arXiv AI
Recursive Improvement of a Differentiable Scientific Software Ecosystem
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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.
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EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
arXiv:2607. 26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics.
arXiv AI
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Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision
The paper introduces Re4, a scientific computing agent that employs a four-step logical chain—rewriting, resolution, review, and revision—using three large language models: a Consultant, a Programmer, and a Reviewer. The Consultant rewrites problem descriptions by linking them to domain knowledge, the Programmer generates and runs code, and the Reviewer iteratively refines the code through self-debugging. Experiments on PDEs, fluid dynamics, ill‑conditioned linear systems, and data‑driven physics show that this collaborative framework markedly improves bug‑free code generation and execution success rates across several LLMs.
arXiv AI
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ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
arXiv:2512. 03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck.
arXiv AI
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CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
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.
Hugging Face Trending Papers
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CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics 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.