When Spectra Become Tokens for AI-Driven Discovery

Artificial intelligence (AI) has learned to work with language by breaking continuous information into discrete tokens and modeling the relationships among them. A new conceptual framework proposes that spectroscopy may offer the physical sciences an analogous information system: spectra can be treated as "physical tokens" generated when matter interacts with electromagnetic radiation. These tokens encode identity, local environment, dynamic change, and relationships governed by quantum mechanics. By reframing spectra as a computable language of matter rather than only an analytical readout, the framework could help connect spectroscopy, machine learning, inverse design, and automated experimentation, laying the groundwork for a more unified, AI-readable language for materials discovery.