Cover Image

ChemGraph: An Agentic Framework for Computational Chemistry Workflows

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pham, Thang D., Tanikanti, Aditya, Keçeli, Murat
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Materials Science Artificial Intelligence Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
by: Grizzi, Vitor F., et al.
Published: (2026)

Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
by: Peivaste, Iman, et al.
Published: (2026)

Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
by: Yin, Shi, et al.
Published: (2025)

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
by: Li, Zihan, et al.
Published: (2025)

Harnessing AtomisticSkills for Agentic Atomistic Research
by: Deng, Bowen, et al.
Published: (2026)

Evaluating Large Language Models in Scientific Discovery
by: Song, Zhangde, et al.
Published: (2025)

Generative Chemical Language Models for Energetic Materials Discovery
by: Salij, Andrew, et al.
Published: (2026)

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design
by: Wang, Xin-De, et al.
Published: (2025)

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
by: Li, Yunyang, et al.
Published: (2025)

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
by: Du, Yuanqi, et al.
Published: (2025)

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
by: Cheng, Austin, et al.
Published: (2024)

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)

Are large language models superhuman chemists?
by: Mirza, Adrian, et al.
Published: (2024)

LEAP: A closed-loop framework for perovskite precursor additive discovery
by: Wang, Xin-De, et al.
Published: (2026)

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
by: Riebesell, Janosh, et al.
Published: (2024)

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols
by: Soleymanibrojeni, Mohammad, et al.
Published: (2025)

Thermal Conductivity Limits of MoS$_2$ and MoSe$_2$: Revisiting High-Order Anharmonic Lattice Dynamics with Machine Learning Potentials
by: Kocabas, Tugbey, et al.
Published: (2025)

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
by: Zhang, Gongbo, et al.
Published: (2025)

A Bayesian Flow Network Framework for Chemistry Tasks
by: Tao, Nianze, et al.
Published: (2024)

Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
by: Wang, Hongyi, et al.
Published: (2025)

Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
by: Goswami, Rohit, et al.
Published: (2025)

QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking
by: Zeng, Yinqi, et al.
Published: (2025)

ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
by: Sprueill, Henry W., et al.
Published: (2024)

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)

Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
by: Yescas-Ramos, Zuriel Y., et al.
Published: (2026)

A Review of Large Language Models and Autonomous Agents in Chemistry
by: Ramos, Mayk Caldas, et al.
Published: (2024)

Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems
by: Pornpatcharapong, Wasut
Published: (2025)

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
by: Duan, Chenru, et al.
Published: (2025)

Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
by: Judge, Edmund, et al.
Published: (2024)

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by: Zimmermann, Yoel, et al.
Published: (2024)

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes
by: Cao, He, et al.
Published: (2024)

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
by: Ye, Xinwu, et al.
Published: (2026)

Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
by: Thiemann, Fabian L., et al.
Published: (2025)

From Generalist to Specialist: A Survey of Large Language Models for Chemistry
by: Han, Yang, et al.
Published: (2024)

ChemNavigator: Agentic AI Discovery of Design Rules for Organic Photocatalysts
by: Peivaste, Iman, et al.
Published: (2026)

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities
by: Ding, Hu, et al.
Published: (2025)

Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
by: Alberts, Marvin, et al.
Published: (2024)

Reciprocal Space Attention for Learning Long-Range Interactions
by: Ramasubramanian, Hariharan, et al.
Published: (2025)

Delta-learned force fields for nonbonded interactions: Addressing the strength mismatch between covalent-nonbonded interaction for global models
by: Cázares-Trejo, Leonardo, et al.
Published: (2025)

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)