TAS-AI

Uses

How it works

Highlighted claims

• TAS-AI is organized as a staged autonomous workflow with agnostic discovery, physics-informed inference, motion-aware sequencing, and an optional audit layer. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
• TAS-AI treats autonomous triple-axis spectroscopy as separate tasks for signal discovery, Hamiltonian selection, and parameter refinement. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
• The workflow begins with agnostic discovery before switching to physics-informed planning after signal structure is localized. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
• The integrated hybrid run used coarse grid sampling and enhanced Log-GP before physics refinement began at measurement 29. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
• The automatic handoff is triggered after discovery identifies a localized signal region sufficient to instantiate a physics model on a restricted action set. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy