jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: InternAgent Team | Bo Zhang | Shiyang Feng | Xiangchao Yan | Jiakang Yuan | Runmin Ma | Yusong Hu | Yu Zhang | Xueqiu He | Songtao Huang | Shaowei Hou | Zheng Nie | Zhilong Wang | Jinyao Liu | Tzu‐Rong Peng | Peng Ye | D.C. Zhou | Shufei Zhang | Xiaosong Wang | Yilan Zhang | Meng Li | Zhongying Tu | Xiangyu Yue | Wen Ouyang | B. Zhou | Lei Bai
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification Artificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce InternAgent, a unified closed-loop multi-agent framework to conduct Autonomous Scientific Research (ASR) across various scientific research fields, enabling researchers to tackle complicated problems in these fields with unprecedented speed and precision. InternAgent highlights three key advantages: 1) Scalability: InternAgent has demonstrated its versatility across 12 scientific research tasks, capable of generating innovative ideas to enhance the performance of baseline code. 2) Interactivity: InternAgent provides an interface for human expert feedback and multi-agent interaction in automated end-to-end processes, allowing for the seamless integration of domain expert knowledge. 3) Efficiency: InternAgent has achieved promising performance gains in several scientific fields with significantly less time cost compared to human efforts. For instance, in reaction yield prediction, it increased from 27.6% to 35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from 0.65 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation, precision advanced from 78.8% to 81.0% in a mere 30 hours. Key (lock) ; Baseline (sea) ; Interface (matter) ; Domain (mathematical analysis) ; Field (mathematics) ; Expert system ; Transformation (genetics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Yi Duan | Lifeng Yan
Source: Chinese Journal of Chemical Physics (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diol-based deep eutectic solvent for cellulose hydrogels from corncob as solid electrolytes for zinc-ion hybrid supercapacitors Green solvent pretreatment of biomass represents a promising approach for enhancing the economic value of lignocellulosic derivatives. In this study, corncob biomass was treated with a diolbased deep eutectic solvent (DES) under mild conditions, facilitating efficient cellulose separation. The extracted cellulose was subsequently used to fabricate cellulose hydrogels in an aqueous zinc chloride solution. The resulting hydrogel exhibited a “water-in-salt” effect due to the high concentration of ZnCl2. Leveraging the antifreeze properties of sorbitol, the system demonstrated outstanding low-temperature electrochemical performance, including a broad operating voltage window and an ionic conductivity of 38.4 mS·cm−1 at —20 °C. At 20 °C, the device achieved an energy density of 206 Wh·kg−1 and a power density of 2701.05 W·kg−1 at a current density of 1 A·g−1. Moreover, the flexible zinc-ion hybrid supercapacitor (ZHSC) maintained 89% of its capacitance and nearly 100% Coulombic efficiency after 5500 cycles at 20 °C. This work not only advances the development of zinc-ion energy storage devices but also establishes a new paradigm for the green and direct utilization of biomass-derived materials. Deep eutectic solvent ; Supercapacitor ; Cellulose ; Self-healing hydrogels ; Lignocellulosic biomass ; Eutectic system ; Electrolyte ; Aqueous solution ; Faraday efficiency Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Membrane-based Ion Separation Techniques
Authors: Fengyu Li | Yuwei Zhang | Fei Xia | Xin Xu
Source: ChemRxiv (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Development of Shell-Space Electrostatic Potential Fitting Charges in a United-Atom Model for Amino Acids Simulations In the development of coarse-grained (CG) models, accurately capturing the electrostatic interactions between CG particles is crucial for enhancing the accuracy of these models. In this study, we developed a shell-space electrostatic potential (SS-ESP) method to derive the partial charges for CG particles by fitting the ESP of all-atom (AA) models. To validate the accuracy of the SS-ESP method, we systematically tested and compared the ESPs and electric dipole moments of CG charges obtained using four different methods, with AA models of amino acids serving as the reference. Based on the SS-ESP charges, we proposed a new Side-chain Reduced CG (SRCG) model for amino acids. The parameterization of the new SRCG force field follows a bottom-up strategy, where the bonding interaction parameters are inherited from the AA model, the CG charges are derived using the SS-ESP method, and the Lennard-Jones (LJ) parameters are fitted to reproduce the static potential for van der Waals (vdW) interactions. Molecular dynamics simulations of amino acids show that the test results for dipeptides and tripeptides based on this SRCG model are consistent with those obtained from the corresponding AA models. Furthermore, the SRCG force field can accurately describe the solvation free energy of amino acids and preserve the stability of protein secondary structures. Hence, the present SS-ESP charge fitting method provides a new framework for accurately describing the electrostatic interactions of CG particles for bottom-up development of protein CG models. van der Waals force ; Electrostatics ; Amino acid ; Force field (fiction) ; Solvation ; Molecular dynamics ; Dipole ; Static electricity ; Electric field ; Partial charge Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Filipp Gusev | Benjamin C Kline | R. P. Quinn | Anqin Xu | Ben J. Smith | Brian Frezza | Olexandr Isayev
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory Autonomous experiments are vulnerable to unforeseen adverse events. We developed a transferable ML framework that flags affected HPLC runs in real time and provides expert-level quality control without human oversight. Anomaly detection ; Pattern recognition (psychology) ; Quality (philosophy) ; Cloud computing ; Control (management) ; Support vector machine Machine Learning in Materials Science ; Machine Learning and Data Classification ; Anomaly Detection Techniques and Applications
Authors: Peter L. M. Jansen | Siti Lailatul Mohd Hassan | Ruoyao Wang
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science Benchmark (surveying) ; Key (lock) ; Context (archaeology) ; Feature (linguistics) Machine Learning in Materials Science ; Academic Writing and Publishing ; scientometrics and bibliometrics research
Authors: Yingkai Sun | Feiyang Xu | Huadong Liang | Xianghui Fan | Guozhu Wan | Wenwan Zhong | Jun Jiang | Xin Li | Linjiang Chen
Source: AI for Science (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning, Sun, Yingkai, Xu, Feiyang, Liang, Huadong, Fan, Xianghui, Wan, Guozhu, Zhong, Wenwan, Jiang, Jun, Li, Xin, Chen, Linjiang Ontology ; Knowledge-based systems ; Knowledge representation and reasoning ; Automated reasoning ; Knowledge acquisition ; Process ontology ; Model-based reasoning ; Expert system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zaifei Yang | Hong Chang | Ruibing Hou | Shiguang Shan | Xilin Chen
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge The molecular large language models have garnered widespread attention due to their promising potential on molecular applications. However, current molecular large language models face significant limitations in understanding molecules due to inadequate textual descriptions and suboptimal molecular representation strategies during pretraining. To address these challenges, we introduce KnowMol-100K, a large-scale dataset with 100K fine-grained molecular annotations across multiple levels, bridging the gap between molecules and textual descriptions. Additionally, we propose chemically-informative molecular representation, effectively addressing limitations in existing molecular representation strategies. Building upon these innovations, we develop KnowMol, a state-of-the-art multi-modal molecular large language model. Extensive experiments demonstrate that KnowMol achieves superior performance across molecular understanding and generation tasks. GitHub: https://github.com/yzf-code/KnowMol Huggingface: https://hf.co/datasets/yzf1102/KnowMol-100K Bridging (networking) ; Representation (politics) ; Face (sociological concept) ; Language model ; Molecular model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Michael Grosskopf | Russell Bent | R. Somasundaram | Isaac Michaud | Arthur Lui | Nathan DeBardeleben | Earl Lawrence | Graham, Warren D. | Wimmer, Golo A | Shivakumar, Sachin | Gallart, Joan Vendrell | Nagarajan, Harsha
Source: arXiv (Cornell University) (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

URSA: The Universal Research and Scientific Agent Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in \quotes{agentic} AI has the potential to revolutionize modern science and remove bottlenecks to progress. In this work, we present URSA, a scientific agent ecosystem for accelerating research tasks. URSA consists of a set of modular agents and tools, including coupling to advanced physics simulation codes, that can be combined to address scientific problems of varied complexity and impact. This work highlights the architecture of URSA, as well as examples that highlight the potential of the system. Modular design ; Set (abstract data type) ; Simple (philosophy) ; Work (physics) ; Scientific modelling ; Scientific discovery Machine Learning in Materials Science ; Language and cultural evolution ; Topic Modeling
Authors: Chaoqiang Feng | Bin Jiang
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

Machine learning accelerated finite-field simulations for electrochemical interfaces Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl(aq) system, this approach not only dramatically accelerates fully first-principles finite-field simulations but also successfully extrapolates to cell potentials beyond the training range while accurately predicting key electrochemical properties. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl$^-$ ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modelling of large-scale electrochemical interfaces under potential control. Electrochemistry ; Artificial neural network ; Orientation (vector space) ; Anode ; Electrode ; Range (aeronautics) ; Molecule ; Interface (matter) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: V. Fedorov | D. Lavitskaya | D. N. Ibragimov | D. Safronov | A. Balles | A. Gribanova | M. Radionov
Source: Doklady Mathematics (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Adapting the AI Scientist for Enterprise: Solving Real Business Problems with Autonomous Text-To-SQL Research Abstract One of the grand challenges in artificial intelligence is automating complex, multi-step reasoning tasks that require deep understanding of both natural language and structured data. While large language models have shown promise in code generation and natural language understanding, their ability to autonomously conduct end-to-end research in specialized domains remains limited. We ask a key question: can AI-driven research reliably solve real enterprise problems? This paper presents a fully automated framework for AI-driven research applied to the text-to-SQL problem, enabling frontier language models to independently generate novel ideas, design experiments, implement solutions, and communicate findings. We adapt the AI Scientist [1] – a comprehensive AI agent for autonomous scientific discovery – to the domain of semantic parsing, where it formulates hypotheses about improving text-to-SQL accuracy, writes executable code, runs experiments on benchmark datasets, visualizes performance gains, and produces complete scientific papers summarizing its results. Each full research cycle costs less than $5, making it a scalable and cost-effective approach for rapid innovation. This work moves toward self-improving AI systems in natural language processing, demonstrating robust performance across both proprietary and public benchmarks. Where AI agents not only solve tasks but also advance the state of the art by conducting independent research. Our code and generated papers are open-sourced at https://gitverse.ru/tr1ggers/AIScientist_Text2SQL.git . Executable ; Natural language understanding ; Scalability ; Natural language ; Autonomous agent ; Domain (mathematical analysis) ; Code (set theory) ; Benchmark (surveying) ; Semantics (computer science) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Natural Language Processing Techniques
Authors: Sirui Lu | Zhijing Jin | Terry Jingchen Zhang | Pavel Kos | J. I. Cirac | Bernhard Schölkopf
Source: ArXiv.org (preprint, 2025-06-06)
Field: Materials Science — Materials Chemistry

Can Theoretical Physics Research Benefit from Language Agents? Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains inadequate. While current models show competence in mathematical reasoning and code generation, we identify critical gaps in physical intuition, constraint satisfaction, and reliable reasoning that cannot be addressed through prompting alone. Physics demands approximation judgment, symmetry exploitation, and physical grounding that require AI agents specifically trained on physics reasoning patterns and equipped with physics-aware verification tools. We argue that LLM would require such domain-specialized training and tooling to be useful in real-world for physics research. We envision physics-specialized AI agents that seamlessly handle multimodal data, propose physically consistent hypotheses, and autonomously verify theoretical results. Realizing this vision requires developing physics-specific training datasets, reward signals that capture physical reasoning quality, and verification frameworks encoding fundamental principles. We call for collaborative efforts between physics and AI communities to build the specialized infrastructure necessary for AI-driven scientific discovery. Constraint (computer-aided design) ; Domain (mathematical analysis) ; Position paper ; Code (set theory) ; Position (finance) Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Minju Seo | Jinheon Baek | Seongyun Lee | Sung Ju Hwang
Source: ArXiv.org (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work. In the meantime, recent Large Language Models (LLMs) excel at understanding scientific documents and generating high-quality code. Inspired by this, we introduce PaperCoder, a multi-agent LLM framework that transforms machine learning papers into operational code repositories. PaperCoder operates in three stages: planning, where it constructs a high-level roadmap, designs the system architecture with diagrams, identifies file dependencies, and generates configuration files; analysis, which focuses on interpreting implementation-specific details; and generation, where modular, dependency-aware code is produced. Moreover, each phase is instantiated through a set of specialized agents designed to collaborate effectively across the pipeline. We then evaluate PaperCoder on generating code implementations from machine learning papers based on both model-based and human evaluations, particularly from the authors of those papers, with author-released repositories as ground truth if available. Our results demonstrate the effectiveness of PaperCoder in creating high-quality, faithful implementations. Furthermore, it consistently shows strengths in the recently released PaperBench benchmark, surpassing strong baselines by substantial margins. Code is available at: https://github.com/going-doer/Paper2Code. Implementation ; Code (set theory) ; Code generation ; Machine code ; Set (abstract data type) ; Source code ; Program code Machine Learning in Materials Science ; Scientific Computing and Data Management ; Software Engineering Research
Authors: Guanhai Li | Jiuxu Wang | Jie Wang | Feilong Yu | Jin Chen | Rongsheng Chen | Tie Geng | Rong Jin | Yiran Zhou | Tao Zheng | Xiaoshuang Chen | Wei Lü
Source: Research Square (preprint, 2025-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unleashing Continuous Polarization–Wavelength Control with Nonlocal Metasurfaces Polarization (electrochemistry) ; Wavelength ; Physics ; Optics ; Computer science ; Chemistry Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Yujie Luo | Zhangjing Yu | Xuehai Wang | Yuqi Zhu | Ningyu Zhang | Wei Lei | Liang Du | Da Zheng | Hua‐Jun Chen
Source: arXiv (Cornell University) (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a pluggable, paper-centric knowledge base that automatically integrates code snippets and technical insights extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code is available at https://github.com/zjunlp/xKG. Executable ; Knowledge base ; Code (set theory) ; Task (project management) ; Knowledge-based systems ; Knowledge representation and reasoning ; Open Knowledge Base Connectivity ; Source code Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Patricia Gómez‐Gutierrez | Juan J. Pérez
Source: Pharmaceuticals (review, 2025-09-15)
Field: Chemistry — Organic Chemistry

Small Molecule Protease Inhibitors as Model Peptidomimetics Proteases constitute one of the largest sub-classes of enzymes, accounting for ca. 2% of the proteins encoded in the human genome. They play a key role in protein degradation and signaling, regulating a variety of physiological processes. Dysregulation of their activity is associated with various pathological conditions like cancer, neurodegenerative disorders, inflammatory or cardiovascular diseases. Protease activity can be controlled by regulating enzyme concentrations, but also by inhibitors, molecules that modulate enzyme function, inspiring the development of small molecule protease inhibitors for therapeutic purposes. Protease inhibitors can be designed from the corresponding substrates by isostere replacement at the scissile bond. This process yields a first-generation of inhibitors that usually exhibit poor drug-like profiles that need subsequently be improved to generate a second-generation, by smoothing their peptide-like features. This process is reviewed in the present report and exemplified in the successful discovery stories of different inhibitors that correspond to four types of proteases, including the angiotensin converting enzyme (metalloprotease); HIV protease (aspartate protease); thrombin (serine protease) and the proteasome (threonine protease). A detailed description of the stories behind their design from their initial discovery to the final product is described in this report. Moreover, despite successful discovery stories, the challenges associated with designing novel protease inhibitors are examined. Finally, the relevance of these drugs in the present drug market is also reported. Protease ; Small molecule ; Proteases ; Peptidomimetic ; Drug discovery ; Enzyme ; Thrombin ; Isostere Click Chemistry and Applications ; Computational Drug Discovery Methods ; Protein Degradation and Inhibitors