Optimize advanced materials synthesis.
Accelerate commercialization.

Unlock innovation today by empowering your materials data with automated, AI-driven analysis.

Use it yourself within 24 hours.

Trusted by scientists
and engineers at

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AtomCloud platform dashboard screenshot

Introducing the AtomCloud.

Generate first-of-their-kind datasets for materials science with AI.

AtomCloud transforms data from nano-instruments into actionable insights to help you make better, faster decisions in R&D and manufacturing.

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Collect Data

As-is from all your instruments: images, videos, and logs, etc. No setup changes needed.

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Upload to AtomCloud

Web platform available anywhere, anytime. Broad file format support - no fussing with converting.

AtomCloud analyses

Apply Analyses

Automated AI-powered workflows analyze your data scalably. Use the state of the art in a single click.

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View Insights

Interactive images and charts to understand your data better and iterate faster. Exports easily to share or store.

Accessible, automated,
state of the art analysis.

Unlock faster validation and shorter cycle times.

Unified Platform
An AI-native workspace for the future of materials.

AtomCloud Data Catalogue

Extract information from files and other sources to build digital fingerprints of the processing environment, material properties, and experiment targets.

Integrate experimental information within a connected and flexible repository, ready for today's analysis and future workflows.

Collaborate seamlessly on raw and analyzed data across your organization by linking directly to interactive results and data exports.

Pattern Discovery
Synthesize data across multiple data sources.

AtomCloud Data Catalogue

Efficient cross-data-stream pattern discovery enabled by a unified data model uncovers valuable insights.

Scalable automated analysis supports broad inquiry into data relationships and reduces operator bias.

Accelerate development by identifying patterns that reduce trials, improve synthesis control, and saves measurement runs.

Integrate Data Streams

Store and understand materials information acquired from any data source.

RHEED
Deeper insights delivered in seconds.

AtomCloud RHEED patterns quantified

Detect important pattern changes automatically to quickly drill in on features of interest.

Quantify key properties of patterns to understand the underlying material changes rigorously.

Visualize quantified patterns through interactive images and charts, empowering operator intuition.

XPS
Elemental analysis without peak fitting.

AtomCloud XPS analysis

Extract atomic concentrations from spectra in one click without needing to apply laborious peak fitting procedures.

Compare analyzed XPS spectra of multiple samples interactively to understand differences.

Reanalyze spectra data easily by selecting atomic elements to focus on.

Instrument Logs
Understand characterization data with context.

AtomCloud XPS analysis

Parse processing history directly from instrument data.

Track actual environment data rather than relying on recipe notes.

Analyze processing conditions directly alongside characterization data for tighter process control.

Custom Data Sources
Incorporate any data source into your analysis.

AtomCloud XPS analysis

Annotate files and physical samples with figures of merit, text labels, or freeform notes.

Ingest any type of tabular information and link with data files or sample records.

Extract context and figures from filenames and metadata.

Case Studies

AtomCloud accelerates time-to-insight for complex data across broad material systems.

Composition and RHEED pattern correleation

In-situ Proxy for Composition

Performing XPS to determine composition for each sample.AtomCloud's automatically extracted pattern features correlated closely with dopant concentration allowing compostion for subsiquent samples to be estimated from in-situ RHEED.
Composition and RHEED pattern correleation

Cross-Data-Stream Pattern Detection

Manual hypothesis-test iterations to identify relationships.AtomCloud's data model enables automated search for patterns within connected data. Early relationship identification accelerates materials engineering and synthesis control.
CoSi material system RHEED pattern image

Detecting Kinetic Transitions

A video of a CoSi material system with complex patterns, low-contrast, and long duration is difficult to analyze by eye.AtomCloud's RHEED analysis workflow was able to automatically detect a subtle but validated pattern change
CoSi material system RHEED pattern image

Analysis Technology

Analyzing RHEED patterns by eye is time-consuming and prone to errors.AtomCloud uses unsupervised learning algorithms and materials science-specifc clustering techniques to automatically analyze RHEED patterns.
CoSi material system RHEED pattern image

Analyzing Rotating Growths

RHEED videos of rotating growths are difficult to analyze.AtomCloud's RHEED analysis workflow accounts for rotation, unlocking rotating RHEED videos as a source of insight.

The AI-driven platform for the
future of materials R&D.

Build physics-rich datasets for bottom-up materials manufacturing.

Accelerate R&D and manufacturing progress

Transfer insights between characterization techniques using feature correlation. Automated workflows scale materials validation and experiment iteration.

Accelerate R&D and manufacturing

Unlock more insights from the same data

Apply state of the art materials-specific analyses with one click to empower domain expertise.

Build an analytical reference library

Generate machine-readable, physics-rich datasets from actual materials characterization. Easily compare multiple characterization results to leverage historical findings.

Accelerate R&D and manufacturing

Improve data integration and accessibility

Store all your materials data in a single, structured repository. Share rich analysis results with context.

Accelerate R&D and manufacturing

Accelerate & optimize your advanced materials production today.

Use it yourself within 24 hours.