jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuanqi Du | Botao Yu | Tianyu Liu | Tony S. Shen | Junwu Chen | Jan G. Rittig | Kunyang Sun | Yikun Zhang | Zhangde Song | Bo Zhou | Cassandra Masschelein | Yingze Wang | Haorui Wang | Haojun Jia | Chao Zhang | Hongyu Zhao | Martin Ester | Teresa Head‐Gordon | Carla P. Gomes | Huan Sun | Chenru Duan | Philippe Schwaller | Wengong Jin | Gomes, Carla P. | Sun, Huan | Duan, Chenru | Schwaller, Philippe | Jin, Wengong
Source: arXiv (Cornell University) (preprint, 2025-12-25)
Field: Materials Science — Materials Chemistry

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science, these objectives may only be imperfect proxies. We argue that automating objective function design is a central, yet unmet need for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to address this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a wide range of design applications, including antibiotics, nanobodies, functional DNA sequences, inorganic materials, and chemical processes. Notably, our experimental validation identifies a structurally novel hit with promising potency and safety profiles for E. coli in the antibiotic design task, and three de novo PD-L1 binders in the nanobody design task. These results suggest that automating objective formulation can substantially improve the effectiveness of scientific discovery agents. Scientific discovery ; Process (computing) ; Function (biology) ; Imperfect ; Architecture ; Space (punctuation) Machine Learning in Materials Science ; Micro and Nano Robotics ; Modular Robots and Swarm Intelligence
Authors: Xu Han | Ze-Feng Gao | Peng‐Jie Guo | Zhong-Yi Lu
Source: Qeios (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

PhysAgent: A Multi-Agent Approach to the Automated Discovery of Physical Laws The discovery of physical laws has traditionally relied on human intuition, analytical reasoning, and experimental observation. However, modern physics research is increasingly constrained by challenges such as high specialization, fragmented workflows, and limited computational resources, which impede scientific progress. To address these issues, we introduce PHYSAGENT, a novel multi-agent system powered by large language models (LLMs), designed to autonomously execute end-to-end scientific workflows—from hypothesis generation and computational modeling to data analysis and discovery. PhysAgent’s innovation lies in its specialized agent collaboration: a Mentor Agent guides scientific reasoning through Socratic questioning, a Student Agent handles technical execution (e.g., code implementation, DFT calculations), and a Leader Agent dynamically optimizes task scheduling and resource allocation. Integrating domain-specific tools like first-principles simulations (e.g., Quantum ESPRESSO, VASP), PhysAgent ensures reproducibility while maintaining human-in-the-loop refinement. We demonstrate its capability to autonomously derive physical laws—such as Kepler’s laws from orbital data and Newton’s second law from force-motion experiments—without prior knowledge. Furthermore, it extends to ab initio materials modeling, automating electronic structure calculations (e.g., GaAs band gaps). In addition, PhysAgent simulates complex real-world phenomena, such as raindrop flow on train windows, highlighting its adaptability beyond traditional physics problems. By harmonizing LLM-driven planning with domain-specific tools, PhysAgent establishes a trustworthy, scalable paradigm for AI-driven physics research, highlighting the transformative potential of multi-agent intelligence in accelerating discovery across classical and quantum systems. Law ; Computer science ; Law and economics ; Political science ; Economics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Utkarsh Pratiush | Austin Houston | Paolo Longo | Remco Geurts | Sergei V. Kalinin | Gerd Duscher
Source:  (preprint, 2025-05-20)
Field: Materials Science — Materials Chemistry

stemOrchestrator: Enabling Seamless Hardware Control and High-Throughput Workflows on Electron Microscopes Scanning Transmission Electron Microscopy (STEM) is one of the most powerful tools for materials characterization, providing access to atomic-scale structure via direct imaging, chemical composition, orbital populations, vibrational properties and quasiparticles via spectral methods, and crystallographic information through diffraction. However, these diverse functionalities are often supported by hardware components from different manufacturers, creating challenges in seamless operation and integration of detectors, holders, and cameras on a single STEM column. As the field moves toward machine learning (ML) enabled experiments and autonomous discovery, the need for combined control across these hardware interfaces becomes critical. This paper develops stemOrchestrator, a software framework which combines detector- and camera specific Application Programming Interfaces (APIs) in a cohesive platform for controlling various STEM hardware modules and developing sophisticated automated workflows. We illustrate the performance of stemOrchestrator using several model workflows including high-throughput particle characterization, hardware tuning using Bayesian Optimization (BO), cross correlation-based drift correction with informative logging of hardware status. Importantly, these workflows are presented not as isolated technical advances, but to highlight how stemOrchestrator renders their implementation almost trivial by abstracting hardware heterogeneity and execution logic. This framework also enables seamless integration with LLM (Large language model) agents to suggest and run complex automated workflows and opens pathway for orchestration of self-driving labs and geographically distributed instrumentation networks. The codes are available at this link for trying and contributing: https://github.com/pycroscopy/pyAutoMic/tree/main/TEM/stemOrchestrator Workflow ; Computer science ; Automation ; Software ; Throughput ; Characterization (materials science) ; Computer hardware ; Embedded system ; Distributed computing ; Nanotechnology ; Operating system ; Materials science ; Engineering ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Scientific Computing and Data Management
Authors: Mohamed Dhouioui | Jonathan Barnoud | Rhoslyn Roebuck Williams | Harry J. Stroud | P. J. Bates | David R. Glowacki
Source: SN Computer Science (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems Molecular dynamics ; Function (biology) ; Molecular graphics ; Visualization ; Virtual screening ; Imitation ; Virtual reality Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Robot Manipulation and Learning
Authors: Xu Huang | Junwu Chen | Yuxing Fei | Zhuohan Li | Philippe Schwaller | Ceder Gerbrand
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution Large language model (LLM) agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search, code extraction, and memory utilization; self-reflection via introspection, knowledge graph exploration, and others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research. Executable ; Scalability ; Cascade ; Adaptability ; Limiting ; Benchmark (surveying) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Marko Petković | Vlado Menkovski | Sofı́a Calero
Source: ArXiv.org (preprint, 2025-09-12)
Field: Materials Science — Materials Chemistry

Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction Automated characterization of porous materials has the potential to accelerate materials discovery, but it remains limited by the complexity of simulation setup and force field selection. We propose a multi-agent framework in which LLM-based agents can autonomously understand a characterization task, plan appropriate simulations, assemble relevant force fields, execute them and interpret their results to guide subsequent steps. As a first step toward this vision, we present a multi-agent system for literature-informed force field extraction and automated RASPA simulation setup. Initial evaluations demonstrate high correctness and reproducibility, highlighting this approach's potential to enable fully autonomous, scalable materials characterization. Correctness ; Characterization (materials science) ; Scalability ; Field (mathematics) ; Force field (fiction) ; Extraction (chemistry) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Andreas Thomas | Yu‐Cheng Chen | Hubert Valencia | Sharu Theresa Jose | R. Wu
Source: ArXiv.org (preprint, 2025-09-05)
Field: Materials Science — Materials Chemistry

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Navigating the vast chemical space of molecular structures to design novel drug molecules with desired target properties remains a central challenge in drug discovery. Recent advances in generative models offer promising solutions. This work presents a novel quantum circuit Born machine (QCBM)-enabled Generative Adversarial Network (GAN), called QCA-MolGAN, for generating drug-like molecules. The QCBM serves as a learnable prior distribution, which is associatively trained to define a latent space aligning with high-level features captured by the GANs discriminator. Additionally, we integrate a novel multi-agent reinforcement learning network to guide molecular generation with desired targeted properties, optimising key metrics such as quantitative estimate of drug-likeness (QED), octanol-water partition coefficient (LogP) and synthetic accessibility (SA) scores in conjunction with one another. Experimental results demonstrate that our approach enhances the property alignment of generated molecules with the multi-agent reinforcement learning agents effectively balancing chemical properties. Reinforcement learning ; Chemical space ; Associative property ; Generative grammar ; Content-addressable memory ; Key (lock) ; Property (philosophy) ; Space (punctuation) Machine Learning in Materials Science ; Quantum-Dot Cellular Automata ; Advancements in Semiconductor Devices and Circuit Design
Authors: Waditwar, Prajkta
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Agentic AI and sustainable procurement: Rethinking anti-corrosion strategies in oil and gas Corrosion remains a persistent challenge in the oil and gas industry, causing infrastructure failures, escalating maintenance costs, and severe environmental risks. Recent innovations in anti-corrosive materials—such as nanotechnology-based coatings, corrosion-resistant alloys, and graphene composites—have been further accelerated by the rise of Artificial Intelligence (AI) in procurement. This paper introduces a next-generation framework for AI-driven procurement of anti-corrosive materials, integrating predictive analytics, generative AI, agentic AI, and blockchain-backed transparency. Generative AI enables dynamic supplier intelligence, automated sustainability reporting, and RFQ drafting, while agentic AI agents autonomously negotiate contracts, optimize sourcing cycles, and adapt procurement strategies in real time. Multimodal AI, when combined with IoT sensors and digital twins, facilitates predictive corrosion monitoring by analyzing sensor streams, visual inspection data, and supplier documents simultaneously. Furthermore, quantum AI simulations hold the potential to model corrosion resistance of emerging alloys under refinery-specific conditions, enabling more accurate material selection. Beyond cost savings and operational resilience, AI aligns procurement with sustainability goals by recommending low-VOC coatings, minimizing carbon footprints, and enabling circular economy practices. To ensure adoption, the paper emphasizes explainable AI (XAI) for procurement leaders, offering transparency into supplier rankings and material recommendations. By synthesizing advances in material science with cutting-edge AI, this study provides a strategic roadmap for resilient, sustainable, and autonomous procurement in the oil and gas sector. Procurement ; Sustainability ; Transparency (behavior) ; Negotiation ; Supply chain Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis
Authors: Marc Teunis | Marie Corradi | I. Djidrovski | T. Leuchtefeld | Donald Grove Barnes | Kang Liang | L. Ladera | B. Staumond | Mathieu Vinken | Thomas Härtung
Source: Toxicology Letters (article, 2025-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

S25-03 The use of natural language processing in toxicology Adverse Outcome Pathway ; Premise ; Construct (python library) ; Systems biology ; Human health ; Stressor ; Human studies ; Outcome (game theory) History and advancements in chemistry ; Statistical and Computational Modeling ; Effects and risks of endocrine disrupting chemicals
Authors: Hea Young Kim | Do‐Hoon Kim | Julie Eun Kim
Source: ArXiv.org (preprint, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

LitMOF: An LLM Multi-Agent for Literature-Validated Metal-Organic Frameworks Database Correction and Expansion Metal-organic framework (MOF) databases have grown rapidly through experimental deposition and large-scale literature extraction, but recent analyses show that nearly half of their entries contain substantial structural errors. These inaccuracies propagate through high-throughput screening and machine-learning workflows, limiting the reliability of data-driven MOF discovery. Correcting such errors is exceptionally difficult because true repairs require integrating crystallographic files, synthesis descriptions, and contextual evidence scattered across the literature. Here we introduce LitMOF, a large language model-driven multi-agent framework that validates crystallographic information directly from the original literature and cross-validates it with database entries to repair structural errors. Applying LitMOF to the experimental MOF database (the CSD MOF Subset), we constructed LitMOF-DB, a curated set of 186,773 computation-ready structures, including the successful repair of 8,771 invalid entries, which accounts for 65.3% of the not-computation-ready MOFs in the latest CoRE MOF database. Additionally, the system uncovered 12,646 experimentally reported MOFs absent from existing resources, substantially expanding the known experimental design space. Using direct air capture screening as a case study, we demonstrate that structural errors severely distort predicted adsorption energies and CO2/H2O selectivity, leading to systematic misranking of materials, false positives, and the omission of high-performance candidates. This work establishes a scalable pathway toward self-correcting scientific databases and a generalizable paradigm for LLM-driven curation in materials science. Scalability ; Limiting ; Set (abstract data type) ; Data curation ; Core (optical fiber) ; Reliability (semiconductor) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jiaxuan Liu | Tongxin Zhu | C. Ye | Fang Zhong | Hongjie Weng | Quansheng Wu
Source: ArXiv.org (preprint, 2025-08-09)
Field: Materials Science — Materials Chemistry

VASPilot: MCP-Facilitated Multi-Agent Intelligence for Autonomous VASP Simulations Density-functional-theory (DFT) simulations with the Vienna Ab initio Simulation Package (VASP) are indispensable in computational materials science but often require extensive manual setup, monitoring, and postprocessing. Here, we introduce VASPilot, an open-source platform that fully automates VASP workflows via a multi-agent architecture built on the CrewAI framework and a standardized Model Context Protocol (MCP). VASPilot's agent suite handles every stage of a VASP study-from retrieving crystal structures and generating input files to submitting Slurm jobs, parsing error messages, and dynamically adjusting parameters for seamless restarts. A lightweight Flask-based web interface provides intuitive task submission, real-time progress tracking, and drill-down access to execution logs, structure visualizations, and plots. We validate VASPilot on both routine and advanced benchmarks: automated band-structure and density-of-states calculations (including on-the-fly symmetry corrections), plane-wave cutoff convergence tests, lattice-constant optimizations with various van der Waals corrections, and cross-material band-gap comparisons for transition-metal dichalcogenides. In all cases, VASPilot completed the missions reliably and without manual intervention. Moreover, its modular design allows easy extension to other DFT codes simply by deploying the appropriate MCP server. By offloading technical overhead, VASPilot enables researchers to focus on scientific discovery and accelerates high-throughput computational materials research. Context (archaeology) ; Workflow ; Suite ; Modular design ; Protocol (science) ; Interface (matter) ; Focus (optics) ; Task (project management) ; Debugging Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: wang, liang
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-14)
Field: Materials Science — Materials Chemistry

OpenSciEval: An Open-Ended Framework for Evaluating Scientific Creativity in AI Agents Large Language Models (LLMs) have demonstrated performance surpassing humans in tasks across various fields such as mathematics, programming, and biology. However, there is still a lack of systematic evaluation regarding whether they possess source scientific creativity—namely, the ability to propose new concepts, establish cross-domain connections, and generate testable hypotheses. Existing evaluations primarily focus on knowledge reproduction or closed-ended reasoning, making it difficult to capture the divergent thinking and abductive reasoning upon which scientific discovery relies. To address this, we propose OpenSciEval (OSE), an open-ended, scalable evaluation framework designed to quantify the creative capabilities of AI agents in scientific exploration. Through carefully designed open-domain scientific challenges, this framework requires models to autonomously construct conceptual bridges, propose explanatory mechanisms, and generate novel insights in the absence of direct supervisory signals. We use the "Prime-Chaos Challenge" as the first OSE use case, requiring models to establish non-trivial connections between Peano arithmetic and symbolic dynamics. Empirical evaluations show that frontier models (such as Gemini and Qwen) can not only complete the task but also spontaneously introduce physics-like concepts to explain pure mathematical structures, demonstrating preliminary scientific modeling capabilities. OpenSciEval does not rely on specific disciplinary knowledge; its design principles are transferable to fields such as physics, chemistry, and life sciences, providing a universal, reproducible quantitative scale for measuring the evolution of AI from "solvers" to "researchers". Construct (python library) ; Task (project management) ; Scientific modelling ; Abductive reasoning ; Exploit ; Scalability ; Creativity Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Multimodal Machine Learning Applications
Authors: Aritra Roy | Enrico Grisan | John Buckeridge | Chiara Gattinoni
Source: ArXiv.org (preprint, 2025-10-23)
Field: Materials Science — Materials Chemistry

ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature Since the advent of various pre-trained large language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional machine learning or natural language processing techniques. Despite these advances, accessible automated tools that allow users to construct, validate, and visualise datasets from scientific literature extraction remain scarce. We therefore developed ComProScanner, an autonomous multi-agent platform that facilitates the extraction, validation, classification, and visualisation of machine-readable chemical compositions and properties, integrated with synthesis data from journal articles for comprehensive database creation. We evaluated our framework using 100 journal articles against 10 different LLMs, including both open-source and proprietary models, to extract highly complex compositions associated with ceramic piezoelectric materials and corresponding piezoelectric strain coefficients (d33), motivated by the lack of a large dataset for such materials. DeepSeek-V3-0324 outperformed all models with a significant overall accuracy of 0.82. This framework provides a simple, user-friendly, readily-usable package for extracting highly complex experimental data buried in the literature to build machine learning or deep learning datasets. Information extraction ; Data extraction ; Scientific literature ; Visualization ; Knowledge extraction ; Data visualization Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Artificial Intelligence in Healthcare and Education