jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiangyu Chen
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language Model (LLM) agents face significant challenges in this setting, including context window limitations during long-horizon reasoning and path-dependent exploration that may lead to mode collapse. To address these issues, we introduce AI4S-SDS, a closed-loop neuro-symbolic framework that integrates multi-agent collaboration with a tailored Monte Carlo Tree Search (MCTS) engine. We propose a Sparse State Storage mechanism with Dynamic Path Reconstruction, which decouples reasoning history from context length and enables arbitrarily deep exploration under fixed token budgets. To reduce local convergence and improve coverage, we implement a Global--Local Search Strategy: a memory-driven planning module adaptively reconfigures the search root based on historical feedback, while a Sibling-Aware Expansion mechanism promotes orthogonal exploration at the node level. Furthermore, we bridge symbolic reasoning and physical feasibility through a Differentiable Physics Engine, employing a hybrid normalized loss with sparsity-inducing regularization to optimize continuous mixing ratios under thermodynamic constraints. Empirical results show that AI4S-SDS achieves full validity under the adopted HSP-based physical constraints and substantially improves exploration diversity compared to baseline agents. In preliminary lithography experiments, the framework identifies a novel photoresist developer formulation that demonstrates competitive or superior performance relative to a commercial benchmark, highlighting the potential of diversity-driven neuro-symbolic search for scientific discovery. Context (archaeology) ; Bridging (networking) ; Differentiable function ; Scalability ; Tree (set theory) ; Rotation formalisms in three dimensions ; Topology (electrical circuits) ; Combinatorial explosion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Big Data and Digital Economy
Authors: Yiyan Zhu | Shunbo Jiang | Borui Feng | Yunfan Wang | Min Fang | Haoran Sun | Wei Liu
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Dispersion-inclusive surface chemistry optimizer: An AI platform for high-precision surface science Surface (topology) ; Science, technology and society ; Surface structure ; Surface modification Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Théophile Gaudin | Alena M. Sheveleva | Marine Ciantar | Samuel P. Niblett | Sandor Farkas | João Miguel Inês | Gregory Price
Source: ChemRxiv (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

«Just ask»: Agentic Pilot to empower chemists Large Language Model agents capable of using software tools have recently surged in capability and popularity in the data science domain, but remain under-used in the physical sciences. We develop Agentic Pilot, a visual user interface built on LangGraph, to help fill this gap. Agentic Pilot enables the intuitive development of complex agentic workflows, allowing even small LLMs to perform industrially relevant tasks such as executing computational screening calculations or querying electronic lab notebook databases via natural language. In this work, we present Agentic Pilot and provide an industrial perspective on the future of LLM-based agentic workflows in chemistry. Workflow ; Perspective (graphical) ; Popularity ; Interface (matter) ; Software ; Natural (archaeology) ; Software tool Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Théophile Gaudin | Octave Malamoud | Alena M. Sheveleva | Marine Ciantar | Samuel P. Niblett | Sandor Farkas | João Miguel Inês | Gregory Price
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

«Just ask»: Agentic Pilot to empower chemists Large Language Model agents capable of using software tools have recently surged in capability and popularity in the data science domain, but remain under-used in the physical sciences. We develop Agentic Pilot, a visual user interface built on LangGraph, to help fill this gap. Agentic Pilot enables the intuitive development of complex agentic workflows, allowing even small LLMs to perform industrially relevant tasks such as executing computational screening calculations or querying electronic lab notebook databases via natural language. In this work, we present Agentic Pilot and provide an industrial perspective on the future of LLM-based agentic workflows in chemistry. Workflow ; Popularity ; Perspective (graphical) ; Software ; Interface (matter) ; Natural (archaeology) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Iman Peivaste | Ahmed Makradi | Salim Belouettar
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

ChemNavigator: Agentic AI Discovery of Design Rules for Organic Photocatalysts The discovery of high-performance organic photocatalysts for hydrogen evolution remains limited by the vastness of chemical space and the reliance on human intuition for molecular design. Here we present ChemNavigator, an agentic AI system that autonomously derives structure-property relationships through hypothesis-driven exploration of organic photocatalyst candidates. The system integrates large language model reasoning with density functional tight binding calculations in a multi-agent architecture that mirrors the scientific method: formulating hypotheses, designing experiments, executing calculations, and validating findings through rigorous statistical analysis. Through iterative discovery cycles encompassing 200 molecules, ChemNavigator autonomously identified six statistically significant design rules governing frontier orbital energies, including the effects of ether linkages, carbonyl groups, extended conjugation, cyano groups, halogen substituents, and amine groups. Importantly, these rules correspond to established principles of organic electronic structure (resonance donation, inductive withdrawal, $π$-delocalization), demonstrating that the system can independently derive chemical knowledge without explicit programming. Notably, autonomous agentic reasoning extracted these six validated rules from a molecular library where previous ML approaches identified only carbonyl effects. Furthermore, the quantified effect sizes provide a prioritized ranking for synthetic chemists, while feature interaction analysis revealed diminishing returns when combining strategies, challenging additive assumptions in molecular design. This work demonstrates that agentic AI systems can autonomously derive interpretable, chemically grounded design principles, establishing a framework for AI-assisted materials discovery that complements rather than replaces chemical intuition. Intuition ; Intuition ; Chemical space ; Chemical space ; Ranking (information retrieval) ; Ranking (information retrieval) ; Space (punctuation) ; Space (punctuation) ; Organic molecules Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Iman Peivaste | Ahmed Makradi | Salim Belouettar
Source: ArXiv.org (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

ChemNavigator: Agentic AI Discovery of Design Rules for Organic Photocatalysts The discovery of high-performance organic photocatalysts for hydrogen evolution remains limited by the vastness of chemical space and the reliance on human intuition for molecular design. Here we present ChemNavigator, an agentic AI system that autonomously derives structure-property relationships through hypothesis-driven exploration of organic photocatalyst candidates. The system integrates large language model reasoning with density functional tight binding calculations in a multi-agent architecture that mirrors the scientific method: formulating hypotheses, designing experiments, executing calculations, and validating findings through rigorous statistical analysis. Through iterative discovery cycles encompassing 200 molecules, ChemNavigator autonomously identified six statistically significant design rules governing frontier orbital energies, including the effects of ether linkages, carbonyl groups, extended conjugation, cyano groups, halogen substituents, and amine groups. Importantly, these rules correspond to established principles of organic electronic structure (resonance donation, inductive withdrawal, $π$-delocalization), demonstrating that the system can independently derive chemical knowledge without explicit programming. Notably, autonomous agentic reasoning extracted these six validated rules from a molecular library where previous ML approaches identified only carbonyl effects. Furthermore, the quantified effect sizes provide a prioritized ranking for synthetic chemists, while feature interaction analysis revealed diminishing returns when combining strategies, challenging additive assumptions in molecular design. This work demonstrates that agentic AI systems can autonomously derive interpretable, chemically grounded design principles, establishing a framework for AI-assisted materials discovery that complements rather than replaces chemical intuition. Intuition ; Chemical space ; Ranking (information retrieval) ; Space (punctuation) ; Organic molecules ; Feature (linguistics) Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Qin Zhang | Zhiyao Su | Yajing Sun
Source: SmartMat (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Harnessing Autonomous AI Agents for the Next Generation of Scientific Discovery ABSTRACT Interdisciplinary collaboration is essential for developing advanced smart materials, yet coordinating diverse expertise remains a significant bottleneck. In a recent study published in Nature , a Virtual Lab where autonomous AI agents collaborate to design functional nanobodies with minimal human intervention was introduced. This work exemplifies a transformative paradigm in AI for Science, demonstrating a versatile multi‐agent architecture that is agnostic to specific domains. Here, we discuss how this framework can be adapted to materials science, offering a blueprint for autonomous discovery in next‐generation smart materials. Blueprint ; Scientific discovery ; Transformative learning ; Autonomous agent ; Architecture ; Intervention (counseling) Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanowire Synthesis and Applications
Authors: Markus J. Buehler
Source: Nano Futures (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Physics-aware emergent cognition for discovery, materials innovation, and design through agentic modeling Abstract Multi-agent, multimodal artificial intelligence (AI) frameworks with scientific platforms represent more than an incremental advance—they mark a paradigm shift in computational research methodology, introducing unprecedented capabilities in real-time knowledge synthesis and autonomous scientific discovery. Drawing parallels with biological evolution, where simple components are optimized and recombined to create sophisticated functional structures, these AI frameworks employ analogous principles to develop autonomous problem-solving systems by predicting new connections and expanding established relationships. The emergence of higher-order capabilities from relatively simple AI building blocks enables these systems to tackle challenging problems in protein modeling, molecular mechanics, and engineering design. Physics-aware agentic models go beyond conventional data-driven inference, through their ability to self-improve, error-correct, and learn from real-time feedback, autonomously planning experiments, generating and executing code, and reasoning over results-effectively closing the loop in scientific discovery. Their inherent capacity to seamlessly integrate data-driven and physics-driven modeling approaches enables AI systems to independently formulate hypotheses, design simulations, and analyze outcomes, representing a significant advance in automated scientific investigation. Unlike prior surveys, this perspective emphasizes the emergence of higher-order cognition and reflection in multi-agent systems, framing them as autonomous collaborators rather than passive assistants. This perspective examines how this new paradigm is accelerating multidisciplinary breakthroughs in protein design, mechanics, and materials science, and explores its implications for the future of computational, theoretical and experimental research methodologies. We further highlight how agentic AI frameworks extend beyond science into domains of art and music, revealing deep isomorphisms across molecules, materials, and creative expression. Parallels ; Perspective (graphical) ; Cognition ; Framing (construction) ; Multidisciplinary approach ; Cognitive robotics ; Scientific modelling ; Cognitive systems Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Seongmin Kim | Jaehwan Choi | K. I. Jang | Junkil Park | Varinia Bernales | Alán Aspuru-Guzik | Yousung Jung
Source: ArXiv.org (article, 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: Zhaolin Hu | Zhiliang Wu | Hehe Fan | Yi Yang
Source: arXiv (Cornell University) (article, 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: Hassan Harb | Yunkai Sun | Mustafa Unal | Nicholas Chia | Zhenzhen Yang | Brian J. Ingram | Rajeev S. Assary
Source: ChemRxiv (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

The Ballad of LLM Agents: Philosophical Reasoning for Chemistry Large language models (LLMs) show remarkable potential for scientific reasoning but often produce unreliable or scientifically unactionable outputs when faced with multi-step logic, domain grounding, and interpretability challenges, especially in complex fields like chemistry and materials science. Here, we introduce a framework of philosophical reasoning agents, inspired by canonical thinkers such as Socrates, Descartes, Kant, and Hume, to guide LLM behavior via structured prompt engineering. These agents embody distinct reasoning paradigms,(dialectical inquiry, deductive logic, rule-based judgment, and empirical validation),and are evaluated across multiple chemistry subdomains (such as physical, organic, and general chemistry) using the ChemBench benchmark. Our agentic prompting approach yields substantial accuracy gains on open-ended numerical chemistry questions, with up to +18.4% for GPT-4o using the Hume agent, +14.1% for GPT-5 with Kant, and +11.7% for GPT-5.1 with Descartes, relative to zero-shot baselines. Beyond accuracy, we reveal distinct “reasoning signatures” across models, reflecting latent epistemic biases in how LLMs approach scientific problem solving. These findings demonstrate that embedding philosophy-of-science principles into multi-agent frameworks can improve and produce interpretable, adaptive, and domain-aligned scientific LLMs. Interpretability ; Scientific reasoning ; Domain (mathematical analysis) ; Deductive reasoning ; Embedding ; Analogical reasoning Machine Learning in Materials Science ; Topic Modeling ; Artificial Intelligence in Healthcare and Education
Authors: Dong Hyeon Mok | Seoin Back | Victor Fung | Guoxiang Hu
Source: Open MIND (preprint, 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: Jiangyu Chen
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language Model (LLM) agents face significant challenges in this setting, including context window limitations during long-horizon reasoning and path-dependent exploration that may lead to mode collapse. To address these issues, we introduce AI4S-SDS, a closed-loop neuro-symbolic framework that integrates multi-agent collaboration with a tailored Monte Carlo Tree Search (MCTS) engine. We propose a Sparse State Storage mechanism with Dynamic Path Reconstruction, which decouples reasoning history from context length and enables arbitrarily deep exploration under fixed token budgets. To reduce local convergence and improve coverage, we implement a Global--Local Search Strategy: a memory-driven planning module adaptively reconfigures the search root based on historical feedback, while a Sibling-Aware Expansion mechanism promotes orthogonal exploration at the node level. Furthermore, we bridge symbolic reasoning and physical feasibility through a Differentiable Physics Engine, employing a hybrid normalized loss with sparsity-inducing regularization to optimize continuous mixing ratios under thermodynamic constraints. Empirical results show that AI4S-SDS achieves full validity under the adopted HSP-based physical constraints and substantially improves exploration diversity compared to baseline agents. In preliminary lithography experiments, the framework identifies a novel photoresist developer formulation that demonstrates competitive or superior performance relative to a commercial benchmark, highlighting the potential of diversity-driven neuro-symbolic search for scientific discovery. Context (archaeology) ; Bridging (networking) ; Differentiable function ; Scalability ; Tree (set theory) ; Rotation formalisms in three dimensions ; Topology (electrical circuits) ; Combinatorial explosion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Big Data and Digital Economy