jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haonan Huang
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguishes research from routine execution is the progressive accumulation of knowledge -- learning which approaches fail, recognizing patterns across systems, and applying understanding to new problems. However, the prevailing paradigm in AI-driven computational science treats each execution in isolation, largely discarding hard-won insights between runs. Here we present QMatSuite, an open-source platform closing this gap. Agents record findings with full provenance, retrieve knowledge before new calculations, and in dedicated reflection sessions correct erroneous findings and synthesize observations into cross-compound patterns. In benchmarks on a six-step quantum-mechanical simulation workflow, accumulated knowledge reduces reasoning overhead by 67% and improves accuracy from 47% to 3% deviation from literature -- and when transferred to an unfamiliar material, achieves 1% deviation with zero pipeline failures. Pipeline (software) ; Reflection (computer programming) ; Theory of computation ; Closing (real estate) ; Computational model ; Consolidation (business) ; Overhead (engineering) ; Knowledge integration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Yuyang Hong | Xin Mao
Source: AI Agent (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

DIVE-to-design: how a multi-agent workflow converts figure-centric literature into an ai-native hydrogen storage discovery engine A major bottleneck in artificial intelligence (AI)-driven materials discovery is not model architecture, but limited data accessibility: critical experimental knowledge remains locked in figures, heterogeneous reporting formats, and unstructured PDFs. A recent study by Li et al. addresses this challenge by introducing DIVE (Descriptive Interpretation of Visual Expression), a multi-agent extraction framework that transforms figure-centric scientific content into structured, machine-actionable data. Applied to solid-state hydrogen storage materials, DIVE demonstrates substantial extraction gains over conventional direct large language model (LLM) parsing, then scales to mine 4,053 publications (1972-2025) and build a &gt; 30,000-entry database that powers a downstream inverse-design agent, DigHyd. This work offers a practical blueprint for moving from “LLM-assisted reading” to “AI-enabled discovery infrastructure”, linking literature mining, quality scoring, database construction, and target-driven candidate generation in a single workflow. Workflow ; Bottleneck ; Knowledge extraction ; Blueprint ; Information extraction ; Data extraction ; Workflow engine ; Work (physics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Thang Duc Pham | Harikrishna Tummalapalli | Fakhrul H. Bhuiyan | Álvaro Vázquez Mayagoitia | Christine M. Simpson | Riccardo Balin | Venkatram Vishwanath | Murat Keçeli
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System The integration of Artificial Intelligence (AI) with High-Performance Computing (HPC) is transforming scientific workflows from human-directed pipelines into adaptive systems capable of autonomous decision-making. Large language models (LLMs) play a critical role in autonomous workflows; however, deploying LLM-based agents at scale remains a significant challenge. Single-agent architectures and sequential tool calls often become serialization bottlenecks when executing large-scale simulation campaigns, failing to utilize the massive parallelism of exascale resources. To address this, we present a scalable, hierarchical multi-agent framework for orchestrating high-throughput screening campaigns. Our planner-executor architecture employs a central planning agent to dynamically partition workloads and assign subtasks to a swarm of parallel executor agents. All executor agents interface with a shared Model Context Protocol (MCP) server that orchestrates tasks via the Parsl workflow engine. To demonstrate this framework, we employed the open-weight gpt-oss-120b model to orchestrate a high-throughput screening of the Computation-Ready Experimental (CoRE) Metal-Organic Framework (MOF) database for atmospheric water harvesting. The results demonstrate that the proposed agentic framework enables efficient and scalable execution on the Aurora supercomputer, with low orchestration overhead and high task completion rates. This work establishes a flexible paradigm for LLM-driven scientific automation on HPC systems, with broad applicability to materials discovery and beyond. Executor ; Orchestration ; Workflow ; Scalability ; Context (archaeology) ; Execution model ; Serialization ; Cloud computing ; Overhead (engineering) ; Automation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Distributed and Parallel Computing Systems
Authors: Thang Duc Pham | Harikrishna Tummalapalli | Fakhrul H. Bhuiyan | Álvaro Vázquez Mayagoitia | Christine M. Simpson | Riccardo Balin | Venkatram Vishwanath | Murat Keçeli
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System The integration of Artificial Intelligence (AI) with High-Performance Computing (HPC) is transforming scientific workflows from human-directed pipelines into adaptive systems capable of autonomous decision-making. Large language models (LLMs) play a critical role in autonomous workflows; however, deploying LLM-based agents at scale remains a significant challenge. Single-agent architectures and sequential tool calls often become serialization bottlenecks when executing large-scale simulation campaigns, failing to utilize the massive parallelism of exascale resources. To address this, we present a scalable, hierarchical multi-agent framework for orchestrating high-throughput screening campaigns. Our planner-executor architecture employs a central planning agent to dynamically partition workloads and assign subtasks to a swarm of parallel executor agents. All executor agents interface with a shared Model Context Protocol (MCP) server that orchestrates tasks via the Parsl workflow engine. To demonstrate this framework, we employed the open-weight gpt-oss-120b model to orchestrate a high-throughput screening of the Computation-Ready Experimental (CoRE) Metal-Organic Framework (MOF) database for atmospheric water harvesting. The results demonstrate that the proposed agentic framework enables efficient and scalable execution on the Aurora supercomputer, with low orchestration overhead and high task completion rates. This work establishes a flexible paradigm for LLM-driven scientific automation on HPC systems, with broad applicability to materials discovery and beyond. Executor ; Orchestration ; Workflow ; Scalability ; Context (archaeology) ; Execution model ; Serialization ; Cloud computing ; Overhead (engineering) ; Automation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Distributed and Parallel Computing Systems
Authors: Chenmu Zhang | Boris I. Yakobson
Source: arXiv (Cornell University) (preprint, 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
Authors: Yue Wu | Tianhao Su | Shunbo Hu | Deng Pan
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Skill-Based Autonomous Agents for Material Creep Database Construction The advancement of data-driven materials science is currently constrained by a fundamental bottleneck: the vast majority of historical experimental data remains locked within the unstructured text and rasterized figures of legacy scientific literature. Manual curation of this knowledge is prohibitively labor-intensive and prone to human error. To address this challenge, we introduce an autonomous, agent-based framework powered by Large Language Models (LLMs) designed to excavate high-fidelity datasets from scientific PDFs without human intervention. By deploying a modular "skill-based" architecture, the agent orchestrates complex cognitive tasks - including semantic filtering, multi-modal information extraction, and physics-informed validation. We demonstrate the efficacy of this framework by constructing a physically self-consistent database for material creep mechanics, a domain characterized by complex graphical trajectories and heterogeneous constitutive models. Applying the pipeline to 243 publications, the agent achieved a verified extraction success rate exceeding 90% for graphical data digitization. Crucially, we introduce a cross-modal verification protocol, demonstrating that the agent can autonomously align visually extracted data points with textually extracted constitutive parameters ($R^2 > 0.99$), ensuring the physical self-consistency of the database. This work not only provides a critical resource for investigating time-dependent deformation across diverse material systems but also establishes a scalable paradigm for autonomous knowledge acquisition, paving the way for the next generation of self-driving laboratories. Pipeline (software) ; Scalability ; Domain (mathematical analysis) ; Modular design ; Visualization ; Information extraction ; Resource (disambiguation) ; Vulnerability (computing) ; Data modeling Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Ju Li | C.-W. Hsu | So Yeon Kim | Zhen Zhang | Zongyou Yin | Zhichu Ren | Ruoyu Yin
Source: AI for Materials (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Collaborative AI Enhances Image Understanding in Materials Science The Copilot for Real-world Experimental Scientist (CRESt) system empowers researchers to control autonomous laboratories through conversational AI, providing a seamless interface for managing complex experimental workflows. We have enhanced CRESt by integrating a multi-agent collaboration mechanism that utilizes the complementary strengths of the ChatGPT and Gemini models for precise image analysis in materials science. This innovative approach significantly improves the accuracy of experimental outcomes by fostering structured debates between the AI models, which enhances decision-making processes in materials phase analysis. Additionally, to evaluate the generalizability of this approach, we tested it on a quantitative task of counting particles. Here, the collaboration between the AI models also led to improved results, demonstrating the versatility and robustness of this method. By harnessing this dual-AI framework, this approach stands as a pioneering method for enhancing experimental accuracy and efficiency in materials research, with applications extending beyond CRESt to broader scientific experimentation and analysis. Generalizability theory ; Robustness (evolution) ; Experimental science ; Task (project management) ; Interface (matter) ; Control (management) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhenyuan Zhao | Yu Xing | Tianyang Xue | Lingxin Cao | Xin Yan | Lin Lü
Source: arXiv (Cornell University) (preprint, 2026-03-28)
Field: Materials Science — Materials Chemistry

AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This inverse design problem involves a vast, discontinuous search space where traditional topology optimization is computationally prohibitive, and deep generative models often suffer from "physical hallucinations," lacking the capability to ensure rigorous validity. To address this limitation, we introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evolutionary search. Unlike methods that treat LLMs merely as interfaces, AutoMS integrates them as "semantic navigators" to initialize search spaces and break local optima, while our novel Simulation-Aware Evolutionary Search (SAES) addresses the "blindness" of traditional evolutionary strategies. Specifically, SAES utilizes simulation feedback to perform local gradient approximation and directed parameter updates, effectively guiding the search toward physically valid Pareto frontiers. Orchestrating specialized agents (Manager, Parser, Generator, and Simulator), AutoMS achieves a state-of-the-art 83.8\% success rate on 17 diverse cross-physics tasks, nearly doubling the performance of traditional NSGA-II (43.7\%) and significantly outperforming ReAct-based LLM baselines (53.3\%). Furthermore, our hierarchical architecture reduces total execution time by 23.3\%. AutoMS demonstrates that autonomous agent systems can effectively navigate complex physical landscapes, bridging the gap between semantic design intent and rigorous physical validity. Evolutionary algorithm ; Bridging (networking) ; Multi-objective optimization ; Pareto principle ; Inverse ; Evolutionary computation ; Generative grammar ; Local search (optimization) Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Multi-Objective Optimization Algorithms
Authors: Frazier N. Baker | Trieu Nguyen | Reza Averly | Botao Yu | Daniel Adu-Ampratwum | Huan Sun | Xia Ning
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (MAS) offer a promising approach for this task: leveraging interactions of specialized agents to incorporate multiple objectives into retrosynthesis planning. We present MMORF, a framework for constructing MAS for multi-objective retrosynthesis planning. MMORF features modular agentic components, which can be flexibly combined and configured into different systems, enabling principled evaluation and comparison of different system designs. Using MMORF, we construct two representative MAS: MASIL and RFAS. On a newly curated benchmark consisting of 218 multi-objective retrosynthesis planning tasks, MASIL achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, while RFAS achieves a 48.6% success rate on hard-constraint tasks, outperforming state-of-the-art baselines. Together, these results show the effectiveness of MMORF as a foundational framework for exploring MAS for multi-objective retrosynthesis planning. Code and data are available at https://anonymous.4open.science/r/MMORF/. Retrosynthetic analysis ; Construct (python library) ; Benchmark (surveying) ; Modular design ; Baseline (sea) ; Task (project management) ; Plan (archaeology) Machine Learning in Materials Science ; Process Optimization and Integration ; Constraint Satisfaction and Optimization
Authors: Yue Wu | Tianhao Su | Shunbo Hu | Deng Pan
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

Skill-Based Autonomous Agents for Material Creep Database Construction The advancement of data-driven materials science is currently constrained by a fundamental bottleneck: the vast majority of historical experimental data remains locked within the unstructured text and rasterized figures of legacy scientific literature. Manual curation of this knowledge is prohibitively labor-intensive and prone to human error. To address this challenge, we introduce an autonomous, agent-based framework powered by Large Language Models (LLMs) designed to excavate high-fidelity datasets from scientific PDFs without human intervention. By deploying a modular "skill-based" architecture, the agent orchestrates complex cognitive tasks - including semantic filtering, multi-modal information extraction, and physics-informed validation. We demonstrate the efficacy of this framework by constructing a physically self-consistent database for material creep mechanics, a domain characterized by complex graphical trajectories and heterogeneous constitutive models. Applying the pipeline to 243 publications, the agent achieved a verified extraction success rate exceeding 90% for graphical data digitization. Crucially, we introduce a cross-modal verification protocol, demonstrating that the agent can autonomously align visually extracted data points with textually extracted constitutive parameters ($R^2 &gt; 0.99$), ensuring the physical self-consistency of the database. This work not only provides a critical resource for investigating time-dependent deformation across diverse material systems but also establishes a scalable paradigm for autonomous knowledge acquisition, paving the way for the next generation of self-driving laboratories. Pipeline (software) ; Scalability ; Domain (mathematical analysis) ; Modular design ; Visualization ; Information extraction ; Resource (disambiguation) ; Vulnerability (computing) ; Data modeling Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Yue Wua | Tianhao Su | Rui Hu | Mingchuan Zhao | Shunbo Hu | Deng Pan | Jizhong Huang
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Epistemic Closure: Autonomous Mechanism Completion for Physically Consistent Simulation The integration of Large Language Models (LLMs) into scientific discovery is currently hindered by the Implicit Context problem, where governing equations extracted from literature contain invisible thermodynamic assumptions (e.g., undrained conditions) that standard generative models fail to recognize. This leads to Physical Hallucination: the generation of syntactically correct solvers that faithfully execute physically invalid laws. Here, we introduce a Neuro-Symbolic Generative Agent that functions as a cognitive supervisor atop traditional numerical engines. By encapsulating physical laws into modular Constitutive Skills and leveraging latent intrinsic priors, the Agent employs a Chain-of-Thought reasoning workflow to autonomously validate, prune, and complete physical mechanisms. We demonstrate this capability on the challenge of thermal pressurization in low-permeability sandstone. While a standard literature-retrieval baseline erroneously predicts catastrophic material failure by blindly adopting a rigid "undrained" simplification, our Agent autonomously identifies the system as operating in a drained regime (Deborah number De &lt;&lt; 1) via dimensionless scaling analysis. Consequently, it inductively completes the missing dissipation mechanism (Darcy flow) required to satisfy boundary constraints, predicting a stable stress path consistent with experimental reality. This work establishes a paradigm where AI agents transcend the role of coding assistants to act as epistemic partners, capable of reasoning about and correcting the theoretical assumptions embedded in scientific data. Generative grammar ; Context (archaeology) ; Modular design ; Workflow ; Satisfiability modulo theories ; Physical system ; Interface (matter) ; Domain (mathematical analysis) ; Mechanism (biology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Ferroelectric and Negative Capacitance Devices