Framework for an AI-Ready ARPES Community Database
As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record.
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Key points
- Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI).
- A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery.
- In the long term, a diverse corpus of measured electronic spectra could support foundation models for quantum matter.
- We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.
Sources (1)
- [1]Framework for an AI-Ready ARPES Community DatabasearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 12:12 AM
As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record.
Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI).
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