Elevate Materials
Data Management.

Go beyond file folders to create a truly integrated repository.

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What it is

AtomCloud offers a unified platform for managing materials science data that goes far beyond storing files.

Cloud Platform with Flexible and Powerful Data Model

AtomCloud's data model allows information and files to be connected together reflecting real-world relationships.

Image of a RHEED pattern

Multimodal Data Ingestion

AtomCloud supports a growing variety of data ingestion methods allowing for easy and scalable use of the platform. Manual upload, screen capture and API integrations enable a broad range of use cases.

Image of a RHEED pattern
RHEED fingerprint

Automated Information Extraction

AtomCloud's focus on automated information extraction means that data files are no longer a deadend. Information from a range of data-streams is fully extracted and stored on equal footing allowing powerful data management and analysis capabilities.

Image of a RHEED pattern

Contextualize and Annotate

AtomCloud leverages a wide variety of metadata to contextualize data. Users also have the option to appy pre-defined and custom labels to data on a wide range of dimension. This is all made possibly by a powerful data structure representing real-world concepts like Physical Samples.

Image of a RHEED pattern

Why it helps

Typical materials science data covers a wide range of data and file types stored in file-tree structure on local or shared drives. This restricts scientists and engineers to a single hierarchy for organizing and viewing data. Storing information in a flexible data model allows for data enrichment to persist and improve over time. This flexible and connected data structure allows information to be accessed efficiently at any scale and time in the future.

Automated information extraction from a range of data-streams and file types unlocks the ability of AtomCloud to provide a unified data platform. While some organizations have sample managment systems to store and organize files, without proper information extraction the potential for truly flexible and scalable analysis is not realized.

FAQs

Case Studies

AtomCloud helps operators extract insights from RHEED patterns 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.

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