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Materials characterization workspace

Faster learning from thin film and 2D semiconductor data

Matter42 helps teams bring spectroscopy, microscopy, documents, and simulation outputs into one workspace for evidence-linked characterization and process decisions.

Explore the docs
Evidence typesRamanPLMicroscopyDocumentsSimulation

ATLAS workspace

One place for materials evidence

spectroscopy + simulation

Raman and PL

Maps, spectra, peaks

Parse hyperspectral measurements, inspect spatial patterns, and keep derived maps tied to the source data.

Microscopy

Images, masks, regions

Bring visual evidence into the same project context as spectra, notes, and derived defect labels.

Simulation

Models, sweeps, priors

Use physics-based context and synthetic calibration data to interpret noisy experimental measurements.

Why it matters

Materials decisions depend on more than one measurement

Useful characterization often lives across Raman peaks, PL maps, microscope images, sample history, literature notes, and simulation results. Matter42 keeps that evidence together and makes the reasoning reproducible.

01

Evidence stays attached

Figures, tool outputs, citations, and caveats remain connected to the files and samples that produced them.

02

Domain tools do the heavy lifting

ATLAS-backed analysis turns spectra and maps into structured outputs instead of leaving interpretation in a generic chat thread.

03

Teams share the same context

Experimentalists, modelers, and process engineers can work from the same project record without rebuilding context from scratch.

ATLAS workflow

From raw files to defensible characterization

The product focus is practical: help materials teams inspect mixed data, run trusted analysis tools, and preserve the evidence behind each recommendation.

01

Collect evidence

Upload spectroscopy maps, microscope images, tabular measurements, papers, and simulation outputs into a shared project workspace.

Multimodal intake

02

Ask grounded questions

Use an agent that can cite project files, call domain tools, and explain how an answer follows from the data.

Cited answers

03

Compare material regions

Turn raw measurements into maps, clusters, region masks, and quantitative summaries that stay linked to each sample.

Spatial maps

04

Decide what to do next

Connect characterization results to process notes and simulation context so teams can plan the next experiment with less ambiguity.

Process signal

Process context

Use experiments and models to understand quality trends

ATLAS can connect characterization outputs with process variables and simulation priors. The goal is not to replace expert judgment; it is to make the next question clearer and the supporting evidence easier to inspect.

MoS2 MOCVD process context

Real recipe nodes on a structured surface.

S/Mo is log-scaled so the optimum near 18 and the high-ratio backfire region both sit on screen.

map the surface, then optimize on it

T 790CS/Mo 18.2Q 0.83FWHM 4.0
13 vs 3 sampledcoverage: process contextQ, normalized quality

Materials scope

Built for thin film and 2D semiconductor programs

Matter42 started with Raman and PL workflows for transition-metal dichalcogenides, but the workspace is designed for broader characterization programs where data, models, and documents all matter.

Explore ATLAS docs

Transition-metal dichalcogenides

Raman and PL workflows for defect density, linewidth shifts, clustering, and region-aware interpretation.

MoS2WS2WSe2MoSe2

Thin film semiconductors

Project memory that connects measurements to sample history, processing notes, and repeatable analysis steps.

GrowthTransferTreatment

Emerging layered materials

A flexible workspace for combining spectroscopy, microscopy, documents, and simulation outputs as the material system evolves.

GraphenehBNHeterostructures

Shared project memory

Each analysis can carry sample context, source files, tool outputs, and notes forward so future work starts from the last reliable result instead of a folder of disconnected artifacts.

Work with us

Bring your characterization workflow into Matter42

Have Raman or PL maps, microscopy, process notes, simulation outputs, or documents you need to interpret together? We can help assess where an ATLAS workflow would make the work faster and easier to defend.

Read the docs
Matter42

Automated analysis and decision support for thin film and 2D semiconductor characterization.

ProductsTeamCareersDocsBlog
LinkedInPrivacy PolicyTerms and Conditions

Copyright © 2026 Matter42. All rights reserved.

Matter42
ProductsTeamCareersDocsBlog
Sign in
ProductsTeamCareersDocsBlog
Sign in

Materials characterization workspace

Faster learning from thin film and 2D semiconductor data

Matter42 helps teams bring spectroscopy, microscopy, documents, and simulation outputs into one workspace for evidence-linked characterization and process decisions.

Explore the docs
Evidence typesRamanPLMicroscopyDocumentsSimulation

ATLAS workspace

One place for materials evidence

spectroscopy + simulation

Raman and PL

Maps, spectra, peaks

Parse hyperspectral measurements, inspect spatial patterns, and keep derived maps tied to the source data.

Microscopy

Images, masks, regions

Bring visual evidence into the same project context as spectra, notes, and derived defect labels.

Simulation

Models, sweeps, priors

Use physics-based context and synthetic calibration data to interpret noisy experimental measurements.

Why it matters

Materials decisions depend on more than one measurement

Useful characterization often lives across Raman peaks, PL maps, microscope images, sample history, literature notes, and simulation results. Matter42 keeps that evidence together and makes the reasoning reproducible.

01

Evidence stays attached

Figures, tool outputs, citations, and caveats remain connected to the files and samples that produced them.

02

Domain tools do the heavy lifting

ATLAS-backed analysis turns spectra and maps into structured outputs instead of leaving interpretation in a generic chat thread.

03

Teams share the same context

Experimentalists, modelers, and process engineers can work from the same project record without rebuilding context from scratch.

ATLAS workflow

From raw files to defensible characterization

The product focus is practical: help materials teams inspect mixed data, run trusted analysis tools, and preserve the evidence behind each recommendation.

01

Collect evidence

Upload spectroscopy maps, microscope images, tabular measurements, papers, and simulation outputs into a shared project workspace.

Multimodal intake

02

Ask grounded questions

Use an agent that can cite project files, call domain tools, and explain how an answer follows from the data.

Cited answers

03

Compare material regions

Turn raw measurements into maps, clusters, region masks, and quantitative summaries that stay linked to each sample.

Spatial maps

04

Decide what to do next

Connect characterization results to process notes and simulation context so teams can plan the next experiment with less ambiguity.

Process signal

Process context

Use experiments and models to understand quality trends

ATLAS can connect characterization outputs with process variables and simulation priors. The goal is not to replace expert judgment; it is to make the next question clearer and the supporting evidence easier to inspect.

MoS2 MOCVD process context

Real recipe nodes on a structured surface.

S/Mo is log-scaled so the optimum near 18 and the high-ratio backfire region both sit on screen.

map the surface, then optimize on it

T 790CS/Mo 18.2Q 0.83FWHM 4.0
13 vs 3 sampledcoverage: process contextQ, normalized quality

Materials scope

Built for thin film and 2D semiconductor programs

Matter42 started with Raman and PL workflows for transition-metal dichalcogenides, but the workspace is designed for broader characterization programs where data, models, and documents all matter.

Explore ATLAS docs

Transition-metal dichalcogenides

Raman and PL workflows for defect density, linewidth shifts, clustering, and region-aware interpretation.

MoS2WS2WSe2MoSe2

Thin film semiconductors

Project memory that connects measurements to sample history, processing notes, and repeatable analysis steps.

GrowthTransferTreatment

Emerging layered materials

A flexible workspace for combining spectroscopy, microscopy, documents, and simulation outputs as the material system evolves.

GraphenehBNHeterostructures

Shared project memory

Each analysis can carry sample context, source files, tool outputs, and notes forward so future work starts from the last reliable result instead of a folder of disconnected artifacts.

Work with us

Bring your characterization workflow into Matter42

Have Raman or PL maps, microscopy, process notes, simulation outputs, or documents you need to interpret together? We can help assess where an ATLAS workflow would make the work faster and easier to defend.

Read the docs
Matter42

Automated analysis and decision support for thin film and 2D semiconductor characterization.

ProductsTeamCareersDocsBlog
LinkedInPrivacy PolicyTerms and Conditions

Copyright © 2026 Matter42. All rights reserved.

Matter42
ProductsTeamCareersDocsBlog
Sign in
ProductsTeamCareersDocsBlog
Sign in

Materials characterization workspace

Faster learning from thin film and 2D semiconductor data

Matter42 helps teams bring spectroscopy, microscopy, documents, and simulation outputs into one workspace for evidence-linked characterization and process decisions.

Explore the docs
Evidence typesRamanPLMicroscopyDocumentsSimulation

ATLAS workspace

One place for materials evidence

spectroscopy + simulation

Raman and PL

Maps, spectra, peaks

Parse hyperspectral measurements, inspect spatial patterns, and keep derived maps tied to the source data.

Microscopy

Images, masks, regions

Bring visual evidence into the same project context as spectra, notes, and derived defect labels.

Simulation

Models, sweeps, priors

Use physics-based context and synthetic calibration data to interpret noisy experimental measurements.

Why it matters

Materials decisions depend on more than one measurement

Useful characterization often lives across Raman peaks, PL maps, microscope images, sample history, literature notes, and simulation results. Matter42 keeps that evidence together and makes the reasoning reproducible.

01

Evidence stays attached

Figures, tool outputs, citations, and caveats remain connected to the files and samples that produced them.

02

Domain tools do the heavy lifting

ATLAS-backed analysis turns spectra and maps into structured outputs instead of leaving interpretation in a generic chat thread.

03

Teams share the same context

Experimentalists, modelers, and process engineers can work from the same project record without rebuilding context from scratch.

ATLAS workflow

From raw files to defensible characterization

The product focus is practical: help materials teams inspect mixed data, run trusted analysis tools, and preserve the evidence behind each recommendation.

01

Collect evidence

Upload spectroscopy maps, microscope images, tabular measurements, papers, and simulation outputs into a shared project workspace.

Multimodal intake

02

Ask grounded questions

Use an agent that can cite project files, call domain tools, and explain how an answer follows from the data.

Cited answers

03

Compare material regions

Turn raw measurements into maps, clusters, region masks, and quantitative summaries that stay linked to each sample.

Spatial maps

04

Decide what to do next

Connect characterization results to process notes and simulation context so teams can plan the next experiment with less ambiguity.

Process signal

Process context

Use experiments and models to understand quality trends

ATLAS can connect characterization outputs with process variables and simulation priors. The goal is not to replace expert judgment; it is to make the next question clearer and the supporting evidence easier to inspect.

MoS2 MOCVD process context

Real recipe nodes on a structured surface.

S/Mo is log-scaled so the optimum near 18 and the high-ratio backfire region both sit on screen.

map the surface, then optimize on it

T 790CS/Mo 18.2Q 0.83FWHM 4.0
13 vs 3 sampledcoverage: process contextQ, normalized quality

Materials scope

Built for thin film and 2D semiconductor programs

Matter42 started with Raman and PL workflows for transition-metal dichalcogenides, but the workspace is designed for broader characterization programs where data, models, and documents all matter.

Explore ATLAS docs

Transition-metal dichalcogenides

Raman and PL workflows for defect density, linewidth shifts, clustering, and region-aware interpretation.

MoS2WS2WSe2MoSe2

Thin film semiconductors

Project memory that connects measurements to sample history, processing notes, and repeatable analysis steps.

GrowthTransferTreatment

Emerging layered materials

A flexible workspace for combining spectroscopy, microscopy, documents, and simulation outputs as the material system evolves.

GraphenehBNHeterostructures

Shared project memory

Each analysis can carry sample context, source files, tool outputs, and notes forward so future work starts from the last reliable result instead of a folder of disconnected artifacts.

Work with us

Bring your characterization workflow into Matter42

Have Raman or PL maps, microscopy, process notes, simulation outputs, or documents you need to interpret together? We can help assess where an ATLAS workflow would make the work faster and easier to defend.

Read the docs
Matter42

Automated analysis and decision support for thin film and 2D semiconductor characterization.

ProductsTeamCareersDocsBlog
LinkedInPrivacy PolicyTerms and Conditions

Copyright © 2026 Matter42. All rights reserved.

Matter42
ProductsTeamCareersDocsBlog
Sign in
ProductsTeamCareersDocsBlog
Sign in

Materials characterization workspace

Faster learning from thin film and 2D semiconductor data

Matter42 helps teams bring spectroscopy, microscopy, documents, and simulation outputs into one workspace for evidence-linked characterization and process decisions.

Explore the docs
Evidence typesRamanPLMicroscopyDocumentsSimulation

ATLAS workspace

One place for materials evidence

spectroscopy + simulation

Raman and PL

Maps, spectra, peaks

Parse hyperspectral measurements, inspect spatial patterns, and keep derived maps tied to the source data.

Microscopy

Images, masks, regions

Bring visual evidence into the same project context as spectra, notes, and derived defect labels.

Simulation

Models, sweeps, priors

Use physics-based context and synthetic calibration data to interpret noisy experimental measurements.

Why it matters

Materials decisions depend on more than one measurement

Useful characterization often lives across Raman peaks, PL maps, microscope images, sample history, literature notes, and simulation results. Matter42 keeps that evidence together and makes the reasoning reproducible.

01

Evidence stays attached

Figures, tool outputs, citations, and caveats remain connected to the files and samples that produced them.

02

Domain tools do the heavy lifting

ATLAS-backed analysis turns spectra and maps into structured outputs instead of leaving interpretation in a generic chat thread.

03

Teams share the same context

Experimentalists, modelers, and process engineers can work from the same project record without rebuilding context from scratch.

ATLAS workflow

From raw files to defensible characterization

The product focus is practical: help materials teams inspect mixed data, run trusted analysis tools, and preserve the evidence behind each recommendation.

01

Collect evidence

Upload spectroscopy maps, microscope images, tabular measurements, papers, and simulation outputs into a shared project workspace.

Multimodal intake

02

Ask grounded questions

Use an agent that can cite project files, call domain tools, and explain how an answer follows from the data.

Cited answers

03

Compare material regions

Turn raw measurements into maps, clusters, region masks, and quantitative summaries that stay linked to each sample.

Spatial maps

04

Decide what to do next

Connect characterization results to process notes and simulation context so teams can plan the next experiment with less ambiguity.

Process signal

Process context

Use experiments and models to understand quality trends

ATLAS can connect characterization outputs with process variables and simulation priors. The goal is not to replace expert judgment; it is to make the next question clearer and the supporting evidence easier to inspect.

MoS2 MOCVD process context

Real recipe nodes on a structured surface.

S/Mo is log-scaled so the optimum near 18 and the high-ratio backfire region both sit on screen.

map the surface, then optimize on it

T 790CS/Mo 18.2Q 0.83FWHM 4.0
13 vs 3 sampledcoverage: process contextQ, normalized quality

Materials scope

Built for thin film and 2D semiconductor programs

Matter42 started with Raman and PL workflows for transition-metal dichalcogenides, but the workspace is designed for broader characterization programs where data, models, and documents all matter.

Explore ATLAS docs

Transition-metal dichalcogenides

Raman and PL workflows for defect density, linewidth shifts, clustering, and region-aware interpretation.

MoS2WS2WSe2MoSe2

Thin film semiconductors

Project memory that connects measurements to sample history, processing notes, and repeatable analysis steps.

GrowthTransferTreatment

Emerging layered materials

A flexible workspace for combining spectroscopy, microscopy, documents, and simulation outputs as the material system evolves.

GraphenehBNHeterostructures

Shared project memory

Each analysis can carry sample context, source files, tool outputs, and notes forward so future work starts from the last reliable result instead of a folder of disconnected artifacts.

Work with us

Bring your characterization workflow into Matter42

Have Raman or PL maps, microscopy, process notes, simulation outputs, or documents you need to interpret together? We can help assess where an ATLAS workflow would make the work faster and easier to defend.

Read the docs
Matter42

Automated analysis and decision support for thin film and 2D semiconductor characterization.

ProductsTeamCareersDocsBlog
LinkedInPrivacy PolicyTerms and Conditions

Copyright © 2026 Matter42. All rights reserved.