visual encoders and stronger reasoning engines to advance autonomous AI chemists. Benchmark (surveying) ; Scientific reasoning ; Perception ; Scientific modelling ; Scientific literature ; Visualization ; Visual language ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Juliana Jansen Ferreira, special to C&EN
Source: C&EN Global Enterprise (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

To leverage AI, chemists need to ask the right questions Chat applications such as ChatGPT, Claude, and Gemini that are based on large language models (LLMs) are artificial intelligence technologies that are already being used by chemists for tasks ranging from literature review and hypothesis generation to regulatory interpretation and toxicological analysis. These systems can summarize complex papers, suggest novel molecular combinations, and even simulate experimental outcomes. In short, they’re accelerating the pace of discovery. This kind of technology is no longer just a tool in the chemist’s toolbox—it’s becoming a research collaborator.I’ve been exploring how LLM-based chats are reshaping the scientific discovery process in chemistry. In a recent study I presented at the American Chemical Society 2025 Fall meeting, my coauthors and I discussed the pitfalls of this transformation. As AI systems evolve from passive assistants to active cocreators, we must rethink how we interact with them. And how we ensure their outputs are trustworthy, ethical, and scientifically sound.In the aforementioned study, chemistry experts described their interactions with LLMs as a mix of curiosity, trial and error, and cautious optimism. They appreciated the speed and convenience but remained wary of overconfidence and lack of nuance in AI-generated responses. One of the most surprising findings from our research was how much effort goes into simply asking the right question. Prompt engineering, the art of crafting inputs to get meaningful outputs from AI, is now a critical skill. Yet most chemists and experts in other domains aren’t trained in it. They rely on intuition, iteration, and a bit of luck.As one Leverage (statistics) ; Pace ; Overconfidence effect ; Interpretation (philosophy) ; Ask price ; Process (computing) ; Surprise ; Intuition Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Computational Drug Discovery Methods
Authors: Chuan Qin | Xiangfan Chen | Chengrui Wang | Pengmin Wu | Xi Chen | Yihang Cheng | Jingyi Zhao | Meng Xiao | Xiangchao Dong | Qingqing Long | Bai‐Chuan Pan | Han Wu | Chengzan Li | Yuanchun Zhou | Hui Xiong | Hengshu Zhu
Source: ArXiv.org (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions: Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators Knowledge, Understanding, Reasoning, Multimodality, and Values spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 50 representative open-source and closed source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en. Benchmarking ; Benchmark (surveying) ; Resource (disambiguation) ; Key (lock) ; Perspective (graphical) ; Core (optical fiber) ; Quality (philosophy) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Bhanu Prakash Vangala | Sajid Mahmud | Pawan Neupane | Joel Selvaraj | Jianlin Cheng
Source: ArXiv.org (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability. Robustness (evolution) ; Consistency (knowledge bases) ; Benchmark (surveying) ; Graph ; Scene graph ; Contradiction Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Artificial Intelligence in Healthcare and Education
Authors: Bhanu Prakash Vangala | Sajid Mahmud | Pawan Neupane | Joel Selvaraj | Jianlin Cheng
Source: arXiv (Cornell University) (preprint, 2025-12-26)
Field: Materials Science — Materials Chemistry

HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability. Robustness (evolution) ; Consistency (knowledge bases) ; Benchmark (surveying) ; Graph ; Scene graph ; Contradiction Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Artificial Intelligence in Healthcare and Education
Authors: Yan Zeng
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Toward Autonomous Battery Materials Research: Advances and Challenges in Automated Synthesis Experimentation Advancements in energy technologies increasingly depend on the development of solid-state materials that serve as high-performance and sustainable electrodes and electrolytes. While computational methods, artificial intelligence, and design strategies can identify promising materials, synthesizing these materials often remains a significant bottleneck due to its inherent complexity and resource-intensive nature. To streamline the design–make–measure loop, an autonomous solid-state synthesis laboratory (named the A-Lab) powered by AI and automation was first established by a team in Berkeley led by Professor Ceder. As one of the initial members in this pioneering project, I will share the stories about the engineering innovations, scientific discoveries, and person experiences during the build of the A-Lab. Through case studies, I will highlight how the A-Lab holds promises to accelerate batteries materials research. Additionally, recognizing the importance of complementary solution-based methods, such as precipitation and hydrothermal synthesis, I will discuss recent efforts in my group aimed at automating these processes, from predicting optimal synthesis condition to automating experimental execution. Automation ; Battery (electricity) ; Bottleneck ; Energy (signal processing) ; Production (economics) ; Robot Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography