Matter42 helps materials teams organize projects, upload characterization data, run calibrated analysis workflows, compare literature, and use AI agents to move from raw measurements to interpretable results.

The platform is built for materials scientists, process engineers, device teams, and research groups working with thin films, 2D semiconductors, Raman, PL, XRD, microscopy-adjacent workflows, and defect analysis.

No. Core workflows run through the web app: upload files, inspect parsed data, run analysis Blocks, and review outputs. Python and custom Blocks are available when your team wants deeper automation or reusable internal workflows.

Create or open a project, upload your first characterization file, then review the generated maps, spectra, and parser summary before running heavier analysis Blocks.

Matter42 supports Raman and PL maps, single spectra, CSV/TSV tables, LabSpec text exports, TIFF hyperspectral stacks, calibrated AFM TIFF/CSV surface exports, XRD patterns, PDFs, markdown, plaintext, and images. When a format is custom, parser Blocks can be adapted for repeat use.

The platform detects the file type, parses it into a dataset, stores the result in your selected project, and prepares visual views such as spatial maps, spectra, tables, parser notes, or document records depending on the input.

Atlas Metrology workflows include defect clustering, Raman-based defect density estimation, defect type classification, PL defect activity scoring, region segmentation, XRD, AFM, and XPS analysis, and literature search. Atlas Physics AI modeling (atomistic and process) is coming soon as plan add-ons; for a specific material or reactor today, contact us.

Yes. Upload both files into the same project and run paired workflows such as defect clustering with PL as the primary dataset and Raman as auxiliary context.

Yes. Region segmentation can label interior, transition, and damaged areas so follow-up analysis can avoid boundary artifacts, PFIB halos, masks, or damaged regions when appropriate.

Matter42 automates the tedious parts, but scientific judgment still matters. Review parser summaries, maps, masks, spectra, and any warnings before treating results as final.

The calibrated Atlas workflows compare measured spectral features with reference data and simulation-backed signatures. The app exposes the workflow output so your team can inspect the assumptions and supporting views.

Keep the dataset, note the issue, and contact support from the app Resources menu. Parser fixes and custom parser Blocks are often the right answer for unusual instrument exports.

Use projects for a research program, customer engagement, wafer run, or experiment series. Within a project, you can keep related files, characterizations, chats, and analysis outputs together.

No. Uploaded datasets are associated with your Matter42 account and selected project. If your organization has specific security, retention, or compliance requirements, contact the Matter42 team before uploading sensitive data.

Yes. Matter42 can run in your own cloud account, on your own servers, or fully air-gapped on a single laptop or workstation with no network connection. The analysis tools are the same in every deployment, and we fit the setup to your network and security review. See Security and deployment.The air-gapped deployment is a separate product and is not part of the self-serve plans. Contact us to set it up.

Yes. Build and refine a Block in Matter42 Cloud or on your private deployment, where the most capable AI assistant is available, then export it and run it on a fully air-gapped machine. The Block runs the same way offline, with no language model or network connection needed. See how Block export works.

Start with the Matter42 documentation, which covers core concepts, app workflows, and the Python SDK.

Signed-in users can use Contact support from the app Resources menu. New customers can also reach out through the website contact form.