Hugging Face Trending Papers
Molecular LLM Agents: From Architectural Design to Scientific Autonomy
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Aug 25
Molecular LLM Agents: From Architectural Design to Scientific Autonomy
arXiv:2608.23104v1 Announce Type: cross Abstract: Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or we...
arXiv AI
Jun 16
Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents
arXiv:2603. 15952v2 Announce Type: replace Abstract: Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks.
arXiv AI
Jun 12
MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback
arXiv:2606. 12916v1 Announce Type: new Abstract: Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principle physics.
arXiv Machine Learning
Jun 11
My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
arXiv AI
Aug 10
CAi Copilot: Reducing Operational Workload in Molecular Design through Intent-Driven Agentic Workflows
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
arXiv AI
4d ago
El Agente Potente: High-Throughput Agentic Atomistic Simulations
El Agente Potente is an agentic system that integrates typed execution graphs and a coding mode to facilitate machine‑learning interatomic potential (MLIP) driven atomistic simulations. Typed execution graphs offer structured, provenance‑aware workflows where large language models handle planning and routing while deterministic Python code performs scientific computation and validation. The coding agent builds customized workflows for tasks needing procedural flexibility, invoking existing Potente functions for supported calculations. The system is demonstrated across materials discovery, energy‑landscape exploration, adsorption, and catalytic reaction workflows, with benchmarks on reproducibility and LLM token cost.