jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabian P. Krüger | Andrea Hunklinger | Adrian Wolny | Tim J. Adler | Tetko Igor | Santiago D. Villalba
Source: Open MIND (preprint, 2026-01-31)
Field: Materials Science — Materials Chemistry

SEISMO: Increasing Sample Efficiency in Molecular Optimization with a Trajectory-Aware LLM Agent Optimizing the structure of molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry where it underlies the discovery of new drugs. Since molecular property evaluation often relies on costly and rate-limited oracles, such as experimental assays, molecular optimization must be highly sample-efficient. To address this, we introduce SEISMO, an LLM agent that performs strictly online, inference-time molecular optimization, updating after every oracle call without the need for population-based or batched learning. SEISMO conditions each proposal on the full optimization trajectory, combining natural-language task descriptions with scalar scores and, when available, structured explanatory feedback. Across the Practical Molecular Optimization benchmark of 23 tasks, SEISMO achieves a 2-3 times higher area under the optimisation curve than prior methods, often reaching near-maximal task scores within 50 oracle calls. Our additional medicinal-chemistry tasks show that providing explanatory feedback further improves efficiency, demonstrating that leveraging domain knowledge and structured information is key to sample-efficient molecular optimization. Bottleneck ; Task (project management) ; Oracle ; Benchmark (surveying) ; Key (lock) ; Domain (mathematical analysis) ; Virtual screening ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Fabian P. Krüger | Andrea Hunklinger | Adrian Wolny | Tim J. Adler | Tetko Igor | Santiago D. Villalba
Source: arXiv (Cornell University) (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

SEISMO: Increasing Sample Efficiency in Molecular Optimization with a Trajectory-Aware LLM Agent Optimizing the structure of molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry where it underlies the discovery of new drugs. Since molecular property evaluation often relies on costly and rate-limited oracles, such as experimental assays, molecular optimization must be highly sample-efficient. To address this, we introduce SEISMO, an LLM agent that performs strictly online, inference-time molecular optimization, updating after every oracle call without the need for population-based or batched learning. SEISMO conditions each proposal on the full optimization trajectory, combining natural-language task descriptions with scalar scores and, when available, structured explanatory feedback. Across the Practical Molecular Optimization benchmark of 23 tasks, SEISMO achieves a 2-3 times higher area under the optimisation curve than prior methods, often reaching near-maximal task scores within 50 oracle calls. Our additional medicinal-chemistry tasks show that providing explanatory feedback further improves efficiency, demonstrating that leveraging domain knowledge and structured information is key to sample-efficient molecular optimization. Bottleneck ; Task (project management) ; Oracle ; Benchmark (surveying) ; Key (lock) ; Domain (mathematical analysis) ; Virtual screening ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Chengbo Li | Nian Ran | Jianxing Liu
Source: Journal of Materials Informatics (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Agentic material science Artificial intelligence (AI) agents, leveraging capabilities in natural language understanding, multimodal knowledge fusion, and tool invocation, are driving material science towards a new stage of agent-driven. This article systematically reviews the progress of AI agents in material science. It highlights their core innovation in material knowledge processing, structure design, and property calculation, significantly accelerating the materials design process. Furthermore, the article analyzes the impact of agents on experiments, which promote the automation of material synthesis and characterization. The integration of these capabilities is driving the development of self-driving laboratories, moving the field towards end-to-end autonomous materials creation. By providing a comprehensive overview of this rapidly developing field, this review aims to clarify the deep integration of AI agents with material science, thereby accelerating the realization of on-demand material design. Field (mathematics) ; Property (philosophy) ; Realization (probability) ; Automation ; Natural (archaeology) ; Core (optical fiber) Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Advanced Sensor and Energy Harvesting Materials
Authors: Juan Carlos Verduzco | Mohnish Harwani | Daniel Mejia | Alejandro Strachan
Source: ChemRxiv (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Autonomous Universal Research Assistant (AURA): Agentic AI meets nanoHUB's FAIR Workflows and Data Agentic artificial intelligence is increasingly impacting scientific research, from ideation and planning to execution. However, most research assistants target specific domains and rely on predetermined toolsets, limiting scalability and community contribution. We introduce AURA, a multi-agent assistant that integrates a large language model with nanoHUB's expert-contributed, FAIR simulation tools and data. AURA discovers and orchestrates these sources of information by reasoning over their metadata at runtime. Thus, new tools and data become available as they are published. With case studies in protein folding, high-pressure materials properties, and melting temperature calculations, we demonstrate capabilities for scientific planning, tool discovery, workflow composition, and autonomous execution. Learning from prior simulation results, AURA optimizes tool inputs and reproduces a recently published result in minutes, a 20x–30x speedup over expert researchers. AURA illustrates a scalable approach to autonomous research assistants that encourages community contributions and will result in an organic growth in capabilities. Workflow ; Scalability ; Metadata ; Limiting ; Relational database ; Automated planning and scheduling ; Automation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Lisheng Zhang | Lilong Wang | Xiangyu Sun | W.H. Wilson Tang | Haoyang Su | Yuehui Qian | Q. Yang | Qingsong Li | Zhenyu Tang | Haoran Sun | Yingnan Han | Yankai Jiang | Wenjie Lou | Bin Zhou | Xiaosong Wang | Lei Bai | Zhengwei Xie
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization Abstract Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw , an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench , a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery. Workflow ; Bottleneck ; Benchmark (surveying) ; Domain (mathematical analysis) ; Competence (human resources) ; Orchestration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Jiyi Yang | Yoshinao Kobayashi | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

AI agents for automating materials research: a case study of crystal plasticity simulations This work introduces a multi-agent system, CrystalPlasticitySim, which leverages large language models (LLMs) to automate complex workflows involved in crystal plasticity simulations. The system substantially reduces the human effort required to learn, prepare, and execute crystal plasticity simulations, enabling workflows that previously required months of manual effort to be completed within hours of autonomous execution. Traditionally, using crystal plasticity simulations requires not only deep expertise in materials science but also technical knowledge of a specific simulation package and computational skills – forcing users to manually configure parameters, prepare input files, and troubleshoot simulations through laborious trial-and-error processes. These technical hurdles limit accessibility and slow scientific progress. To overcome these challenges, CrystalPlasticitySim employs three collaborating AI agents – the Supervisor Agent, Simulation Agent, and Computational Assistant Agent – that autonomously generate input files, execute simulations, extract results, and even perform parameter optimization. Using a case study on the anisotropic behavior of Ni3Al single crystals during cold rolling, we demonstrate that the system can autonomously solve well-defined tasks, thereby lowering the barrier to crystal plasticity modeling and improving accessibility to advanced simulation tools to researchers across disciplines. Workflow ; Troubleshooting ; Crystal plasticity ; Forcing (mathematics) ; Supervisor ; Work (physics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and Mechanical Properties of Steels
Authors: Zongxiao Jin | Xiaobo Sun | Xiaoli Xi | Zuoren Nie
Source: Journal of Computational Chemistry (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Simulon: An <scp>AI</scp> ‐Assisted, <scp>PyTorch</scp> ‐Native Framework of Molecular Dynamics and Modeling The growing complexity of modern molecular simulations calls for new frameworks that unify physical modeling, machine learning, and human-AI interaction in the era of artificial intelligence. Simulon is an open-source, PyTorch-native molecular dynamics (MD) platform designed to bridge MD and artificial intelligence through a tensor-based architecture. By representing atomic systems, forces, and trajectories as differentiable tensors, Simulon enables end-to-end learning over molecular data while leveraging GPU hardware and PyTorch software to accelerate simulations. Compared with other similar frameworks, Simulon employs a highly optimized tensor-based computational kernel, achieving substantial speedup. A retrieval-augmented large language model (LLM) agent serves as an intelligent interface, translating natural-language instructions into executable simulation workflows and analytical routines, which allow scientists to configure, execute, and interpret MD simulations conversationally, forming an autonomous loop between physical computation and semantic intent. This integration can relieve scientists of tedious data processing jobs and enable them to focus on more creative work. Simulon supports both classical and machine-learning potentials under GPU acceleration and achieves quantitative agreement with established engines such as LAMMPS while maintaining ML compatibility. By combining differentiable simulation, scalable computation, and natural-language control, Simulon establishes a new paradigm for AI-assisted molecular modeling-where chemical information, data-driven potentials, and autonomous agents converge to accelerate scientific discovery. Executable ; Workflow ; Scalability ; Molecular dynamics ; Differentiable function ; Focus (optics) ; Software ; Interface (matter) ; Physics engine Machine Learning in Materials Science ; Scientific Computing and Data Management ; Block Copolymer Self-Assembly
Authors: Maxwell J. Jacobson | Daniel Xie | Jackson Shen | Adil Wazeer | Haiyan Wang | Xinghang Zhang | Yexiang Xue
Source: ArXiv.org (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, including autonomous summarization and question answering, have been developed to aid in understanding scientific literature. However, these tools lack the structured, multi-step approach necessary for extracting deep insights from scientific literature. Large Language Models (LLMs) offer new possibilities for literature analysis, but remain unreliable due to hallucinations and incomplete extraction. We introduce Elhuyar, a multi-agent, human-in-the-loop system that integrates LLMs, structured AI, and human scientists to extract, analyze, and iteratively refine insights from scientific literature. The framework distributes tasks among specialized agents for filtering papers, extracting data, fitting models, and summarizing findings, with human oversight ensuring reliability. The system generates structured reports with extracted data, visualizations, model equations, and text summaries, enabling deeper inquiry through iterative refinement. Deployed in materials science, it analyzed literature on tungsten under helium-ion irradiation, showing experimentally correlated exponential helium bubble growth with irradiation dose and temperature, offering insight for plasma-facing materials (PFMs) in fusion reactors. This demonstrates how AI-assisted literature review can uncover scientific patterns and accelerate discovery. Automatic summarization ; Scientific literature ; Scientific discovery ; Workflow ; Abstraction ; Scientific progress Machine Learning in Materials Science ; Computational and Text Analysis Methods ; scientometrics and bibliometrics research
Authors: Seongmin Kim | Jaehwan Choi | K. I. Jang | Junkil Park | Varinia Bernales | Alán Aspuru-Guzik | Yousung Jung
Source: arXiv (Cornell University) (preprint, 2026-01-22)
Field: Materials Science — Materials Chemistry

Materealize: a multi-agent deliberation system for end-to-end material design and synthesis We propose Materealize, a multi-agent system for end-to-end inorganic materials design and synthesis that orchestrates core domain tools spanning structure generation, property prediction, synthesizability prediction, and synthesis planning within a single unified framework. Through a natural-language interface, Materealize enables non-experts to access computational materials workflows and obtain experimentally actionable outputs for material realization. Materealize provides two complementary modes. In instant mode, the system rapidly composes connected tools to solve diverse inorganic tasks-including property-conditioned synthesizable candidate design with synthesis recipes, diagnosis, and redesign of unsynthesizable structures, and synthesizable data augmentation-within a few minutes. In thinking mode, Materealize applies multi-agent debate to deliver more refined and information-rich synthesis recommendations, including reasoning- and model-driven synthesis routes and mechanistic hypotheses. The mechanistic hypotheses are validated by direct comparison with the literature for known mechanisms and further supported by physics-grounded simulations for novel synthesis pathways. By combining tool-level accuracy with reasoning-level integration, Materealize can bridge the gap between computational discovery and practical experimental realization. Workflow ; Bridge (graph theory) ; Core (optical fiber) ; Domain (mathematical analysis) ; Property (philosophy) ; Deliberation ; Texture synthesis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Aikaterini Vriza | Michael H. Prince | Tao Zhou | Henry Chan | Mathew J. Cherukara
Source: npj Computational Materials (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Operating advanced scientific instruments with AI agents that learn on the job Abstract Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities. Pipeline (software) ; Automation ; Toolbox ; Focus (optics) ; Bridge (graph theory) ; Transformative learning ; Workflow Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Multimodal Machine Learning Applications
Authors: Daojian Zeng | Tianle Li | Jiahao Yang | Jiacai Yi | Xieping Gao | Lin Jiang | Tengfei Ma | Xiangxiang Zeng
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Expert-Inspired Multi-Agent Coordination for Multi-Objective Molecular Optimization Multi-objective molecular optimization is a fundamental yet inherently challenging task in drug discovery, as it requires simultaneously optimizing multiple, often conflicting, molecular properties. Although recent deep learning methods have shown promise, they often lack objective-specific specialization and dynamic coordination, making them ineffective in handling competing objectives and difficult to scale in complex, high-dimensional molecular design tasks. Inspired by the division of labor among domain experts in medicinal chemistry, we propose MAMO, a multi-agent framework for molecular design that simulates expert collaboration. Each agent specializes in optimizing a single objective, and their interactions are orchestrated by a central scheduling module that dynamically reallocates tasks based on evaluation feedback. This coordination mechanism enables interpretable and goal-conditioned optimization while adaptively balancing conflicting objectives. Extensive experiments on benchmark datasets demonstrate that MAMO consistently achieves superior performance in both objective quality and Pareto diversity, particularly in scenarios with strong inter-objective conflict. Our results highlight the potential of multi-agent coordination strategies for scalable and conflict-aware molecular design. Scalability ; Scheduling (production processes) ; Benchmark (surveying) ; Task (project management) ; Domain (mathematical analysis) ; Pareto principle ; Scale (ratio) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Huan Zhang | Yizhan Li | Wenhao Huang | Ziyu Hou | Yu Song | Xuye Liu | Farshid Effaty | Jinya Jiang | Sifan Wu | Qianggang Ding | Izumi Takahara | Leonard R. MacGillivray | Teruyasu Mizoguchi | Tianshu Yu | Lizi Liao | Yuyu Luo | Rong Yu | Jia Li | Ying Diao | Heng Ji | Bang Liu
Source: arXiv (Cornell University) (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Towards Agentic Intelligence for Materials Science The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials. Workflow ; Process (computing) ; Transformative learning ; Interfacing ; Field (mathematics) ; Scientific discovery ; Affordance ; Domain knowledge ; TRACE (psycholinguistics) ; Agile software development Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management