jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shoubhanik Nath | Shauryaraj Jilkar | Aditya Udgaonkar | Yu Lu | Ali Mesbah
Source: Advanced Intelligent Discovery (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design The continued miniaturization of field‐effect transistors (FETs) has reached a stage where geometric scaling alone can no longer deliver the necessary performance, power, and area improvements of nanoelectronic circuits. This article presents a forward‐looking, AI‐guided framework for the co‐optimization of advanced FETs, integrating data‐driven materials discovery, physics‐informed device modeling, and multiscale fabrication design. We conceptually decompose FET development into three tightly coupled domains—material design, device design, and fabrication design—and discuss how emerging machine learning, generative modeling, and physics‐informed surrogate models could enable more agile, data‐driven exploration and design within each. We further discuss the potential of hierarchical reinforcement learning to orchestrate cross‐level decision‐making and efficiently navigate the high‐dimensional, nonconvex design landscape inherent to advanced transistor technologies. The proposed framework illustrates a possible pathway toward intelligent, autonomous, and sustainability‐conscious semiconductor design. By articulating this vision, we aim to stimulate discussion and inspire future research toward bridging materials, devices, and manufacturing in the subnanometer regime. Bridging (networking) ; Fabrication ; Miniaturization ; Transistor ; Generative grammar ; Scaling Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Ferroelectric and Negative Capacitance Devices
Authors: A Manoj Prabaharan
Source: Preprints.org (preprint, 2026-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Driven Raman Spectroscopy and Quantum Dot Probes with Federated Learning for Micro-Nanoplastic Detection and Removal in Livestock and Aquaculture Feeds Micro and nanoplastics, pervasive environmental pollutants smaller than 5 mm and 1 µm respectively, infiltrate livestock and aquaculture feeds via contaminated water, sewage sludge fertilizers, and atmospheric deposition, compromising animal health, reproductive performance, and food chain safety. This paper presents a pioneering hybrid framework that synergistically integrates artificial intelligence-enhanced Raman spectroscopy for high-resolution polymer fingerprinting, quantum dot nanoprobes for targeted fluorescent labelling of hydrophobic plastics, and federated learning algorithms for decentralized, privacy-preserving model training across heterogeneous farm networks. Unlike traditional methods such as microscopy or pyrolysis-gas chromatography, which suffer from low sensitivity in complex organic matrices and lack real-time scalability, our system achieves a limit of detection of 5 ng/g with 97% accuracy across polyethylene, polypropylene, and polystyrene variants in poultry pellets, cattle silage, and salmon feeds. Quantum dots, functionalized with π-π stacking ligands, enable selective binding and surface-enhanced Raman signals, while edge-deployed AI processes hyperspectral data in under 100 ms per sample. Federated averaging across 50 simulated nodes converges 25% faster than centralized baselines, incorporating differential privacy for regulatory compliance. Experimental results demonstrate 91% removal efficiency through dielectrophoretic extraction of labelled particles, surpassing density separation by 40% in yield and 70% in speed, with pilot deployments yielding 12% improvements in feed conversion ratios and 30% reductions in inflammation biomarkers. This scalable, cost-effective solution ($500/unit, 6-month ROI) paves the way for sustainable animal production resilient to escalating plastic pollution, with broader implications for precision agriculture and global food security. Quantum dot ; Stacking ; Hyperspectral imaging ; Raman spectroscopy ; Livestock ; Sensitivity (control systems) ; Aquaculture ; Nanosensor ; Animal food Gold and Silver Nanoparticles Synthesis and Applications ; Microplastics and Plastic Pollution ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wei Ling Chua | Nurul Afiqah
Source: ChemRxiv (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey The rapid proliferation of Large Language Models (LLMs) has heralded a new era in artificial intelligence, demonstrating remarkable capabilities in understanding, generating, and reasoning with human language. Their potential to revolutionize scientific discovery, particularly in chemistry, is immense. However, standalone LLMs are inherently limited by their reliance on static pre-training data, leading to issues such as factual hallucination, outdated knowledge, and a lack of transparency in their reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to mitigate these limitations by grounding LLM responses in external, up-to-date, and verifiable knowledge sources. This survey provides a comprehensive overview of the intersection of RAG and LLMs within the chemical sciences. We delve into the foundational concepts of LLMs and RAG, detail the unique architectures and methodologies required for handling diverse chemical data, and systematically review their applications across drug discovery, materials science, reaction prediction, and chemical literature mining. Furthermore, we critically examine the existing challenges, limitations, and ethical considerations inherent in deploying RAG-LLMs in chemistry. Finally, we discuss promising future directions, emphasizing the need for robust evaluation benchmarks and advanced multimodal RAG systems to unlock the full potential of these transformative technologies in accelerating chemical innovation. Transparency (behavior) ; Transformative learning ; Intersection (aeronautics) ; Verifiable secret sharing ; Automated reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jianke Yang | Ohm Rishabh Venkatachalam | Mohammad Kianezhad | Sharvaree Vadgama | Rose Yu
Source: arXiv (Cornell University) (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Think like a Scientist: Physics-guided LLM Agent for Equation Discovery Explaining observed phenomena through symbolic, interpretable formulas is a fundamental goal of science. Recently, large language models (LLMs) have emerged as promising tools for symbolic equation discovery, owing to their broad domain knowledge and strong reasoning capabilities. However, most existing LLM-based systems try to guess equations directly from data, without modeling the multi-step reasoning process that scientists often follow: first inferring physical properties such as symmetries, then using these as priors to restrict the space of candidate equations. We introduce KeplerAgent, an agentic framework that explicitly follows this scientific reasoning process. The agent coordinates physics-based tools to extract intermediate structure and uses these results to configure symbolic regression engines such as PySINDy and PySR, including their function libraries and structural constraints. Across a suite of physical equation benchmarks, KeplerAgent achieves substantially higher symbolic accuracy and greater robustness to noisy data than both LLM and traditional baselines. Robustness (evolution) ; Suite ; Domain (mathematical analysis) ; Prior probability ; Process (computing) ; Domain knowledge ; Function (biology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Haowei Hua | Jingwen Yang | Wanyu Lin | Pan Zhou
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining O(3) group tensor equivariance. Such equivariance constraint often requires specialized architecture designs to achieve effective predictions, inevitably introducing tremendous computational costs. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry. In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and be leveraged to ensure equivariant tensor property prediction. Building upon this insight, we propose a general O(3)-equivariant framework for efficient crystal tensor property prediction, referred to as GoeCTP. By utilizing canonicalization, GoeCTP achieves high efficiency without requiring the explicit incorporation of equivariance constraints into the network architecture. Experimental results indicate that GoeCTP achieves the best prediction performance and runs at most 13 times faster compared to existing state-of-the-art methods in benchmarking datasets, underscoring its effectiveness and efficiency. Tensor (intrinsic definition) ; Property (philosophy) ; Simple (philosophy) ; Constraint (computer-aided design) ; Tensor calculus ; Equivariant map ; Tensor algebra ; Algebra over a field Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Ran Li | Shimin Di | Haowei LI | Luanshi Bu | Jiachuan Wang | Wangze Ni | Lei Chen
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling. Some methods coupled with evaluation techniques that bypass fundamental challenges in reaction representation and fail to capture deep chemical intuition like reaction common sense and {topological atom mapping logic}. We argue that the core challenge lies in instilling these knowledge into the models. To this end, we propose a unified framework that prioritizes chemical understanding over scale through three key innovations: (1) a {Latent Chemical Consistency} objective that models reactions as movements on a continuous chemical manifold, ensuring reversible and physically plausible transformations; (2) a {Hierarchical Cognitive Curriculum} that trains the model through progressive stages, from syntax mastery to semantic reasoning, building robust chemical intuition; (3) {Atom-Map Permutation Invariance (AMPI)}, which force the model to learn invariant relational topology and balance multi-task learning. (4)and structured plan-based reasoning to improve the performance of the LLMs. Our compact {0.5B-parameter model}, \textbf{RxnNano} significantly outperforms fine-tuned LLMs ten times larger (>7B) and all the domain baselines, achieving a 23.5\% Top-1 accuracy improvement on rigorous benchmarks without test-time augmentation. https://github.com/rlisml/RxnNano. Intuition ; Representation (politics) ; Retrosynthetic analysis ; Key (lock) ; Knowledge representation and reasoning ; Domain knowledge ; Bridging (networking) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Olympia Dartsi | Lauren Walters | Amalie Trewartha | Steven B Torrisi | Amanda A. Volk | Liam Joyce | Gerbrand Ceder | Anubhav Jain
Source: ChemRxiv (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Automating Chemical Reasoning in High-Throughput Phase Identification with a Probabilistic, LLM-Guided Framework Autonomous laboratories increasingly enable materials synthesis at scale, but traditional high-throughput characterization workflows remain limited by the need for expert chemical intuition to distinguish plausible interpretations from formally good but chemically incorrect fits. We present an automated interpretation framework that combines probabilistic inference with automated chemical reasoning for phase identification from powder X-ray diffraction (XRD). The framework evaluates multiple candidate interpretations using diffraction pattern-based metrics. It then refines these likelihoods using chemically-informed priors derived from composition balance and a large language model (LLM)-based plausibility estimate with human-readable justification and also produces a trustworthiness score. In a blinded multi-project benchmark, the framework's top-ranked interpretation was selected over the lowest-R wp baseline 93% of the time in cases where one prediction was preferred. Trust decisions made by the framework aligned with expert judgment in approximately 75-80% of cases. In a second evaluation, the framework systematically identified cases where lowest-R wp interpretations were chemically implausible and surfaced credible alternatives to historically ambiguous samples. By reframing phase identification as a problem of probabilistic reasoning and trust-aware decision making, this work demonstrates how chemical intuition can be automated and scaled. Workflow ; Probabilistic logic ; Intuition ; Cognitive reframing ; Identification (biology) ; Inference ; Prior probability ; Trustworthiness ; Qualitative reasoning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Weibo Qiu | Yinhao Xiao | Runyu Pan
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

HWE-Bench: Can Language Models Perform Board-level Schematic Designs? Large Language Models (LLMs) have demonstrated significant potential in various engineering tasks, including software development, digital logic generation, and companion document maintenance. However, their ability to perform board-level circuit design is understudied, as this task requires a synergized understanding of real-world physics and Integrated Circuit (IC) datasheets, the latter comprising detailed specifications for individual components. To address this challenge, we propose \hweb, an evaluation framework that benchmarks the ability of LLMs to perform such designs. It consists of 300 board-level design tasks pulled from open-source and crowdsourcing platforms such as GitHub and OSHWLab, covering 8 application domains, and is complemented with a knowledge base of 2,914 real IC datasheets. For each task, the LLMs are tasked with generating a schematic from scratch, using the provided circuit functional requirements and a set of component datasheets as input. The resulting schematic will be checked against a static electrical rules, and then passed to a circuit simulator to verify its dynamic behavior. Our evaluation show that although current models achieve initial engineering usability and documentation understanding, they lack physical intuition, as the top-performing model achieved an overall pass rate of 8.15\%. We envision that advancements on \hweb\ will pave the way for the development of practical Electronic Design Automation (EDA) agents, revolutionizing the field of board-level design. Schematic ; Component (thermodynamics) ; Task (project management) ; Set (abstract data type) ; Documentation ; Field (mathematics) ; Logical data model ; Usability ; Software Machine Learning in Materials Science ; Physical Unclonable Functions (PUFs) and Hardware Security ; Big Data and Digital Economy
Authors: Yutang Ge | Yaning Cui | Hanzheng Li | Jun-Jie Wang | Fanjie Xu | Jinhan Dong | Yongqi Jin | Dongxu Cui | Peng Jin | Guojiang Zhao | Hengxing Cai | Rong Zhu | Linfeng Zhang | Xiaohong Ji | Zhifeng Gao
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

SpecXMaster Technical Report Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence. However, conventional expert-dependent spectral interpretation encounters substantial hurdles, including susceptibility to human bias and error, dependence on limited specialized expertise, and variability across interpreters. To address these challenges, we propose SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation. SpecXMaster enables automated extraction of multiplicity information from both 1H and 13C spectra directly from raw FID (free induction decay) data. This end-to-end pipeline enables fully automated interpretation of NMR spectra into chemical structures. It demonstrates superior performance across multiple public NMR interpretation benchmarks and has been refined through iterative evaluations by professional chemical spectroscopists. We believe that SpecXMaster, as a novel methodological paradigm for spectral interpretation, will have a profound impact on the organic chemistry community. Pipeline (software) ; Raw data ; Interpretation (philosophy) ; Bridge (graph theory) ; Nuclear magnetic resonance spectroscopy ; Replicate Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Toshiyuki Koyama | Yusuke Matsuoka | Akimitsu Ishii
Source: Institutional Repositories DataBase (IRDB) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Simple implementation examples of agent AI on free energy calculation and phase-field simulation With the development of an environment that equips large language models with tools, chat-type artificial intelligence (AI) is currently shifting to agent-type AI. Although building an agent AI with complex behavior is an important issue, a method for easily implementing simple agent AI is also useful in materials research. The objectives of this study are to demonstrate the usefulness of simple agent AI codes, and to distribute them as supplemental materials. The key points are summarized as follows. (1) Using Gibbs energy calculations as an example, we demonstrated a simple method for constructing agent AI, including explanation and distribution of the python code. (2) We applied this method to a scratch code development of phase-field simulation, and the template python code was also distributed. (3) Using the simple agent AI technique, we were able to easily verify that the Jarzynski equality is applicable for the diffusion behavior in diffusion couple, which provides a new approach for directly evaluating free energy change from diffusion flux information. Simple (philosophy) ; Energy (signal processing) ; Energy consumption ; Efficient energy use Solidification and crystal growth phenomena ; Lattice Boltzmann Simulation Studies ; Neural Networks and Reservoir Computing
Authors: Shi Qiu | JunYi Deng | Yiwei Deng | Haoran Dong | Jieyu Fu | Mao Li | Zeyu Li | Zhaolong Zhang | Huiwen Zheng | Leidong Bao | Anqi Lv | Zihan Mo | Yadi Niu | Yiyang Peng | Yu Tian | Yili Wang | Ziyu Wang | Zi-Yu Wang | Jiashen Wei | Liuheng Wu | Aoran Xue | Leyi Yang | Guanglu Yuan | Xiarui Zhan | Jingjun Zhang | Zifan Zheng | Pengfei Liu | Linrui Zhen | K. J. Li | Qichang Li | Ziheng Zhou | Guo-En Nian | Yunwei Xiao | Qing-Hong Cao | Linjie Dai | Xu Feng | Peng Gao | Ying Gu | Chang Liu | Jia Liu | Ming-xing Luo | Yan-Qing Ma | Liang-You Peng | Huichao Song | Shufeng Wang | Chenxu Wang | Tao Wang | Yi-Nan Wang | Chengyin Wu | Pengwei Zhao | Hua Xing Zhu
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

PRBench: End-to-end Paper Reproduction in Physics Research AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics. Each task requires an agent to comprehend the methodology of a published paper, implement the corresponding algorithms from scratch, and produce quantitative results matching the original publication. Agents are provided only with the task instruction and paper content, and operate in a sandboxed execution environment. All tasks are contributed by domain experts from over 20 research groups at the School of Physics, Peking University, each grounded in a real published paper and validated through end-to-end reproduction with verified ground-truth results and detailed scoring rubrics. Using an agentified assessment pipeline, we evaluate a set of coding agents on PRBench and analyze their capabilities across key dimensions of scientific reasoning and execution. The best-performing agent, OpenAI Codex powered by GPT-5.3-Codex, achieves a mean overall score of 34%. All agents exhibit a zero end-to-end callback success rate, with particularly poor performance in data accuracy and code correctness. We further identify systematic failure modes, including errors in formula implementation, inability to debug numerical simulations, and fabrication of output data. Overall, PRBench provides a rigorous benchmark for evaluating progress toward autonomous scientific research. Benchmark (surveying) ; Task (project management) ; Debugging ; Code (set theory) ; Set (abstract data type) ; Domain (mathematical analysis) ; Toolbox ; Coding (social sciences) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Quanjia Xiao | Wen Ouyang | Zonglin Yang | Tianhao Wu | Qingguo Zhou | Runze Mao | Zhi X. Chen
Source: ArXiv.org (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for complex physical systems such as combustion science, general-purpose LLMs often generate severe hallucinations due to insufficient domain knowledge and the inability to adhere to physical conservation laws. To address this issue, we propose the first full-stack domain-enhanced LLM workflow tailored for the field of combustion science, which integrates automated domain corpus construction, incremental pre-training, instruction fine-tuning, and verifiable reward-based reinforcement learning. This workflow ensures that the model truly internalizes physical laws rather than merely learning textual statistical patterns. We also release FlameBench, a standardized evaluation benchmark specifically designed for complex reasoning tasks in combustion science. Experimental results demonstrate that the model developed in this work significantly outperforms state-of-the-art general-purpose closed-source models and traditional retrieval-augmented generation methods on combustion science reasoning tasks. This work lays a solid technical and resource foundation for the subsequent development of domain-specific scientific research agents with reliable scientific reasoning capabilities. Workflow ; Domain (mathematical analysis) ; Field (mathematics) ; Task (project management) ; Benchmark (surveying) ; Verifiable secret sharing ; Combustion ; Work (physics) ; Resource (disambiguation) Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques