arXiv:2610.10140v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectro...
Magnolia is a persistent‑memory AI agent framework designed for computational chemistry. It integrates protocols such as docking, quantum chemistry, and molecular dynamics with lab notebooks and LLM‑augmented chat logs, using a three‑tier memory system to capture, distill, and promote learnings into project‑tier entries and cross‑project rules. In four case studies, Magnolia tracked 317 runs, produced 116 project‑tier entries, and promoted four cross‑project rules, demonstrating that runs with memory passed all checks while memory‑less runs failed.
arXiv:2607. 02771v1 Announce Type: new Abstract: Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data.
arXiv:2609.39090v1 Announce Type: cross Abstract: Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of...
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.
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
arXiv:2603. 13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher.
The article titled "The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry" discusses insights from the TPC26 conference, where leaders from academia, national laboratories, and industry examined how AI, autonomous agents, self-driving labs, high‑performance computing, and quantum computing converge to accelerate materials science discovery. It presents firsthand experiences from researchers at the forefront of these technologies and argues that their simultaneous maturation marks a tipping point for transformative advances and productive disruption in chemical sciences.
arXiv:2607. 09806v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge.
arXiv:2605.02003v3 Announce Type: replace Abstract: Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely...
arXiv:2603. 23101v3 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence.
arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.