Materials AI
SciPhys is built for the physical world: measurements, uncertainty, structure, literature, sample history and the decisions researchers make next.
Instrument data becomes the primary evidence source.
Domain algorithms produce measurable signals before AI explains them.
Literature and lab history provide context when available.
What it does
A general model can write plausible scientific text, but it does not automatically know the peak list, measurement settings, reference pattern, residuals or lab history behind a run.
Materials interpretation often depends on structure files, reference patterns and papers. SciPhys is designed to bring those sources next to the experiment rather than leaving them scattered across tabs.
The long-term advantage is the feedback loop: every upload, correction, comparison and report strengthens the lab's private knowledge graph.
Workflow
Start from measured data, not only prompts.
Combine algorithms, references, papers and lab context.
Use corrections and outcomes to sharpen the next analysis.
FAQ
It means AI workflows grounded in instrument data, physics-aware analysis, structure context, literature and lab history.
SciPhys is designed to work with reference patterns, structure files, literature metadata and user-provided lab data where available.
SciPhys has access to the experimental object: files, metadata, computed peaks, fits, references, reports and lab history. A general assistant usually sees only the text a user pastes into it.