arXiv:2603. 29152v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties.
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:2505. 04997v3 Announce Type: replace Abstract: Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage workflow create significant barriers to entry.
The paper introduces a multi‑agent framework that lets large language models (LLMs) perform controlled experiments using scientific simulation models, specifically for pharmaceutical process design. Given a user query and baseline configuration, the system builds a structured task, designs and runs comparative simulations, interprets outcomes, and generates evidence‑based recommendations for optimizing process parameters. By integrating high‑fidelity simulations with LLMs, the approach yields more specific, actionable outputs and improves user‑rated correctness and helpfulness compared to language‑only reasoning.
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.
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
arXiv:2608. 04942v1 Announce Type: cross Abstract: CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications.
CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage.