arXiv AI By Zhi-Xun Shen, Jin Gene Wong, Karen Lei, Leon Garcia, Felix Baumberger, Sergey Borisenko, Aaron Bostwick, Ting Cao, Tanny Chavez, Yulin Chen, Monika Choudhary, Andrea Damascelli, Dan Dessau, Thomas P. Devereaux, Sandy A. Ekahana, Alexei Fedorov, Atsushi Fujimori, Yucheng Guo, M. Zahid Hasan, Makoto Hashimoto, Yu He, Philip Hofmann, Zahid Hussain, Harold Y. Hwang, Zhurun Ji, Chunjing Jia, Timur K. Kim, Phil D. C. King, Takeshi Kondo, Alessandra Lanzara, Chun Lin, Donghui Lu, Matteo Michiardi, Sung-Kwan Mo, Brian Moritz, Laurenz Rettig, Eli Rotenberg, Takafumi Sato, Kyle M. Shen, Jonathan A. Sobota, Inna Vishik, Shuolong Yang, Ming Yi, Timothy Zaklama

Framework for an AI-Ready ARPES Community Database

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning
Oct 8

Progress and Prospect of AI in ARPES Workflow

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...

By Sandy Adhitia Ekahana, Aalok Tiwari, Pratik Saud, Aaron Bostwick, Chris Jozwiak, Eli Rotenberg, Jyoti Katoch
arXiv AI
2d ago

Magnolia: A Persistent-Memory Agent Framework for Computational Chemistry

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.

By Tao Jiang, Benjamin Navet, Yousouf Taghzouti, Fabien Gandon, Louis-F\'elix Nothias, \'Elise Dumont
arXiv AI
Jul 7

Automated Data Readiness for Scientific AI

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.

By Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi, Ketan Maheshwari, Marshall McDonnell, Massimiliano Lupo Pasini, Polina Shpilker, Renan Souza, Patrick Widener, Sarp Oral, Wesley Brewer
arXiv Machine Learning
Oct 2

A strategic roadmap for an atomistic machine-learning ecosystem

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...

By J\"org Behler, Michele Ceriotti, Cecilia Clementi, G\'abor Cs\'anyi, Alin-Marin Elena, Aditi Krishnapriyan, Joseph W. Abbott, Fabio Affinito, Albert P. Bart\'ok, Ilyes Batatia, Filippo Bigi, Florian N. Br\"unig, Yannick Calvino Alonso, Giuseppe Carleo, Aur\'elie Champagne, Stefan Chmiela, Marc L. Descoteaux, Ralf Drautz, Alexandra Farcas, Meng Gao, Rohit Goswami, Michael F. Herbst, Christian Holm, James R. Kermode, Alexander L. M. Knoll, Tobias Kreiman, Hoang-Thien Luu, Yury Lysogorskiy, Mihai-Cosmin Marinica, Rocco Meli, Klaus-Robert M\"uller, Frank No\'e, Mohamadhosein Nosratjoo, Simon Olsson, Christoph Ortner, Aldo S. Pasos-Trejo, Anyang Peng, Eric Qu, Andrea Rizzi, Mariana Rossi, Bassem Sboui, Gregor N. C. Simm, Alexandre Tkatchenko, Jacopo Venturin, O. Anatole von Lilienfeld, William C. Witt, Brandon M. Wood, Tigany Zarrouk, Fabian Zills