01
Bring in any measurement
Raman, PL, microscopy, tabular data, and literature land in one project workspace, straight from the instrument or the fab floor.
Multimodal intake
Characterization · Analysis · Process optimization
Materials intelligence for labs and fabs
Matter42 turns Raman, PL, XRD, and AFM measurements into defect densities, uniformity maps, and film-quality metrics with the method and calibration status attached. Use it to characterize samples, qualify materials, or optimize a process, whichever side of the loop your team owns.
Works withRamanPLXRDAFMIRXPS
Growth methodsMOCVDMBECVDALDPVD
Platform
One home for your materials intelligence
Measurements, growth records, literature, and simulation in a shared workspace, so what your team knows about a material lives with the project instead of scattered across notebooks, one-off scripts, and institutional memory.
02
Ask scientific questions
The agent keeps project context, cites files, and calls domain tools instead of leaving analysis in a generic chat.
Context preserved
03
Map defect populations
Explore spectra, cluster regions, estimate defect density, and classify likely defect families.
Spatial maps
04
Connect to process knobs
Use simulation and structured outputs to reason from growth conditions to measurable material quality.
Process signal
Products
Three ways into the materials intelligence loop
Analyze measured samples with Atlas, model growth with Apollo, and interrogate the structured research record with Literature.
Agent analysis
Intelligence that shows its work
Every result comes back as an interactive figure with the numbers behind it: which pixels were used, which were masked, and whether the estimate sits inside calibration. These are real outputs from measured samples, not illustrations.
AFM surface roughness
Loading Plotly figure...
A calibrated Bruker scan is plane- and line-leveled into a topography map with ISO-style roughness metrics. Raw heights are stored untouched.
Roughness report
Sa 5.0 nm, Sq 6.8 nm over a 1 um scan after plane and scan-line leveling. Right-tailed height distribution (skewness 1.02) from grown islands, 262,144 valid pixels.
MetricValueStatus
Sa (mean abs)5.0 nmleveled
Sq (RMS)6.8 nmleveled
Skewness1.02right-tailed
Excess kurtosis1.42peaked
Workflow
From raw data to intelligence in three steps
Every project moves through the same loop, so results stay comparable across samples, runs, and team members, and every decision traces back to the measurement behind it.
01
Collect
Create a project and upload the raw files that define a sample, experiment, or growth run.
Raman map · PL file · paper
02
Analyze
Let the agent parse data, inspect maps, run defect tools, and return figures with structured outputs.
parse · cluster · estimate
03
Decide
Compare regions, document caveats, and turn analysis into a decision: a qualification report, a spec check, or the next process run.
compare · cite · decide
Why we're building this
Every lab and every fab should have materials intelligence on tap.
Today that expertise lives in a handful of people, one-off scripts, and institutional memory. Matter42 combines calibrated physics-based models with multimodal analysis so the connection from measurement to decision is explicit, repeatable, and reviewable, whether it runs as your team's workspace, embedded behind your instruments, or delivered alongside our scientists as a service.
Team
Built by people who have run the experiments
Decades of combined experience in materials physics, multiscale simulation, and large-scale science programs, applied to turning raw characterization data into intelligence teams can act on.
Request a walkthrough
See materials intelligence on your data
Bring a Raman map, PL scan, or process question. We'll show how the agent analyzes it, what the outputs look like, and how it fits your workflow, as a platform, an integration, or an engagement with our team.