Imagen de Portada

Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mok, Dong Hyeon, Back, Seoin, Fung, Victor, Hu, Guoxiang
Formato:	Preprint
Publicado:	2026
Materias:	Materials Science Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Generative Language Model for Catalyst Discovery
por: Mok, Dong Hyeon, et al.
Publicado: (2024)

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
por: Kong, Lingyu, et al.
Publicado: (2025)

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
por: Kong, Lingyu, et al.
Publicado: (2025)

Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
por: Cha, Seungjun, et al.
Publicado: (2026)

Validating ΔΔG Selectivity Descriptor for Electrosynthesis of H2O2 from Oxygen Reduction Reaction
por: Dong Hyeon Mok, et al.
Publicado: (2024)

LLMatDesign: Autonomous Materials Discovery with Large Language Models
por: Jia, Shuyi, et al.
Publicado: (2024)

Inverse Materials Design by Large Language Model-Assisted Generative Framework
por: Hao, Yun, et al.
Publicado: (2025)

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
por: Pan, Mianzhi, et al.
Publicado: (2026)

Enhanced Fe IV ═O Generation via Peroxymonosulfate Activation by an Edge‐Site Engineered Single‐Atom Iron Catalyst
por: Donghyun Lee, et al.
Publicado: (2025)

Differentiation of Distinct Single Atoms via Multi-Defocus Fusion Method
por: Li, Yangfan, et al.
Publicado: (2025)

Determining Atomic Structure from Spectroscopy via an Active Learning Framework
por: Slagle, Ian, et al.
Publicado: (2026)

Electronic Structure Guided Inverse Design Using Generative Models
por: Jia, Shuyi, et al.
Publicado: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
por: Jia, Shuyi, et al.
Publicado: (2025)

A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design
por: Tian, Jie, et al.
Publicado: (2025)

Training-Free Active Learning Framework in Materials Science with Large Language Models
por: Wang, Hongchen, et al.
Publicado: (2025)

Facet: highly efficient E(3)-equivariant networks for interatomic potentials
por: Miklaucic, Nicholas, et al.
Publicado: (2025)

Decoding the Electronic and Structural Fingerprints of Single-Atom Catalysts via DFT-Assisted XANES Analysis
por: Lazar, Petr, et al.
Publicado: (2026)

A Multi-Technique Study of C2H4 Adsorption on a Model Single-Atom Rh1 Catalyst
por: Wang, Chunlei, et al.
Publicado: (2024)

Stabilizing Single-Atom Catalysts on Metastable Phases of Transition Metal Dichalcogenides
por: Wang, Lina, et al.
Publicado: (2025)

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
por: Guo, Leo, et al.
Publicado: (2025)

MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
por: Badrinarayanan, Srivathsan, et al.
Publicado: (2025)

Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
por: Cheula, Raffaele, et al.
Publicado: (2025)

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
por: Mukherjee, Shrimon, et al.
Publicado: (2024)

MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
por: Weng, Yonghao, et al.
Publicado: (2025)

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
por: Rothfarb, Samuel, et al.
Publicado: (2025)

Artificial Intelligence-Enabled Holistic Design of Catalysts Tailored for Semiconducting Carbon Nanotube Growth
por: Qian, Liu, et al.
Publicado: (2025)

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
por: Anam, Md Zaibul, et al.
Publicado: (2025)

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
por: Jin, Emily, et al.
Publicado: (2025)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
por: Hu, Jeffrey, et al.
Publicado: (2026)

Hydrogen Activation via Dihydride Formation on a Rh1/Fe3O4(001) Single-Atom Catalyst
por: Wang, Chunlei, et al.
Publicado: (2026)

Potential-Induced Dynamic Coordination of Nonmetal Atoms Directly Bound to Metal Centers in Graphene-Embedded Single-Atom Catalysts and Its Implications
por: Li, Jiahang, et al.
Publicado: (2026)

Convolutional Neural Networks and Volcano Plots: Screening and Prediction of Two-Dimensional Single-Atom Catalysts
por: Yang, Haoyu, et al.
Publicado: (2024)

AlloyBERT: Alloy Property Prediction with Large Language Models
por: Chaudhari, Akshat, et al.
Publicado: (2024)

Regression with Large Language Models for Materials and Molecular Property Prediction
por: Jacobs, Ryan, et al.
Publicado: (2024)

Causal Discovery from Data Assisted by Large Language Models
por: Barakati, Kamyar, et al.
Publicado: (2025)

Unifying Polymer Modeling and Design via a Conformation-Centric Generative Foundation Model
por: Wang, Fanmeng, et al.
Publicado: (2025)

Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
por: Takahara, Izumi, et al.
Publicado: (2024)

Single Atom Catalysts with Halogen Ligands: Elevating the HER Performance of Pd-anchored MoS2 monolayer
por: Sun, Feng, et al.
Publicado: (2025)

Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery
por: Li, Mingze, et al.
Publicado: (2026)

Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
por: Okabe, Ryotaro, et al.
Publicado: (2024)