jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhan Li | Florence Cloutier | Sifan Wu | Ali Parviz | B. A. Knyazev | Yan Zhang | Glen Berseth | Bang Liu
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints Generating molecules that satisfy precise numeric constraints over multiple physicochemical properties is critical and challenging. Although large language models (LLMs) are expressive, they struggle with precise multi-objective control and numeric reasoning without external structure and feedback. We introduce \textbf{M olGen}, a fragment-level, retrieval-augmented, two-stage framework for molecule generation under multi-property constraints. Stage I : Prototype generation: a multi-agent reasoner performs retrieval-anchored, fragment-level edits to produce a candidate near the feasible region. Stage II : RL-based fine-grained optimization: a fragment-level optimizer trained with Group Relative Policy Optimization (GRPO) applies one- or multi-hop refinements to explicitly minimize the property errors toward our target while regulating edit complexity and deviation from the prototype. A large, automatically curated dataset with reasoning chains of fragment edits and measured property deltas underpins both stages, enabling deterministic, reproducible supervision and controllable multi-hop reasoning. Unlike prior work, our framework better reasons about molecules by leveraging fragments and supports controllable refinement toward numeric targets. Experiments on generation under two sets of property constraints (QED, LogP, Molecular Weight and HOMO, LUMO) show consistent gains in validity and precise satisfaction of multi-property targets, outperforming strong LLMs and graph-based algorithms. Semantic reasoner ; Property (philosophy) ; Fragment (logic) ; Control (management) ; Automated reasoning ; Chain (unit) ; Measure (data warehouse) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Xu Hu | Letian Chen | Hao Wang | Xu Zhang | Zhen Zhou
Source: Current Opinion in Electrochemistry (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Accelerating Discovery in Computational Electrocatalysis: From Machine Learning Potentials to AI-Driven Workflows Workflow ; Focus (optics) ; Automation ; Quantum ; Convergence (economics) ; Computational model Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Chun Huang
Source: arXiv (Cornell University) (preprint, 2026-03-30)
Field: Materials Science — Materials Chemistry

AI Cosplaying as Astrophysicists: A Controlled Synthetic-Agent Study of AI-Assisted Astrophysical Research Workflows Large Language Models (LLMs) are now widely used in astrophysics, but do they actually make our lives easier, or do they merely invent new physics with enough confidence to hide a minus sign? In a specialized field where checking fluent hallucinations is itself labor-intensive, AI assistance can demand as much work as the task it claims to simplify. To evaluate where AI genuinely improves scientific workflows, we bypassed human trials and instead forced AI agents to cosplay as astrophysicists. We simulated 144 synthetic researchers, varying in career stage, AI awareness, and willingness to verify outputs, across 2,592 daily astrophysics research assignments. Comparing solo work against four styles of AI assistance produced 12,960 scored episodes. No assisted policy universally outperformed unassisted work in the primary Qwen production run. Instead, performance depends strongly on the task, the style of AI use, and the identity of the actor. While cautious assistance helps on creative, extractive, and critique-oriented tasks, it can fail catastrophically on derivation-heavy physics. A full actor-swap DeepSeek rerun changes that picture materially: verification-heavy use becomes the strongest assisted policy, two assisted modes enter the higher-utility/lower-risk quadrant, and the derivation-heavy fragility that dominates the Qwen production run largely disappears. In its current form, AI is useful, but only conditionally, its value is uneven, task-specific, and shaped jointly by workflow, usage policy, and which LLM you are using. Workflow ; Task (project management) ; Field (mathematics) ; Work (physics) ; Production (economics) ; Style (visual arts) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Machine Learning and Data Classification
Authors: Sukriti Manna | Henry Chan | Subramanian K. R. S. Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2026-03-22)
Field: Materials Science — Materials Chemistry

AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation Multiphysics simulation frameworks such as MOOSE provide rigorous engines for phase-field materials modeling, yet adoption is constrained by the expertise required to construct valid input files, coordinate parameter sweeps, diagnose failures, and extract quantitative results. We introduce AutoMOOSE, an open-source agentic framework that orchestrates the full simulation lifecycle from a single natural-language prompt. AutoMOOSE deploys a five-agent pipeline in which the Input Writer coordinates six sub-agents and the Reviewer autonomously corrects runtime failures without user intervention. A modular plugin architecture enables new phase-field formulations without modifying the core framework, and a Model Context Protocol (MCP) server exposes the workflow as ten structured tools for interoperability with any MCP-compatible client. Validated on a four-temperature copper grain growth benchmark, AutoMOOSE generates MOOSE input files with 6 of 12 structural blocks matching a human expert reference exactly and 4 functionally equivalent, executes all runs in parallel with a 1.8x speedup, and performs an end-to-end physical consistency check spanning intent, finite-element execution, and Arrhenius kinetics with no human verification. Grain coarsening kinetics are recovered with R^2 = 0.90-0.95 at T &gt;= 600 K; the recovered activation energy Q_fit = 0.296 eV is consistent with a human-written reference (Q_fit = 0.267 eV) under identical parameters. Three runtime failure classes were diagnosed and resolved autonomously within a single correction cycle, and every run produces a provenance record satisfying FAIR data principles. These results show that the gap between knowing the physics and executing a validated simulation campaign can be bridged by a lightweight multi-agent orchestration layer, providing a pathway toward AI-driven materials discovery and self-driving laboratories. Workflow ; Interoperability ; Context (archaeology) ; Modular design ; Pipeline (software) ; Consistency (knowledge bases) ; Orchestration ; Construct (python library) ; Flexibility (engineering) ; Plug-in Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Sukriti Manna | Henry Chan | Subramanian K. R. S. Sankaranarayanan
Source: ArXiv.org (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation Multiphysics simulation frameworks such as MOOSE provide rigorous engines for phase-field materials modeling, yet adoption is constrained by the expertise required to construct valid input files, coordinate parameter sweeps, diagnose failures, and extract quantitative results. We introduce AutoMOOSE, an open-source agentic framework that orchestrates the full simulation lifecycle from a single natural-language prompt. AutoMOOSE deploys a five-agent pipeline in which the Input Writer coordinates six sub-agents and the Reviewer autonomously corrects runtime failures without user intervention. A modular plugin architecture enables new phase-field formulations without modifying the core framework, and a Model Context Protocol (MCP) server exposes the workflow as ten structured tools for interoperability with any MCP-compatible client. Validated on a four-temperature copper grain growth benchmark, AutoMOOSE generates MOOSE input files with 6 of 12 structural blocks matching a human expert reference exactly and 4 functionally equivalent, executes all runs in parallel with a 1.8x speedup, and performs an end-to-end physical consistency check spanning intent, finite-element execution, and Arrhenius kinetics with no human verification. Grain coarsening kinetics are recovered with R^2 = 0.90-0.95 at T >= 600 K; the recovered activation energy Q_fit = 0.296 eV is consistent with a human-written reference (Q_fit = 0.267 eV) under identical parameters. Three runtime failure classes were diagnosed and resolved autonomously within a single correction cycle, and every run produces a provenance record satisfying FAIR data principles. These results show that the gap between knowing the physics and executing a validated simulation campaign can be bridged by a lightweight multi-agent orchestration layer, providing a pathway toward AI-driven materials discovery and self-driving laboratories. Workflow ; Interoperability ; Context (archaeology) ; Modular design ; Pipeline (software) ; Consistency (knowledge bases) ; Orchestration ; Construct (python library) ; Flexibility (engineering) ; Plug-in Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Pengbo Cao | Qiang Xie | Xiaoyu Yang | Bingjie Wang | Shangbo Wang | Hongyang Zhou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Specific Capacitance in Porous Carbons via Large Language Model-Driven Literature Mining and Machine Learning Workflow ; Random forest ; Reliability (semiconductor) ; SPARK (programming language) ; Benchmark (surveying) ; Ambiguity ; Heuristic ; Volume (thermodynamics) Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Yizhan Li | Florence Cloutier | Sifan Wu | Ali Parviz | B. A. Knyazev | Yan Zhang | Glen Berseth | Bang Liu
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints Generating molecules that satisfy precise numeric constraints over multiple physicochemical properties is critical and challenging. Although large language models (LLMs) are expressive, they struggle with precise multi-objective control and numeric reasoning without external structure and feedback. We introduce \textbf{M olGen}, a fragment-level, retrieval-augmented, two-stage framework for molecule generation under multi-property constraints. Stage I : Prototype generation: a multi-agent reasoner performs retrieval-anchored, fragment-level edits to produce a candidate near the feasible region. Stage II : RL-based fine-grained optimization: a fragment-level optimizer trained with Group Relative Policy Optimization (GRPO) applies one- or multi-hop refinements to explicitly minimize the property errors toward our target while regulating edit complexity and deviation from the prototype. A large, automatically curated dataset with reasoning chains of fragment edits and measured property deltas underpins both stages, enabling deterministic, reproducible supervision and controllable multi-hop reasoning. Unlike prior work, our framework better reasons about molecules by leveraging fragments and supports controllable refinement toward numeric targets. Experiments on generation under two sets of property constraints (QED, LogP, Molecular Weight and HOMO, LUMO) show consistent gains in validity and precise satisfaction of multi-property targets, outperforming strong LLMs and graph-based algorithms. Semantic reasoner ; Property (philosophy) ; Fragment (logic) ; Control (management) ; Chain (unit) ; Automated reasoning ; Measure (data warehouse) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Mingxi Jiang | Jie Zhou | Yanggang An | Zhengran Lin | Menghao Yang
Source: ENGINEERING Energy (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence for energy materials research: From classical machine learning to large models Abstract With the global energy system transitioning to renewable energy, high-efficiency energy storage and conversion technologies have become crucial. However, traditional research paradigms for the research and development (R&amp;D) of energy materials such as batteries and electrocatalysts present the limitations in efficiency. This review systematically summarizes the progress of artificial intelligent (AI) in this field, ranging from classical machine learning (ML) to advanced representation methods such as graph neural networks (GNNs) and transformers that enable precise property prediction and structure generation. It also covers generative models for inverse design and large language models (LLMs) for knowledge extraction, along with key domain databases. Current challenges include limited interpretability and the underutilization of emerging AI technologies. Finally, this review discusses future directions such as the applications of multimodal language models, aiming to provide insights for accelerating high-performance energy materials innovation and advancing the global renewable energy transition. Interpretability ; Applications of artificial intelligence ; Generative grammar ; Renewable energy ; Artificial neural network ; Domain (mathematical analysis) ; Field (mathematics) ; Transformer Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Big Data and Digital Economy
Authors: Dianyuan Li | Xichen Sun | Shaohua Sun | Runzhou Wang | Miaomiao Zhang | Meng Xiao | Yue Wang | Yuezhou Zhang
Source: Advanced Science (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Prompt Engineering Accelerates the Data‐Driven Discovery of Photocatalysts via an LLM‐Based Model Ensemble Strategy The overwhelming volume of unstructured scientific literature presents a fundamental bottleneck to materials discovery, where critical data on synthesis and properties remain locked in text. Here, a closed-loop framework that integrates automated knowledge extraction with interpretable machine learning and targeted experimental validation is presented. This approach is centered on a novel data extraction pipeline, which combines a prompt-engineered large language model with a model ensemble strategy, systematically optimized to interpret complex materials science narratives. When deployed to construct a database for defect-engineered carbon nitride photocatalysts, the system achieved 90% accuracy and recall for key parameters. Analysis of the high-fidelity dataset enabled reliable machine learning models to identify specific surface area (170 m<sup>2</sup> g<sup>-1</sup>) and bandgap (≈2.31 eV) as dominant performance parameters. Crucially, SHapley Additive exPlanations analysis elucidated a non-monotonic relationship for bandgap, identifying an optimal range of 2.2-2.4 eV and quantifying the fundamental trade-off between light absorption and charge recombination. These data-driven insights guided the synthesis of representative materials, with experimental hydrogen evolution rates deviating by less than 5% from predictions. This work establishes a scalable and transferable paradigm, transforming fragmented literature into actionable intelligence and offering a powerful strategy for accelerating the development of functional materials. Bottleneck ; Scalability ; Key (lock) ; Ensemble learning ; Big data ; Range (aeronautics) ; Construct (python library) ; Execution model Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Sajid Mannan | Rupert J. Myers | Rohit Batra | Rocio Mercado | Lothar Wondraczek | N. M. Anoop Krishnan
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Sustainable Materials Discovery in the Era of Artificial Intelligence Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current AI workflows optimize performance first, deferring sustainability to post synthesis assessment. This creates inefficiency by the time environmental burdens are quantified, resources have been invested in potentially unsustainable solutions. The disconnect between atomic scale design and lifecycle assessment (LCA) reflects fundamental challenges, data scarcity across heterogeneous sources, scale gaps from atoms to industrial systems, uncertainty in synthesis pathways, and the absence of frameworks that co-optimize performance with environmental impact. We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment. The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning glass, cement, semiconductor photoresists, and polymers demonstrate both necessity and feasibility while identifying material-specific integration challenges. Realizing ML-LCA demands coordinated advances in data infrastructure, ex-ante assessment methodologies, multi-objective optimization, and regulatory alignment enabling the discovery of materials that are sustainable by design rather than by chance. Sustainability ; Workflow ; Scale (ratio) ; Upstream (networking) ; Big data ; Bridging (networking) ; Scarcity ; Knowledge extraction Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Catalysis and Oxidation Reactions
Authors: Jing Ping | Zhendong Liu
Source: ChemRxiv (article, 2026-04-07)
Field: Chemistry — Inorganic Chemistry

ZeoAgent: An LLM-Driven Agent for Automated Zeolite Design and Property Prediction Artificial intelligence is transforming scientific research, yet most materials discovery approaches remain limited to isolated tasks.A key next step is therefore to develop agentic systems that can autonomously coordinate these tasks within iterative scientific workflows.Despite their broad industrial importance, the discovery of new zeolite frameworks remains highly challenging because of the lack of efficient discovery workflows and rational design strategies.Here we present ZeoAgent, a large language-model-driven agent system that autonomously coordinates zeolite structure analysis, property prediction, and framework design through natural-language interaction.Equipped with a suite of computational tools, ZeoAgent performs structure analysis and property prediction with results consistent with manual calculations.To enable autonomous, goal-directed design, ZeoAgent further incorporates a newly developed point-cloud-based generative method that converts natural-language objectives into physically plausible framework candidates by learning from existing structures.Representative case studies demonstrate ZeoAgent's ability to address three increasingly challenging design tasks, including generating frameworks under user defined structural constraints, modifying existing topologies to enhance transport performance, and designing frameworks for CO2/CH4 separation.This work provides a promising strategy for computational zeolite research and establishes a practical foundation for goal-directed, LLM-assisted zeolite design. Property (philosophy) ; Zeolite ; Multi-agent system Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; CO2 Sequestration and Geologic Interactions
Authors: Guangyao Chen | Wenhao Yuan | Fengqi You
Source: Science Advances (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Bridging electron microscopy and materials analysis with an autonomous agentic platform Electron microscopy (EM) reveals atomic-scale structures that underpin catalysis, energy storage, and semiconductor reliability, yet current workflows remain fragmented across segmentation, crystallographic reconstruction, property modeling, and literature review, often requiring weeks of expert effort. Although recent artificial intelligence models have assisted individual steps, the diversity of EM modalities and tasks means existing approaches remain siloed and perform poorly in complex multistage workflows. We present EMSeek, a modular, provenance-tracked multiagent platform that connects EM to materials insight through five key units: reference-guided one-for-all segmentation, mask-aware reconstruction of crystal structures from EM data, a gated mixture of experts property predictor with uncertainty calibration, literature retrieval with citation anchoring, and physical consistency checks with audit-ready reporting. These units are orchestrated by large language models (LLMs) that automatically plan, invoke, and execute tools, minimizing human intervention. On 20 material systems and five tasks, EMSeek delivers segmentation about twice as fast as Segment Anything with higher accuracy, achieves more than 90% structural similarity on STEM2Mat, and, with about 2% labeled calibration, matches or surpasses strong single experts on three out-of-distribution property benchmarks. A complete query runs in 2 to 5 minutes per image, roughly 50 times faster than expert workflows. Case studies on two-dimensional lattices and nanoparticles validate EMSeek's ability to automate complex workflows, with integrated uncertainty calibration and audit signals that provide scientists with rigorous yet actionable guidance to accelerate materials discovery. Workflow ; Bridging (networking) ; Consistency (knowledge bases) ; Property (philosophy) ; Key (lock) ; Segmentation ; Calibration ; Modalities Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques