jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zuhair Ahmed Khan Taha | Mohammed Mudassir Uddin | Shahnawaz Alam
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

AgentCompress: Task-Aware Compression for Affordable Large Language Model Agents Large language models hold considerable promise for various applications, but their computational requirements create a barrier that many institutions cannot overcome. A single session using a 70-billion-parameter model can cost around $127 in cloud computing fees, which puts these tools out of reach for organizations operating on limited budgets. We present AgentCompress, a framework that tackles this problem through task-aware dynamic compression. The idea comes from a simple observation: not all tasks require the same computational effort. Complex reasoning, for example, is far more demanding than text reformatting, yet conventional compression applies the same reduction to both. Our approach uses a lightweight neural controller that looks at the first few tokens of each request, estimates how complex the task will be, and sends it to an appropriately quantized version of the model. This routing step adds only about 12 milliseconds of overhead. We tested the framework on 290 multi-stage workflows from domains including computer science, physics, chemistry, and biology. The results show a 68.3% reduction in computational costs while preserving 96.2% of the original success rate. These findings suggest that routing queries intelligently can make powerful language models substantially more affordable without sacrificing output quality Task (project management) ; Workflow ; Session (web analytics) ; Core (optical fiber) ; Cloud computing ; Simple (philosophy) ; Language model ; Artificial neural network Machine Learning in Materials Science ; Scientific Computing and Data Management ; Big Data and Digital Economy
Authors: Rushikesh Deotale | Adithya Srinivasan | Yuan Tian | tianyi zhang | Pavlos P. Vlachos | Hector Gomez
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods Finite element (FE) analysis guides the design and verification of nearly all manufactured objects. It is at the core of computational engineering, enabling simulation of complex physical systems, from fluids and solids to multiphysics systems. However, implementing FE codes and analyzing simulation results demands expertise across numerical analysis, continuum mechanics, and programming. Conventional Large Language Models (LLMs) can generate FE code, but they hallucinate, lack awareness of variational structures, and cannot close the loop from problem statement to a verified solution. Here, we propose ALL-FEM, an autonomous simulation system that integrates agentic AI with domain-specific, fine-tuned LLMs for FEniCS code generation across solid, fluid, and multiphysics applications. We construct a corpus of 1000+ verified FEniCS scripts by combining 500+ curated expert codes with a retrieval-augmented, multi-LLM pipeline that generates and filters codes for diverse PDEs, geometries, and boundary conditions. We used the corpus to fine-tune LLMs with 3B to 120B parameters. Our agentic framework orchestrates specialized agents, powered by fine-tuned LLMs, to formulate problems as PDEs, generate and debug code and visualize the results. We evaluated the system on 39 benchmarks that include problems of linear/nonlinear elasticity, plasticity, Newtonian/non-Newtonian flow, thermofluids, fluid-structure interaction, phase separation, and transport on moving domains. Embedded in a multi-agent workflow with runtime feedback, the best fine-tuned model (GPT OSS 120B) achieves code-level success of 71.79%, outperforming a non-agentic deployment of GPT 5 Thinking. By showing that relatively small, fine-tuned LLMs, orchestrated through agentic frameworks, can automate FE workflows, ALL-FEM offers a blueprint for autonomous simulation systems in computational science and engineering. Multiphysics ; Scripting language ; Toolchain ; Workflow ; Debugging ; Finite element method ; Construct (python library) ; Code (set theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Lattice Boltzmann Simulation Studies
Authors: Kan Deng | Xiangfei Wang | Guijing Duan | Chen Mo | Junkun Huang | Runqing Zhang | Ling Qian | Zhiguo Huang | Jize Han | Di Luo
Source: ArXiv.org (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations Recent advances in automated scientific discovery have shown remarkable promise across frontier research domains, with agent systems driven by large language models (LLMs) emerging as powerful tools for physics research. However, in practical applications, LLM scientific research is prone to hallucinations, highlighting the need for reliable verification and error-correction mechanisms. Here we introduce PhysVEC, an automated multi-agent framework for verifiable and error-correcting AI-driven physics research. PhysVEC incorporates a programming verifier and a scientific verifier to ensure both coding correctness and physical validity, and provides human-auditable evidence at each stage. We curate QMB100, an end-to-end research-level benchmark dataset consisting of $100$ tasks extracted from $21$ high impact articles that focus on quantum many-body physics. We evaluated PhysVEC with four frontier LLMs and found that it significantly outperformed baselines in both programming tests and scientific tests across all LLMs and task categories. PhysVEC demonstrates effective inference-time scaling and delivers accurate physical predictions through integrated verification and error-correction mechanisms, paving the way for reliable and interpretable AI physicists. Verifiable secret sharing ; Correctness ; Task (project management) ; Benchmark (surveying) ; Coding (social sciences) ; Quantum ; Focus (optics) Machine Learning in Materials Science ; Quantum many-body systems ; Scientific Computing and Data Management
Authors: Yingzhuo Han | Yingbo Wang | Yucheng Xue | Jiafu Shi | Xiaomeng Wang | Kenji Watanabe | Takashi Taniguchi | Jian Kang | Yuhang Jiang | Jinhao Mao
Source: Nature Communications (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Spectral signature of periodic modulation and shifting of pseudogap state in moiré system The nature of the pseudogap state is widely believed as a key to understanding the pairing mechanism underlying unconventional superconductivity. Although pseudogap states have recently been realized in moiré systems with extensive gate tunability, their local electronic structure remains largely unexplored. Here we show gate-tunable spontaneous symmetry breaking and shifting behavior of the pseudogap state in magic-angle twisted bilayer graphene using spectroscopic imaging scanning tunneling microscopy. A pronounced pseudogap emerges over a broad doping range and exhibits strong spatial modulation at the moiré scale. Spectroscopic imaging highlights a gap size modulation at moiré scale that is sensitive to the filling, indicative of a wave-like pseudogap feature. At certain dopings, the pseudogap size distribution causes a clear nematic order, or an anisotropic gap distribution. Our results have shed light on the complex nature of this pseudogap state, revealing critical insights into the phase diagram of correlated electron systems. Pseudogap ; Bilayer graphene ; Symmetry breaking ; Bilayer ; Phase diagram ; Scanning tunneling microscope ; Quantum tunnelling Graphene research and applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: Bo Zhang | Jinfeng Zhou | Yuxuan Chen | Jianing Yin | Minlie Huang | Hongning Wang
Source: ArXiv.org (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Grounding LLMs in Scientific Discovery via Embodied Actions Large Language Models (LLMs) have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and verifiable physical simulation. Existing solutions operate in a passive "execute-then-response" loop and thus lacks runtime perception, obscuring agents to transient anomalies (e.g., numerical instability or diverging oscillations). To address this limitation, we propose EmbodiedAct, a framework that transforms established scientific software into active embodied agents by grounding LLMs in embodied actions with a tight perception-execution loop. We instantiate EmbodiedAct within MATLAB and evaluate it on complex engineering design and scientific modeling tasks. Extensive experiments show that EmbodiedAct significantly outperforms existing baselines, achieving SOTA performance by ensuring satisfactory reliability and stability in long-horizon simulations and enhanced accuracy in scientific modeling. Embodied cognition ; Bridge (graph theory) ; Verifiable secret sharing ; Ground ; Reliability (semiconductor) ; Software ; Scientific discovery ; Work (physics) ; Scientific modelling Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Ziyang Cai | Harkirat Behl
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

AI Scientist via Synthetic Task Scaling With the advent of AI agents, automatic scientific discovery has become a tenable goal. Many recent works scaffold agentic systems that can perform machine learning research, but don't offer a principled way to train such agents -- and current LLMs often generate plausible-looking but ineffective ideas. To make progress on training agents that can learn from doing, we provide a novel synthetic environment generation pipeline targeting machine learning agents. Our pipeline automatically synthesizes machine learning challenges compatible with the SWE-agent framework, covering topic sampling, dataset proposal, and code generation. The resulting synthetic tasks are 1) grounded in real machine learning datasets, because the proposed datasets are verified against the Huggingface API and are 2) verified for higher quality with a self-debugging loop. To validate the effectiveness of our synthetic tasks, we tackle MLGym, a benchmark for machine learning tasks. From the synthetic tasks, we sample trajectories from a teacher model (GPT-5), then use the trajectories to train a student model (Qwen3-4B and Qwen3-8B). The student models trained with our synthetic tasks achieve improved performance on MLGym, raising the AUP metric by 9% for Qwen3-4B and 12% for Qwen3-8B. Pipeline (software) ; Task (project management) ; Metric (unit) ; Benchmark (surveying) ; Synthetic data ; Active learning (machine learning) Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Jiuxu Wang | Jie Wang | Feilong Yu | Jin Chen | Rongsheng Chen | Tie Geng | Rong Jin | Yiran Zhou | Tongwen Zheng | Guanhai Li | Xiaoshuang Chen | Wei Lu
Source: Light Science & Applications (article, 2026-03-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Continuous polarization–wavelength mapping with nonlocal metasurfaces Simultaneous and continuous control over polarization and wavelength-two orthogonal and information-rich degrees of freedom-remains a central challenge in metasurface photonics, long hindered by intrinsic dispersion constraints and structural degeneracy. Here, we customize continuous polarization-wavelength mapping through a nonlocal metasurface platform that decouples birefringent evolution from structural dispersion. We achieve programmable, spectrally resolved polarization shaping across the broadband mid-infrared regime by introducing an equivalent nonlocal Jones matrix formalism and a dimension-interlaced vectorial diffraction neural network. This framework enables fully continuous and arbitrarily prescribed mapping across the joint polarization-wavelength space-beyond the capabilities of segmented or interleaved metasurface designs. We experimentally demonstrate non-degenerate multicolor vectorial holography, broadband achromatic imaging, and arbitrary elliptical polarization multiplexing with high fidelity and minimal crosstalk, maintaining strong channel isolation. Our results establish a scalable route toward continuous-domain photonic encoding, offering a powerful foundation for ultracompact optical communication, vectorial information encryption, and high-dimensional light-field processing. Achromatic lens ; Broadband ; Polarization (electrochemistry) ; Multiplexing ; Birefringence ; Photonics ; Diffraction ; Formalism (music) ; High fidelity Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Orbital Angular Momentum in Optics
Authors: Zonglin Yang | Lidong Bing
Source: arXiv (Cornell University) (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning process, $P(\text{hypothesis}|\text{background})$ ($P(h|b)$), unexplored. We demonstrate that directly training $P(h|b)$ is mathematically intractable due to the combinatorial complexity ($O(N^k)$) inherent in retrieving and composing inspirations from a vast knowledge base. To break this barrier, we introduce MOOSE-Star, a unified framework enabling tractable training and scalable inference. In the best case, MOOSE-Star reduces complexity from exponential to logarithmic ($O(\log N)$) by (1) training on decomposed subtasks derived from the probabilistic equation of discovery, (2) employing motivation-guided hierarchical search to enable logarithmic retrieval and prune irrelevant subspaces, and (3) utilizing bounded composition for robustness against retrieval noise. To facilitate this, we release TOMATO-Star, a dataset of 108,717 decomposed papers (38,400 GPU hours) for training. Furthermore, we show that while brute-force sampling hits a ''complexity wall,'' MOOSE-Star exhibits continuous test-time scaling. Scalability ; Inference ; Robustness (evolution) ; Probabilistic logic ; Bounded function ; Logarithm ; Generative grammar ; Computational complexity theory Machine Learning in Materials Science ; Topic Modeling ; Text Readability and Simplification
Authors: Tim Schopf | Michael Färber
Source: ArXiv.org (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existing knowledge rather than reiterate minor variations. However, given the exponential growth of scientific literature, manually judging the novelty of research ideas through literature reviews is labor-intensive, subjective, and infeasible at scale. Therefore, recent efforts have proposed automated approaches for research idea novelty judgment. Yet, evaluation of these approaches remains largely inconsistent and is typically based on non-standardized human evaluations, hindering large-scale, comparable evaluations. To address this, we introduce RINoBench, the first comprehensive benchmark for large-scale evaluation of research idea novelty judgments. It comprises 1,381 research ideas derived from and judged by human experts as well as nine automated evaluation metrics designed to assess both rubric-based novelty scores and textual justifications of novelty judgments. Using this benchmark, we evaluate several state-of-the-art large language models (LLMs) on their ability to judge the novelty of research ideas. Our findings reveal that while LLM-generated reasoning closely mirrors human rationales, this alignment does not reliably translate into accurate novelty judgments, which diverge significantly from human gold standard judgments - even among leading reasoning-capable models. Data and code available at: https://github.com/TimSchopf/RINoBench. Novelty ; Benchmark (surveying) ; Novelty detection ; Identification (biology) ; Code (set theory) ; Outcome (game theory) Machine Learning in Materials Science ; Topic Modeling ; Advanced Text Analysis Techniques
Authors: Ling Yue | Kushal Raj Bhandari | Ching-Yun Ko | Dhaval Patel | Shuxin Lin | Nianjun Zhou | Jianxi Gao | Pin-Yu Chen | Shaowu Pan
Source: ArXiv.org (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows, which we treat as agentic computation graphs (ACGs). We organize the literature based on when workflow structure is determined, where structure refers to which components or agents are present, how they depend on each other, and how information flows between them. This lens distinguishes static methods, which fix a reusable workflow scaffold before deployment, from dynamic methods, which select, generate, or revise the workflow for a particular run before or during execution. We further organize prior work along three dimensions: when structure is determined, what part of the workflow is optimized, and which evaluation signals guide optimization (e.g., task metrics, verifier signals, preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating reusable design choices from the structures actually deployed in a given run and from realized runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. Our goal is to provide a clear vocabulary, a unified framework for positioning new methods, a more comparable view of existing body of literature, and a more reproducible evaluation standard for future work in workflow optimizations for LLM agents. Workflow ; Executable ; Workflow technology ; Task (project management) ; Workflow engine ; Workflow management system ; Windows Workflow Foundation ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Tianshu Zhang | Huan Sun
Source: ArXiv.org (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

SciNav: A General Agent Framework for Scientific Coding Tasks Autonomous science agents built on large language models (LLMs) are increasingly used to generate hypotheses, design experiments, and produce reports. However, prior work mainly targets open-ended scientific problems with subjective outputs that are difficult to evaluate. Scientific coding benchmarks, by contrast, provide executable outputs for objective assessment. Existing approaches remain engineering-driven pipelines, revealing the need for structured, end-to-end science agent frameworks for scientific coding tasks. We address this gap by focusing on scientific coding tasks, where evaluation can be made rigorously, and introducing an agent framework SciNav (Scientific Navigator) that enables more effective solution exploration. Our framework is designed to operate under constrained search budgets, moving beyond reliance on pre-defined success metrics and prolonged search cycles. Inspired by findings that comparative judgments often reveal finer-grained quality differences and therefore provide greater discriminative power than absolute scoring, our framework leverages pairwise relative judgments within a tree search process to select top-K promising solution branches, prune low-potential ones, and progressively narrow down the solution candidates on the selected branches guided by relative comparisons. We demonstrate our agent's effectiveness across different types of tasks on two benchmarks. Experiments show that SciNav significantly outperforms direct prompting and prior agents like OpenHands and Self-Debug across different base models, task types, and difficulty levels, and exceeds different frontier comparators such as random selection and LLM absolute scoring. These results confirm the strength of our agent design and highlight the effectiveness of relative judgment-guided top-K search for high-quality scientific coding, marking a step toward more practical science agents. Pairwise comparison ; Coding (social sciences) ; Discriminative model ; Executable ; Scientific discovery ; Process (computing) ; Task (project management) ; Selection (genetic algorithm) Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Tomas Gregan | Juraj Gregáň
Source: AI (article, 2026-03-02)
Field: Chemistry — Inorganic Chemistry

Artificial Intelligence at the Intersection of Chemistry and Materials Science Research on metal-organic frameworks (MOFs) bridges the fields of chemistry and materials science. MOFs consist of metal ions linked together by long organic molecules. These materials are known for their high porosity and large surface area, with numerous applications ranging from storage of various gases to medical uses. Recent developments show that artificial intelligence (AI) is revolutionizing the discovery and design of MOFs. Despite these advancements in AI-driven approaches in MOFs, many challenges remain in processes such as data quality assurance and experimental validation. In this perspective, we highlight recent progress in MOFs and discuss the role of AI in this truly interdisciplinary field. Intersection (aeronautics) ; Applications of artificial intelligence ; Quality (philosophy) ; Quality assurance ; Field (mathematics) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Xinyu Li | Zhen Zhang | Qi Chen | Anton van den Hengel | Lina Yao | Javen Qinfeng Shi
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding Existing chemical understanding tasks primarily rely on static molecular representations, limiting their ability to model inherently dynamic phenomena such as bond breaking or conformational changes, which are essential for a chemist to understand chemical reactions. To address this gap, we introduce Chemical Dynamics Understanding (ChemDU), a new task that translates 4D molecular trajectories into interpretable natural-language explanations. ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions, and requires models to reason about key events along molecular trajectories, such as bond formation and dissociation, and to generate coherent, mechanistically grounded narratives. To benchmark this capability, we construct Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations across these settings. We further propose Chem4DLLM, a unified model that integrates an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics. We hope that ChemDU, together with Chem4DBench and Chem4DLLM, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning. Construct (python library) ; Key (lock) ; Limiting ; Task (project management) ; Vulnerability (computing) ; Molecular dynamics ; Benchmark (surveying) ; Bond graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods