jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samuel Rothfarb | Megan C. Davis | Ivana Matanović | Baikun Li | Edward F. Holby | Wilton J. M. Kort-Kamp
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), an active learning framework where large language models autonomously design, execute, and interpret atomistic simulations. In MASTER, a multimodal system translates natural language into density functional theory workflows, while higher-level reasoning agents guide discovery through a hierarchy of strategies, including a single agent baseline and three multi-agent approaches: peer review, triage-ranking, and triage-forms. Across two chemical applications, CO adsorption on Cu-surface transition metal (M) adatoms and on M-N-C catalysts, reasoning-driven exploration reduces required atomistic simulations by up to 90% relative to trial-and-error selection. Reasoning trajectories reveal chemically grounded decisions that cannot be explained by stochastic sampling or semantic bias. Altogether, multi-agent collaboration accelerates materials discovery and marks a new paradigm for autonomous scientific exploration. Hierarchy ; Natural language ; Language model ; Natural language understanding ; Limiting ; Autonomous agent ; Automated reasoning ; Baseline (sea) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Tongao Yao | Junming Huang | Yujie Yan | Yang Yang | Ziye Wang | Xuqiang Shao | Zhijun Gao | Weijie Yang
Source: AI Agent (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

From large language models to AI agents in energy materials research: enabling discovery, design, and automation Fragmented knowledge and slow experimental iteration constrain the discovery of energy materials. We trace the evolution of artificial intelligence (AI) in materials science, from large language models as knowledge assistants to autonomous agents that can reason, plan, and use tools. We introduce a two-path framework to analyze this evolution, distinguishing architectural innovation (agent collaboration) from cognitive innovation (learning and representation). This framework synthesizes recent progress in AI-driven discovery, design, and automation. By examining challenges in reliability, interpretability, and physical grounding, we outline a roadmap toward physics-informed, human-AI systems for autonomous scientific discovery. TRACE (psycholinguistics) ; Automation ; Energy (signal processing) ; Field (mathematics) ; Semantics (computer science) ; Knowledge-based systems Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Multimodal Machine Learning Applications
Authors: Xinrui Wang | Chengbo Li | Baopeng Zhang | Jiahui Shi | Nian Ran | Linjing Li | Jianjun Liu | Donglin Zeng
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

S1-MatAgent: A planner driven multi-agent system for material discovery The discovery of high-performance materials is crucial for technological advancement. Inverse design using multi-agent systems (MAS) shows great potential for new material discovery. However, current MAS for materials research rely on predefined configurations and tools, limiting their adaptability and scalability. To address these limitations, we developed a planner driven multi-agent system (S1-MatAgent) which adopts a Planner-Executor architecture. Planner automatically decomposes complex materials design tasks, dynamically configures various tools to generate dedicated Executor agents for each subtask, significantly reducing reliance on manual workflow construction and specialized configuration. Applied to high-entropy alloy catalysts for hydrogen evolution reactions in alkaline conditions, S1-MatAgent completed full-cycle closed-loop design from literature analysis and composition recommendation to performance optimization and experimental validation. To tackle the deviations between designed materials and target, as well as high experimental verification costs, S1-MatAgent employs a novel composition optimization algorithm based on gradients of machine learning interatomic potential, achieving 27.7 % improvement in material performance. S1-MatAgent designed 13 high-performance catalysts from 20 million candidates, with Ni4Co4Cu1Mo3Ru4 exhibiting an overpotential of 18.6 mV at 10 mA cm-2 and maintaining 97.5 % activity after 500 hours at 500 mA cm-2. The universal MAS framework offers a universal and scalable solution for material discovery, significantly improving design efficiency and adaptability. Workflow ; Scalability ; Adaptability ; Planner ; Limiting ; Extensibility Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; High Entropy Alloys Studies
Authors: Yingjie Shi | Yiru Gong | Yuchang Su | Shisheng Xiong | Jiale Han | Runtian Miao
Source: ArXiv.org (preprint, 2025-06-19)
Field: Materials Science — Materials Chemistry

Aethorix v1.0: An Integrated Scientific AI Agent for Scalable Inorganic Materials Innovation and Industrial Implementation Artificial Intelligence (AI) is redefining the frontiers of scientific domains, ranging from drug discovery to meteorological modeling, yet its integration within industrial manufacturing remains nascent and fraught with operational challenges. To bridge this gap, we introduce Aethorix v1.0, an AI agent framework designed to overcome key industrial bottlenecks, demonstrating state-of-the-art performance in materials design innovation and process parameter optimization. Our tool is built upon three pillars: a scientific corpus reasoning engine that streamlines knowledge retrieval and validation, a diffusion-based generative model for zero-shot inverse design, and specialized interatomic potentials that enable faster screening with ab initio fidelity. We demonstrate Aethorix's utility through a real-world cement production case study, confirming its capacity for integration into industrial workflows and its role in revolutionizing the design-make-test-analyze loop while ensuring rigorous manufacturing standards are met. Workflow ; Scalability ; Process (computing) ; Key (lock) ; Bridge (graph theory) ; Consistency (knowledge bases) ; Data integration ; Knowledge integration Machine Learning in Materials Science ; Scientific Computing and Data Management ; AI-based Problem Solving and Planning
Authors: Rupesh Kohli | Vishwajit K. Barbudhe | Vijay Rakhade | Trushna M Bhanarkar | Nandini S. Patil | Shreya Deshmukh
Source:  (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Autonomous AI Agents in Scientific Discovery – How Agentic AI Accelerates Research in Drug Design, Materials Science, or Climate Modeling You know, science is getting a serious upgrade thanks to these new agentic AI systems. We’re not talking about the old-school, "let’s analyze some data and hope for the best" kind of stuff—now AI can actually plan, learn, and run experiments on its own, all in this wild sort of feedback loop where it designs, tests, learns, and just keeps getting better. With this closed-loop thing, you mash up automation with hands-on experimentation, and suddenly your AI is tossing out high-value experiments, speeding up the whole process, and even taking some of the grunt work off scientists’ plates. That’s a big deal in areas like drug development, material science, and even climate prediction—stuff that used to take ages, now? Way faster, less mind-numbing for people, and actually exploring smarter options. The paper runs through the current tech stack— platforms, system architecture, all the little bits like perception, planning, reinforcement learning, symbolic reasoning, plus tossing humans into the mix when needed. It digs into how these systems work across different fields, sketches out how you’d roll out one of these setups no matter the industry, and doesn’t shy away from the messy parts (yeah, I’m talking about trust issues, bias, safety hiccups, reproducibility headaches). Then there’s some crystal ball gazing about what all this means for science, both now and down the road. Bottom line? Using things like policy optimization, these agentic AI setups can shrink development cycles without turning your research into a rush job. Science just got way more interesting. Drug discovery ; Scientific discovery ; Key (lock) ; Drug Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Truyen Tran
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

AI for Scientific Discovery: A New Frontier Sydney Brenner, Nobel Laureate in Physiology, once said, “Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order.” A paradigm-shifting technology of our time is artificial intelligence (AI), which offers new ways to accelerate scientific discovery. This talk will explore how AI is transforming science by automating discovery workflows—representing and leveraging scientific artefacts and knowledge, predicting properties and phenomena, optimising and generating new designs, and reasoning about competing hypotheses. We will also discuss the emerging concept of AI co-scientists: autonomous agents that emulate the scientific discovery process by combining creative exploration with systematic reasoning. These agents collectively propose hypotheses, design experiments, analyse results, and communicate findings while collaborating with human scientists. Specific examples will be drawn from molecular space exploration, drug–protein interactions, chemical synthesis, crystal structure prediction, and property prediction for advanced materials such as superconductors and alloys. Scientific discovery ; Nobel laureate ; Frontier ; Process (computing) ; Applications of artificial intelligence ; Property (philosophy) ; Space (punctuation) ; Scientific progress Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Tuan D. Pham | Aditya Tanikanti | Murat Keçeli
Source: ArXiv.org (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

ChemGraph: An Agentic Framework for Computational Chemistry Workflows Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations with methods ranging from tight-binding and machine learning interatomic potentials to density functional theory or wave function theory-based methods. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models like GPT-4o. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables smaller LLM models to match or exceed GPT-4o's performance in specific scenarios. Workflow ; Cheminformatics ; Range (aeronautics) ; Computational model ; Benchmark (surveying) ; Software ; Task (project management) ; Function (biology) ; Graph Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Yuki Orimo | Iori Kurata | Hiroki Mori | Ryuhei Okuno | Ryohto Sawada | Daisuke Okanohara
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks We introduce PARC, a coding agent for the autonomous and robust execution of long-horizon computational tasks. PARC is built on a hierarchical multi-agent architecture incorporating task planning, execution, and a mechanism that evaluates its own actions and their outcomes from an independent context and provides feedback, namely self-assessment and self-feedback. This design enables PARC to detect and correct high-level strategic errors and sustain progress without human intervention. We evaluate PARC across computational science and data science tasks. In materials science, it autonomously reproduces key results from studies on lithium-ion conduction and alloy segregation. In particular, it coordinates dozens of parallel simulation tasks, each requiring roughly 43 hours of computation, managing orchestration, monitoring, and error correction end-to-end. In Kaggle-based experiments, starting from minimal natural-language instructions, PARC conducts data analysis and implements search strategies, producing solutions competitive with human-engineered baselines. These results highlight the potential of integrating a hierarchical multi-agent system with self-assessment and self-feedback to enable AI systems capable of independent, large-scale scientific and analytical work. Coding (social sciences) ; Task (project management) ; Context (archaeology) ; Key (lock) ; Robustness (evolution) ; Computational model ; Computational complexity theory ; Architecture Machine Learning in Materials Science ; Scientific Computing and Data Management ; Big Data and Digital Economy
Authors: Jieyu Lu (10124828) | Zhangde Song (21655163) | Qiyuan Zhao (8605641) | Yuanqi Du (17903453) | Yirui Cao (9956019) | Haojun Jia (17955501) | Chenru Duan (5831468)
Source: Figshare (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Generative Design ofFunctional Metal Complexes Utilizingthe Internal Knowledge and Reasoning Capability of Large LanguageModels Large language models (LLMs) have shown promise in science, such as structure–property prediction and acting as AI agents, yet their intrinsic knowledge and reasoning capability for scientific discovery remains underexplored. We introduce LLM-EO, an integration of LLMs into evolutionary optimization, and demonstrate its success in optimizing transition metal complexes (TMCs). LLM-EO demonstrates advantages in few-sample learning due to the intrinsic chemical knowledge embedded within LLMs and their ability to leverage entire historical data collected during optimizations. Through natural language instructions, LLM-EO offers enhanced accessibility for multiobjective optimizations, potentially lowering barriers for experimental chemists without extensive programming expertise. As generative models, LLM-EO possesses the capability to propose novel ligands and TMCs with unique chemical properties by amalgamating both internal knowledge and external chemistry data, thus combining the benefits of efficient optimization and generation. With advancements in LLMs, both in their capacity as pretrained foundational models and new strategies in post-training inference, we anticipate broad applications of LLM-EO in chemistry and materials design. Generative grammar ; Leverage (statistics) ; Natural language ; Generative model ; Domain (mathematical analysis) ; Natural language understanding Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Saejin Oh | Xinyi Fang | I Long Lin | Paris Dee | Christopher Dunham | Stacy M. Copp | Abigail G. Doyle | Javier Read de Alaniz | Mengyang Gu
Source: ArXiv.org (preprint, 2025-10-18)
Field: Materials Science — Materials Chemistry

Synergizing chemical and AI communities for advancing laboratories of the future The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As many laboratory tasks involve predicting and understanding previously unknown chemical relationships, machine learning (ML) approaches trained on experimental data can substantially accelerate the conventional design-build-test-learn process. This outlook article aims to help chemists understand and begin to adopt ML predictive models for a variety of laboratory tasks, including experimental design, synthesis optimization, and materials characterization. Furthermore, this article introduces how artificial intelligence (AI) agents based on large language models can help researchers acquire background knowledge in chemical or data science and accelerate various aspects of the discovery process. We present three case studies in distinct areas to illustrate how ML models and AI agents can be leveraged to reduce time-consuming experiments and manual data analysis. Finally, we highlight existing challenges that require continued synergistic effort from both experimental and computational communities to address. Variety (cybernetics) ; Digitization ; Experimental data ; Experimental science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Faqing Yang | Weitong Chen | Jack D. Evans
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Large language models in materials science and the need for open-source approaches Large language models (LLMs) are rapidly transforming materials science. This review examines recent LLM applications across the materials discovery pipeline, focusing on three key areas: mining scientific literature, predictive modelling,... Key (lock) ; Benchmark (surveying) ; Scientific modelling ; Language model ; Scientific discovery Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Inorganic Chemistry and Materials
Authors: Heng Lian | Lei Du | Jian Wu | Yi He
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

AI-Driven Literature Mining for Intelligent Synthesis Design The exponential growth of scientific literature creates information overload for materials synthesis researchers, making comprehensive literature reviews increasingly impractical. Current large language models struggle with domain-specific entity extraction and lack effective unstructured data reading capabilities for processing experimental data in figures and tables. We present an automated framework for intelligent synthesis design that first employs semantic similarity-based screening to identify relevant papers from vast literature databases, then systematically extracts synthesis compositions, elemental ratios, and performance metrics through progressive ontology prompting method and dual LLM agents. The framework incorporates multi-modal capabilities from large multi-model models for understanding scientific figures and tables, implements automated entity standardization to resolve compound representation variations, and applies explainable AI which leverages machine learning analysis to identify promising elemental combinations. This system efficiently processes thousands of scientific papers to provide data-driven guidance for experimental materials synthesis while reducing research costs and accelerating discovery. Expert system ; Production (economics) ; Feature (linguistics) ; Key (lock) ; Process (computing) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Biomedical Text Mining and Ontologies
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 | C. Gomes
Source: arXiv (Cornell University) (article, 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