jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Faqing Yang | Jack D. Evans
Source: arXiv (Cornell University) (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-calling paradigms and narrowly scoped agents. In this work, we introduce QUASAR, a universal autonomous system for atomistic simulation designed to facilitate production-grade scientific discovery. QUASAR autonomously orchestrates complex multi-scale workflows across diverse methods, including density functional theory, machine learning potentials, molecular dynamics, and Monte Carlo simulations. The system incorporates robust mechanisms for adaptive planning, context-efficient memory management, and hybrid knowledge retrieval to navigate real-world research scenarios without human intervention. We benchmark QUASAR against a series of three-tiered tasks, progressing from routine tasks to frontier research challenges such as photocatalyst screening and novel material assessment. These results suggest that QUASAR can function as a general atomistic reasoning system rather than a task-specific automation framework. They also provide initial evidence supporting the potential deployment of agentic AI as a component of computational chemistry research workflows, while identifying areas requiring further development. Benchmark (surveying) ; Workflow ; Software deployment ; Function (biology) ; Transformative learning ; Automation ; Component (thermodynamics) ; Monte Carlo method Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Yishu Wang | Wei Liu | Yifan Li | Shengxiang Xu | Xujie Yuan | Ran Li | Yuyu Luo | Jia Zhu | Shimin Di | Min-Ling Zhang | Guixiang Li
Source: Open MIND (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

PeroMAS: A Multi-agent System of Perovskite Material Discovery As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential. The development process of PSCs is precise and complex, involving a series of closed-loop workflows such as literature retrieval, data integration, experimental design, and synthesis. However, existing AI perovskite approaches focus predominantly on discrete models, including material design, process optimization,and property prediction. These models fail to propagate physical constraints across the workflow, hindering end-to-end optimization. In this paper, we propose a multi-agent system for perovskite material discovery, named PeroMAS. We first encapsulated a series of perovskite-specific tools into Model Context Protocols (MCPs). By planning and invoking these tools, PeroMAS can design perovskite materials under multi-objective constraints, covering the entire process from literature retrieval and data extraction to property prediction and mechanism analysis. Furthermore, we construct an evaluation benchmark by perovskite human experts to assess this multi-agent system. Results demonstrate that, compared to single Large Language Model (LLM) or traditional search strategies, our system significantly enhances discovery efficiency. It successfully identified candidate materials satisfying multi-objective constraints. Notably, we verify PeroMAS's effectiveness in the physical world through real synthesis experiments. Perovskite (structure) ; Workflow ; Context (archaeology) ; Property (philosophy) ; Photovoltaic system ; Process (computing) ; Benchmark (surveying) ; Construct (python library) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Scientific Computing and Data Management
Authors: Yishu Wang | Wei Liu | Yifan Li | Shengxiang Xu | Xujie Yuan | Ran Li | Yuyu Luo | Jia Zhu | Shimin Di | Min-Ling Zhang | Guixiang Li
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

PeroMAS: A Multi-agent System of Perovskite Material Discovery As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential. The development process of PSCs is precise and complex, involving a series of closed-loop workflows such as literature retrieval, data integration, experimental design, and synthesis. However, existing AI perovskite approaches focus predominantly on discrete models, including material design, process optimization,and property prediction. These models fail to propagate physical constraints across the workflow, hindering end-to-end optimization. In this paper, we propose a multi-agent system for perovskite material discovery, named PeroMAS. We first encapsulated a series of perovskite-specific tools into Model Context Protocols (MCPs). By planning and invoking these tools, PeroMAS can design perovskite materials under multi-objective constraints, covering the entire process from literature retrieval and data extraction to property prediction and mechanism analysis. Furthermore, we construct an evaluation benchmark by perovskite human experts to assess this multi-agent system. Results demonstrate that, compared to single Large Language Model (LLM) or traditional search strategies, our system significantly enhances discovery efficiency. It successfully identified candidate materials satisfying multi-objective constraints. Notably, we verify PeroMAS's effectiveness in the physical world through real synthesis experiments. Perovskite (structure) ; Workflow ; Context (archaeology) ; Property (philosophy) ; Photovoltaic system ; Process (computing) ; Benchmark (surveying) ; Construct (python library) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Scientific Computing and Data Management
Authors: Xian Wang | Qun Zeng | Dong-Hui Xu | Li Zhang | Gang Jiang | Mingli Yang
Source: Journal of Materials Informatics (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery via AI-Agent integration of large language models and simulation tools The integration of artificial intelligence (AI) with materials science is driving a paradigm shift in how functional materials are discovered and designed. In this work, we present an AI-Agent platform that leverages large language model-driven reasoning to assist users in designing and executing computational workflows for materials research. Rather than relying on rigid pipelines, the Agent interprets natural language prompts, dynamically assembles task-specific workflows from existing simulation tools, and executes calculations accordingly. To illustrate its capabilities, we show two representative cases: (i) a goal-driven electronic structure calculation for periodic monolayer transition metal dichalcogenides, and (ii) an inverse design of battery electrolyte additives based on user-defined targets for molecular weight and frontier orbital energies. These examples illustrate the Agent’s capacity to translate high-level design intent into coordinated multi-tool operations, thereby streamlining complex workflows and lowering the entry barrier for non-expert users. As AI continues to advance, the Agent is poised to become an increasingly valuable partner in materials research, enhancing efficiency and improving design quality, and enabling broader access to materials discovery. Workflow ; Natural language ; Key (lock) ; Battery (electricity) ; Computational model ; Modeling language ; Language model Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Achuth Chandrasekhar | Janghoon Ock | Amir Barati Farimani
Source: ArXiv.org (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for this include time-consuming and expensive experimental trial-and-error approaches in labs based on chemical theory or heavily computational first-principles approaches based on density functional theory. Recent studies show that deep learning models like graph neural networks (GNNs) can significantly speed up the screening and discovery of catalyst materials by many orders of magnitude, with very high accuracy and fidelity. In this work, we introduce Catalyst-Agent, a Model Context Protocol (MCP) server-based, LLM-powered AI agent. It can explore vast material databases using the OPTIMADE API, make structural modifications, calculate adsorption energies using Meta FAIRchem's UMA (GNN) model via FAIRchem's AdsorbML workflow and slab construction, and make useful material suggestions to the researcher in a closed-loop manner, including surface-level modifications to refine near-miss candidates. It is tested on three pivotal reactions: the oxygen reduction reaction (ORR), the nitrogen reduction reaction (NRR), and the CO2 reduction reaction (CO2RR). Catalyst-Agent achieves a success rate of 23-34 percent among all the materials it chooses and evaluates, and manages to converge in 1-2 trials per successful material on average. This work demonstrates the potential of AI agents to exercise their planning capabilities and tool use to operationalize the catalyst screening workflow, provide useful, testable hypotheses, and accelerate future scientific discoveries for humanity with minimal human intervention. Workflow ; Reduction (mathematics) ; Context (archaeology) ; Protocol (science) ; Operationalization ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: WU Ying | LU Zhong-Yi | GAO Ze-Feng
Source: Acta Physica Sinica (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Applications of multi-agent systems in computational materials science 随着材料研发日益复杂，传统“试错法”与零散计算模拟面临效率低下、资源消耗大的挑战。人工智能，特别是大语言模型的发展，为材料计算领域带来新机遇，其中多智能体系统通过模拟科研团队协作，展现出处理复杂任务的潜力。本文系统综述了多智能体在物理材料计算中的应用，重点分析了VASPilot、PhysAgent等专业化系统如何通过角色分工与工具集成，实现从结构建模、参数设置到结果分析的全流程自动化。研究表明，多智能体系统能够有效提升计算效率与可重复性，并在物理定律自主发现、复杂材料模拟等方面取得初步成果。然而，通过对现有系统的客观分析和实际操作使用，本文发现其在计算资源分配、物理一致性、科学创造性等方面仍存在局限。未来通过深化物理机理融合、构建闭环科研生态，多智能体有望从辅助工具演化为能够自主探索与发现的科研伙伴，推动材料研究范式的根本变革。 Computational model ; Science, technology and society Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics
Authors: Tongyu Shi | Yanfang Li | Zhanlong Wang | Wenhe Xu | Guolai Jiang | Dawei Dai | Jin Zhou | Hao Huang | Rui He | Seeram Ramakrishna | Paul K. Chu | Wenhua Zhou | Xue‐Feng Yu
Source: Matter (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Knowledge-driven autonomous materials research via collaborative multi-agent and robotic system Robot ; Focus (optics) ; Mars Exploration Program ; Work (physics) ; Architecture ; Acceleration ; Natural language ; Complex system Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Perovskite Materials and Applications
Authors: Xiangyi Wei | Fei Wang | Haotian Zhang | Xin An | Haitian Zhu | Lianrui Hu | Yang Li | Changbo Wang | Xiao He
Source: arXiv (Cornell University) (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

AgentChemist: A Multi-Agent Experimental Robotic Platform Integrating Chemical Perception and Precise Control Chemical laboratory automation has long been constrained by rigid workflows and poor adaptability to the long-tail distribution of experimental tasks. While most automated platforms perform well on a narrow set of standardized procedures, real laboratories involve diverse, infrequent, and evolving operations that fall outside predefined protocols. This mismatch prevents existing systems from generalizing to novel reaction conditions, uncommon instrument configurations, and unexpected procedural variations. We present a multi-agent robotic platform designed to address this long-tail challenge through collaborative task decomposition, dynamic scheduling, and adaptive control. The system integrates chemical perception for real-time reaction monitoring with feedback-driven execution, enabling it to adjust actions based on evolving experimental states rather than fixed scripts. Validation via acid-base titration demonstrates autonomous progress tracking, adaptive dispensing control, and reliable end-to-end experiment execution. By improving generalization across diverse laboratory scenarios, this platform provides a practical pathway toward intelligent, flexible, and scalable laboratory automation. Workflow ; Automation ; Task (project management) ; Adaptability ; Scalability ; Set (abstract data type) ; Control (management) ; Perception Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Control Systems Optimization
Authors: Xiangyi Wei | Fei Wang | Haotian Zhang | Xin An | Haitian Zhu | Lianrui Hu | Yang Li | Changbo Wang | Xiao He
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

AgentChemist: A Multi-Agent Experimental Robotic Platform Integrating Chemical Perception and Precise Control Chemical laboratory automation has long been constrained by rigid workflows and poor adaptability to the long-tail distribution of experimental tasks. While most automated platforms perform well on a narrow set of standardized procedures, real laboratories involve diverse, infrequent, and evolving operations that fall outside predefined protocols. This mismatch prevents existing systems from generalizing to novel reaction conditions, uncommon instrument configurations, and unexpected procedural variations. We present a multi-agent robotic platform designed to address this long-tail challenge through collaborative task decomposition, dynamic scheduling, and adaptive control. The system integrates chemical perception for real-time reaction monitoring with feedback-driven execution, enabling it to adjust actions based on evolving experimental states rather than fixed scripts. Validation via acid-base titration demonstrates autonomous progress tracking, adaptive dispensing control, and reliable end-to-end experiment execution. By improving generalization across diverse laboratory scenarios, this platform provides a practical pathway toward intelligent, flexible, and scalable laboratory automation. Workflow ; Automation ; Task (project management) ; Adaptability ; Scalability ; Set (abstract data type) ; Control (management) ; Perception Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Control Systems Optimization
Authors: Jiawei Qiu | Youle Wang | Fan Jiang | Song Wei | Lei Zhang
Source: ChemRxiv (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

STALAgent: A Multi-Agent System Based on Large Language Model (LLM) for Steel and Alloy Design The design of steel and alloy materials is of critical importance across a wide range of industrial applications; however, effective intelligent agent-based assistants for this domain remain limited. To address this gap, we introduce STALAgent, a large language model (LLM)-based multi-agent system specifically tailored for intelligent and automated design of steel and alloy materials. STALAgent is centered on an LLM brain with several key agents (e.g., task assignment, semantic search, inverse design and heat treatment simulation) that collectively form a closed-loop workflow from user query to material recommendation. This system leverages a CrewAI-based orchestrator to intelligently assign tasks and coordinate a suite of specialized agents, including tools for knowledge retrieval using retrieval augmented generation (RAG), inverse materials design using variational encoder (VAE) and thermodynamic calculations using Pycalphad. Through case studies involving inverse alloy design tasks and knowledge-based steel design queries, we showcase the capacity of the LLM agent to offer effective and dependable guidance for steel and alloy material design. STALAgent is practical and scalable, serving as a supplementary tool for materials researchers, and holding promise for extension to other materials science domains requiring scientific discovery and domain knowledge-intensive tasks. Workflow ; Domain (mathematical analysis) ; Suite ; Key (lock) ; Task (project management) ; Domain-specific language ; Encoder ; Alloy ; Inverse Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Jihua Chen | Panagiotis Christakopoulos | Karuna D. Chen | Ilia N. Ivanov | Rigoberto C. Advíncula
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries. Applications of artificial intelligence ; Focus (optics) ; Core (optical fiber) ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Jihua Chen | Panagiotis Christakopoulos | Karuna D. Chen | Ilia N. Ivanov | Rigoberto C. Advíncula
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries. Applications of artificial intelligence ; Focus (optics) ; Core (optical fiber) ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Achuth Chandrasekhar | Janghoon Ock | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2026-03-01)
Field: Materials Science — Materials Chemistry

Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for this include time-consuming and expensive experimental trial-and-error approaches in labs based on chemical theory or heavily computational first-principles approaches based on density functional theory. Recent studies show that deep learning models like graph neural networks (GNNs) can significantly speed up the screening and discovery of catalyst materials by many orders of magnitude, with very high accuracy and fidelity. In this work, we introduce Catalyst-Agent, a Model Context Protocol (MCP) server-based, LLM-powered AI agent. It can explore vast material databases using the OPTIMADE API, make structural modifications, calculate adsorption energies using Meta FAIRchem's UMA (GNN) model via FAIRchem's AdsorbML workflow and slab construction, and make useful material suggestions to the researcher in a closed-loop manner, including surface-level modifications to refine near-miss candidates. It is tested on three pivotal reactions: the oxygen reduction reaction (ORR), the nitrogen reduction reaction (NRR), and the CO2 reduction reaction (CO2RR). Catalyst-Agent achieves a success rate of 23-34 percent among all the materials it chooses and evaluates, and manages to converge in 1-2 trials per successful material on average. This work demonstrates the potential of AI agents to exercise their planning capabilities and tool use to operationalize the catalyst screening workflow, provide useful, testable hypotheses, and accelerate future scientific discoveries for humanity with minimal human intervention. Workflow ; Reduction (mathematics) ; Context (archaeology) ; Protocol (science) ; Operationalization ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Dong Hyeon Mok | Seoin Back | Victor Fung | Guoxiang Hu
Source: arXiv (Cornell University) (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally require human expertise. This progress has extended into materials discovery, where LLMs introduce a new paradigm by leveraging reasoning and in-context learning, capabilities absent from conventional machine learning approaches. Here, we present a Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization (MAESTRO) framework in which multiple LLMs with specialized roles collaboratively discover high-performance single atom catalysts for the oxygen reduction reaction. Within an autonomous design loop, agents iteratively reason, propose modifications, reflect on results and accumulate design history. Through in-context learning enabled by this iterative process, MAESTRO identified design principles not explicitly encoded in the LLMs' background knowledge and successfully discovered catalysts that break conventional scaling relations between reaction intermediates. These results highlight the potential of multi-agent LLM frameworks as a powerful strategy to generate chemical insight and discover promising catalysts. Natural language ; Atom (system on chip) ; Scaling ; Language model ; Electrocatalyst ; Human language Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Rigoberto Advincula | Jihua Chen
Source: ChemRxiv (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Discovery Science with Autonomous ML-Driven Continuous Flow Chemistry In the context of discovery science, particularly in autonomous ML-driven continuous-flow chemistry, AI and ML play pivotal roles in general data analytics by transforming raw experimental data into actionable insights for experimental design. AI encompasses broader systems like autonomous agents and large language models (LLMs), while ML focuses on algorithms that learn from data to predict, optimize, and classify outcomes. In continuous flow setups, where reactions occur in real-time streams, data analytics involves handling multimodal data (e.g., spectroscopic, geometric, kinetic) from sensors and in-line monitors. This enables closed-loop systems in self-driving labs (SDLs), where AI/ML analyzes trends, reduces noise, and guides iterative experiments to efficiently explore chemical spaces, minimizing human intervention and accelerating discoveries in materials, catalysis, and pharmaceuticals. Analytics ; Context (archaeology) ; Continuous flow ; Raw data ; Data analysis ; Big data ; Flow (mathematics) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Scientific Computing and Data Management