jablonkagroup/rise_ai_scientists huggingface dataset:
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: Open MIND (preprint, 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
Authors: Zhaolin Hu | Zhiliang Wu | Hehe Fan | Yi Yang
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

OSCAgent: Accelerating the Discovery of Organic Solar Cells with LLM Agents Organic solar cells (OSCs) hold great promise for sustainable energy, but discovering high-performance materials is time-consuming and costly. Existing molecular generation methods can aid the design of OSC molecules, but they are mostly confined to optimizing known backbones and lack effective use of domain-specific chemical knowledge, often leading to unrealistic candidates. In this paper, we introduce OSCAgent, a multi-agent framework for OSC molecular discovery that unifies retrieval-augmented design, molecular generation, and systematic evaluation into a continuously improving pipeline, without requiring additional human intervention. OSCAgent comprises three collaborative agents. The Planner retrieves knowledge from literature-curated molecules and prior candidates to guide design directions. The Generator proposes new OSC acceptors aligned with these plans. The Experimenter performs comprehensive evaluation of candidate molecules and provides feedback for refinement. Experiments show that OSCAgent produces chemically valid, synthetically accessible OSC molecules and achieves superior predicted performance compared to both traditional and large language model (LLM)-only baselines. Representative results demonstrate that some candidates achieve predicted efficiencies approaching 18\%. The code will be publicly available. Organic molecules ; Generator (circuit theory) ; Organic solar cell ; Planner ; Chemical space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Honghao Chen | Jiangjie Qiu | Yi Shen Tew | Xiaonan Wang
Source: arXiv (Cornell University) (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

Autonomous Computational Catalysis Research via Agentic Systems Fully automating the scientific process is a transformative ambition in materials science, yet current artificial intelligence masters isolated workflow fragments. In computational catalysis, a system autonomously navigating the entire research lifecycle from conception to a scientifically meaningful manuscript remains an open challenge. Here we present CatMaster, a catalysis-native multi-agent framework that couples project-level reasoning with the direct execution of atomistic simulations, machine-learning modelling, literature analysis, and manuscript production within a unified autonomous architecture. Across progressively demanding evaluations, CatMaster achieves perfect scores on four end-to-end short-form catalysis scenarios, reaches near-leaderboard performance on five of six MatBench tasks, performs self-discovery of reaction mechanisms grounded in literature or from scratch, and executes a fully closed-loop single-atom catalyst design problem. Together, these results show that end-to-end autonomous computational catalysis is now practical for research programmes, while highlighting that bridging the gap to genuine scientific closure requires tighter integration with reliable physical engines and domain-rigorous methodologies. Workflow ; Scripting language ; Focus (optics) ; Key (lock) ; Automation ; Bookkeeping ; Heterogeneous catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: He Cao | Siyu Liu | Fan Zhang | Zijing Liu | Hao Li (31608) | Bin Feng | Shengyuan Bai | Leqing Chen | Kai Xie | Yu Li
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Mozi: Governed Autonomy for Drug Discovery LLM Agents Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and poor long-horizon reliability. In dependency-heavy pharmaceutical pipelines, autonomous agents often drift into irreproducible trajectories, where early-stage hallucinations multiplicatively compound into downstream failures. To overcome this, we present Mozi, a dual-layer architecture that bridges the flexibility of generative AI with the deterministic rigor of computational biology. Layer A (Control Plane) establishes a governed supervisor--worker hierarchy that enforces role-based tool isolation, limits execution to constrained action spaces, and drives reflection-based replanning. Layer B (Workflow Plane) operationalizes canonical drug discovery stages -- from Target Identification to Lead Optimization -- as stateful, composable skill graphs. This layer integrates strict data contracts and strategic human-in-the-loop (HITL) checkpoints to safeguard scientific validity at high-uncertainty decision boundaries. Operating on the design principle of ``free-form reasoning for safe tasks, structured execution for long-horizon pipelines,'' Mozi provides built-in robustness mechanisms and trace-level audibility to completely mitigate error accumulation. We evaluate Mozi on PharmaBench, a curated benchmark for biomedical agents, demonstrating superior orchestration accuracy over existing baselines. Furthermore, through end-to-end therapeutic case studies, we demonstrate Mozi's ability to navigate massive chemical spaces, enforce stringent toxicity filters, and generate highly competitive in silico candidates, effectively transforming the LLM from a fragile conversationalist into a reliable, governed co-scientist. Robustness (evolution) ; Flexibility (engineering) ; Hierarchy ; Drug discovery ; Action (physics) ; Identification (biology) ; Generative grammar ; Orchestration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Sai Govind Hari Kumar | Yunheng Zou | Andrew Wang | Jesús Valdés-Hernández | Tsz Wai Ko | Nathan Yue | Olivia Leng | Hanyong Xu | Chris Crebolder | Alán Aspuru-Guzik | Varinia Bernales
Source: ArXiv.org (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

El Agente Sólido: A New Age(nt) for Solid State Simulations Quantum chemistry calculations are a key component of the materials discovery process. The results from first-principles explorations enable the prediction of material properties prior to experimental validation. Despite their impact, the practical use of first-principles methods remains limited by the expertise required to design, execute, and troubleshoot complex computational workflows. Even when workflows are successfully built, they are sometimes rigid and not adaptable to different use cases. Recent advances in large language models (LLMs) and agentic systems offer a pathway to flexibly automate these processes and lower barriers to entry. Here, we introduce El Agente Sólido, a hierarchical multi-agent framework for automating solid-state quantum chemistry workflows using the open-source Quantum ESPRESSO simulation package. The framework translates high-level scientific objectives expressed in natural language into end-to-end computational pipelines that include structure generation, input file construction, workflow execution, and post-processing analysis. El Agente Sólido integrates density functional theory with phonon calculations and machine-learning interatomic potentials to enable efficient and physically consistent simulations. Extensive benchmarking and case studies demonstrate that El Agente Sólido reliably executes a wide range of solid-state calculations, highlighting its potential to improve reproducibility and accelerate computational materials discovery Workflow ; Troubleshooting ; Key (lock) ; Quantum ; Benchmarking ; Range (aeronautics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Harshitha Menon | Charles F. Jekel | Kevin Korner | Brian Gunnarson | Nathan Brown | Michael Stees | M. Giselle Fernandez-Godino | Walter Nissen | Meir H. Shachar | Dane M. Sterbentz | William J. Schill | Yue Hao | Robert N. Rieben | William Roshan Quadros | Steve Owen | Scott Mitchell | Ismael D. Boureima | Jonathan L. Belof
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems Today's scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-impact scientific discovery, we need to scale up our ability to test hypotheses, generate results, and learn from them rapidly. We present MADA (Multi-Agent Design Assistant), a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, a Geometry Agent (GA) generates meshes, and an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. While general purpose, we focus development and validation on Richtmyer--Meshkov Instability (RMI) suppression, a critical challenge in Inertial Confinement Fusion. We evaluate on two complementary settings: running a hydrodynamics simulations on HPC systems, and using a pre-trained machine learning surrogate for rapid design exploration. Our results demonstrate that the MADA system successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. More broadly, it demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery. Workflow ; Iterative design ; Focus (optics) ; Design tool ; Systems design ; Scale (ratio) ; Coupling (piping) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Sai Govind Hari Kumar | Yunheng Zou | Andrew Wang | Jesús Valdés-Hernández | Tsz Wai Ko | Nathan Yue | Olivia Leng | Hanyong Xu | Chris Crebolder | Alán Aspuru-Guzik | Varinia Bernales
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

El Agente Sólido: A New Age(nt) for Solid State Simulations Quantum chemistry calculations are a key component of the materials discovery process. The results from first-principles explorations enable the prediction of material properties prior to experimental validation. Despite their impact, the practical use of first-principles methods remains limited by the expertise required to design, execute, and troubleshoot complex computational workflows. Even when workflows are successfully built, they are sometimes rigid and not adaptable to different use cases. Recent advances in large language models (LLMs) and agentic systems offer a pathway to flexibly automate these processes and lower barriers to entry. Here, we introduce El Agente Sólido, a hierarchical multi-agent framework for automating solid-state quantum chemistry workflows using the open-source Quantum ESPRESSO simulation package. The framework translates high-level scientific objectives expressed in natural language into end-to-end computational pipelines that include structure generation, input file construction, workflow execution, and post-processing analysis. El Agente Sólido integrates density functional theory with phonon calculations and machine-learning interatomic potentials to enable efficient and physically consistent simulations. Extensive benchmarking and case studies demonstrate that El Agente Sólido reliably executes a wide range of solid-state calculations, highlighting its potential to improve reproducibility and accelerate computational materials discovery Workflow ; Troubleshooting ; Key (lock) ; Quantum ; Benchmarking ; Range (aeronautics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Dinh Long Huynh | Srijit Seal | Dylan Reid | Anne E Carpenter | Andreas Bender | Ola Spjuth | M. Chelbi | Arijit Patra | Sara Khosravi | Ankur Kumar | Mattson Thieme | Isaac Wilks | Mark Davies | Filippo Abbondanza | Jessica Mustali | Yannick Sun | Nick Edwards | Julie E. Penzotti | Daniil Boiko | Andrei S. Tyrin | Douglas W. Selinger | Ayaan Parikh | Rahul Vijayan | Shoman Kasbekar
Source: Drug Discovery Today (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

AI agents in drug discovery: applications and case studies AI agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act and learn through complicated research workflows. Building on large language models and specialized tools, these systems can integrate biomedical data, execute tasks, conduct experiments and iteratively refine hypotheses. We provide a conceptual overview of agentic AI architectures and illustrate their applications across key stages of drug discovery, including literature synthesis, automated protocol generation, toxicity prediction, small-molecule synthesis, drug repurposing and end-to-end decision-making. Early implementations demonstrate substantial gains in speed, reproducibility and scalability. We discuss the challenges related to data heterogeneity, system reliability, privacy, benchmarking and outline future directions toward technology in support of science and translation. Implementation ; Workflow ; Repurposing ; Transformative learning ; Benchmarking ; Key (lock) ; Drug repositioning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Maxwell J. Jacobson | Daniel Xie | Jackson Shen | Adil Wazeer | Haiyan Wang | Xinghang Zhang | Yexiang Xue
Source: arXiv (Cornell University) (preprint, 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: Yang Tan | Lingrong Zhang | Mingchen Li | Yuanxi Yu | Bozitao Zhong | Bingxin Zhou | Nanqing Dong | Liang Hong
Source: arXiv (Cornell University) (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Self-evolving AI agents for protein discovery and directed evolution Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt. Workflow ; Orchestration ; Domain (mathematical analysis) ; Set (abstract data type) ; Drug discovery ; Directed evolution ; Scientific discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Yang Tan | Lingrong Zhang | Mingchen Li | Yuanxi Yu | Bozitao Zhong | Bingxin Zhou | Nanqing Dong | Liang Hong
Source: arXiv (Cornell University) (preprint, 2026-03-28)
Field: Materials Science — Materials Chemistry

Self-evolving AI agents for protein discovery and directed evolution Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt. Workflow ; Orchestration ; Domain (mathematical analysis) ; Set (abstract data type) ; Drug discovery ; Directed evolution ; Scientific discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Chenmu Zhang | Boris I. Yakobson
Source: ArXiv.org (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration Existing LLM agents for computational materials science are constrained by pipeline-bounded architectures tied to specific simulation codes and by dependence on manually written tool functions that grow with task scope. We present MatClaw, a code-first agent that writes and executes Python directly, composing any installed domain library to orchestrate multi-code workflows on remote HPC clusters without predefined tool functions. To sustain coherent execution across multi-day workflows, MatClaw uses a four-layer memory architecture that prevents progressive context loss, and retrieval-augmented generation over domain source code that raises per-step API-call accuracy to ${\sim}$99 %. Three end-to-end demonstrations on ferroelectric CuInP2S6 (machine-learning force field training via active learning, Curie temperature prediction, and heuristic parameter-space search) reveal that the agent handles code generation reliably but struggles with tacit domain knowledge. The missing knowledge, such as appropriate simulation timescales, equilibration protocols, and sampling strategies, is the kind that researchers accumulate through experience but rarely formalize. Two lightweight interventions, literature self-learning and expert-specified constraints, bridge these gaps, defining a guided autonomy model in which the researcher provides high-level domain knowledge while the agent handles workflow execution. Our results demonstrate that the gap between guided and fully autonomous computational materials research is narrower than ever before: LLMs already handle code generation and scientific interpretation reliably, and the rapid improvement in their capabilities will accelerate materials discovery beyond what manual workflows can achieve. All code and benchmarks are open-source. Workflow ; Code generation ; Domain (mathematical analysis) ; Python (programming language) ; Context (archaeology) ; Code (set theory) ; Source code ; Field (mathematics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electronic and Structural Properties of Oxides