Framework for an AI-Ready ARPES Community Database
cond-mat.mtrl-sci, cond-mat.str-el, cond-mat.supr-con, cs.AI
Submitted: 2026-10-11
Updated: 2026-10-11
Sources
- Probabilistic denoising for reliable signal extraction in spectroscopy
- A simulation-based training framework for machine-learning applications in ARPES
- AIMS: an AI experimentalist turns uncertainty into quantum matter discovery
- Qumus: Realization of An Embodied AI Quantum Material Experimentalist
- On the Opportunities and Risks of Foundation Models
- Scaling Laws for Neural Language Models
- Training Compute-Optimal Large Language Models
- Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
- MAGIC: Near-Optimal Data Attribution for Deep Learning
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