01
Upload research data
Bring Raman, PL, microscopy, tabular measurements, and papers into one project workspace.
Multimodal intake
AI workflows for materials R&D
From complex materials data to reliable manufacturing decisions
Matter42 turns messy spectroscopy, microscopy, documents, and simulation outputs into an AI-powered workspace that helps materials and semiconductor teams make faster characterization and manufacturing decisions.
Platform
One workspace for characterization, context, and simulation
Matter42 gives materials teams a shared place to upload multimodal evidence, ask reproducible questions, and keep the scientific trail attached to the project instead of scattered across notebooks and one-off scripts.
02
Ask scientific questions
The agent keeps project context, cites files, and calls domain tools instead of leaving analysis in a generic chat.
papermaprun
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.
growthquality
Process signal
Workflow
A cleaner loop from raw data to next experiment
01
Collect
Create a project and upload the raw files that define a sample, experiment, or growth run.
Raman mapPL filepaper
02
Analyze
Let the agent parse data, inspect maps, run defect tools, and return figures with structured outputs.
parseclusterestimate
03
Decide
Compare regions, document caveats, and turn characterization into the next process experiment.
comparecitenext run
Agent analysis
From raw spectra to defensible defect maps
Upload Raman or PL data and get interactive maps, spectral inversion, defect estimates, and caveats that stay tied to the evidence instead of disappearing into a one-off notebook.
WS2 PFIB Raman map
Defect density estimation
Raman pixels
885
Mean density
1.38%
Correction
10.0 cm^-1
Loading Plotly figure...
Hover the maps to inspect local linewidth and density estimates. The table keeps calibration status visible.
Tool output
Mean E2g FWHM: 9.2 cm^-1. Best estimate: 1.4% S->O substitution. Valid Raman pixels: 885.
Defect typeEst.Status
S->O substitution1.37%interpolated
W vacancy5.05%interpolated
S vacancy6.08%interpolated
S->C substitution4.00%above calibration
Vision
Matter42 brings agentic analysis and calibrated models to accelerate discovery, characterization, and process learning
Materials teams need to connect noisy measurements, simulation outputs, and scattered documentation without losing evidence. Matter42 combines ATLAS, multimodal analytics, and physics-based models so research teams can move from raw data to defensible characterization and process decisions.
Team
Built by scientists and operators who understand experimental complexity
Matthias Kling, PhD
Co-Founder
Physicist with 20+ years expertise in ultrafast electronics, nanophotonics, and quantum physics. Experience in leading $100M+ science programs. Passionate about advancing the frontiers of technology with AI for solving urgent grand problems.
LinkedIn
Thomas Linker, PhD
Co-Founder
Physicist with 8+ years expertise in multiscale simulations and ML for atomic scale control and characterization of materials. Experience in leading combined experimental and theoretical science campaigns on complex systems.
LinkedIn
Kamila Stepniowska
Founding Team Member
Evangelist in AI and data science, 10+ years experience in operations. Experience in building partnerships in tech industry & open source community. Partnered with Intel, Loreal, United Nations, Orange.
Founding experience: In Browser AI, Ginger Tech, GC
LinkedIn
Let's talk
Have a dataset, workflow, or materials problem in mind?
We can help you map a first experiment, evaluate the current tool fit, or discuss how Matter42 could support your research team.