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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
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...
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.
Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or web environments, molecular LLM agents must perceive,...
AgentFold is a multi‑agent framework that treats protein‑folding model design as a closed‑loop search over executable code variants. Starting from the ESMFold codebase, the agents generate hypotheses, modify and debug code, evaluate model variants, and store both successes and failures in structured memory, guided by an MCTS‑style policy that allocates GPU resources. In an engineering‑scale experiment, AgentFold explored about 80 variants using 5,000 GPU‑hours and 170 million LLM tokens, improving the best lDDT score by 7.5% over independent Codex proposals and outperforming a random‑search baseline, while also uncovering empirical design patterns such as the benefits of early, soft, learnable priors.
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.