Matter42 is built around a practical observation: useful materials decisions rarely come from one clean file. A single sample may have a Raman map, a PL map, microscopy, sample history, process notes, simulation outputs, and a few relevant papers. The workflow only becomes trustworthy when those artifacts can be interpreted together.

The product question is therefore not just "can we plot a spectrum?" It is "can a team come back later and understand what was measured, what was concluded, which tool produced the conclusion, and what evidence still looks ambiguous?"

What the workspace has to preserve

The first requirement is provenance. If a defect-density map comes from an E2g linewidth inversion, the project should preserve the source file, the region selection, the calibration assumptions, the figure, and the numerical output. A collaborator should not have to reconstruct the workflow from screenshots and memory.

The second requirement is context. A spectroscopy result means something different when it came from a clean interior region, a damaged edge, a transfer residue, or a sample with known growth drift. Matter42 keeps sample notes and analysis outputs in the same project so the agent can reason with both.

The third requirement is repeatability. A workflow that works once in a notebook is useful; a workflow that can be rerun across a batch, reviewed by a teammate, and compared against future samples is much more valuable.

Why multimodal matters

Spectroscopy is often the fastest measurement, but it is not the whole story. A Raman linewidth can point to vacancy density, a PL map can reveal spatial heterogeneity, microscopy can expose damaged regions, and simulation can help decide whether a spectral signature is plausible for a given defect family.

The workspace is designed to let those pieces inform each other without forcing every user into the same rigid pipeline. A team can start with a single Raman map, add PL or microscopy later, and keep the project record coherent as more evidence arrives.

A concrete example

Suppose a researcher uploads a Raman map from an MOCVD-grown MoS2 film. The workspace parses the file, detects the spatial grid and spectral axis, identifies the E2g and A1g regions, and masks noisy pixels before treating them as evidence.

From there, calibrated tools can extract E2g linewidths, correct for instrument broadening, estimate a defect-density map, and compare representative spectra against simulated defect signatures. The key is that the final conclusion stays connected to the raw file, selected pixels, fitting assumptions, calibration curve, generated figures, and caveats, so another teammate can later see not just the answer, but how it was produced.

What the agent should do

The agent is most useful when it stays close to the evidence. It should parse files, call calibrated tools, cite the figures it used, and say when the data is not strong enough for a confident conclusion. For quantitative work, the tool output is the source of truth; the agent's job is to assemble the right context and explain the result clearly.

That is the difference between a generic chat interface and a research workspace. The answer is not just a paragraph. It is a result with inputs, assumptions, caveats, and a trail someone else can audit.

Where we are focusing

Our near-term focus is ATLAS-backed characterization: Raman and PL analysis, defect clustering, defect-density estimation, region segmentation, defect-type classification, literature context, and simulation outputs that help materials teams plan the next experiment.

The broader goal is a workspace where thin film and 2D semiconductor teams can move from messy project evidence to defensible decisions without losing the scientific trail along the way.