A Platform for Scalable
Pattern Discovery.

Efficiently uncover patterns and relationships across multiple data sources.

Use it yourself within 24 hours.

What it is

Unified materials science data enables powerful automated pattern identification across a nearly unlimited amount of data.

Unified Data Means Uninhibited Information Availability

AtomCloud's automated data extractions means that critical Information is not stuck in file formats that are not otherwise machine readable. Analysis becomes far more efficient when information is extracted, and stored on equal footing.

The platform captures context through automated processing and user input to build scalable datasets that provide powerful analysis potential today and far into the future. These rich datasets unlock automated pattern searching and advanced computational techniques.

Image of a RHEED pattern

Efficient Pattern Search

Testing combinations of variables for possible relationships can be done efficiently with the foundation provided by AtomCloud.

Image of a RHEED pattern
RHEED fingerprint

Enabling Advanced Computational Techniques

Small datasets: bootstrap aggregation with linear and nonlinear models for classification and regression.

Image of a RHEED pattern
RHEED fingerprint

Large datasets: validation for digital twins and active learning for recipe recommendation.

Design of Experiment: Bayesian Optimization can be employed to suggest the next trial condition and and reduce the number of trials by up to 80%.

Image of a RHEED pattern

Why it helps

Typically materials science data is analyzed serially. File formats such as images or spectra are analyzed independently and extracted conclusions or information is then analyzed alongside other experimental data based on identified hypothese. The number of hypotheses that can be investigated depends on time and resources available.

AtomCloud's platform for unified data vastly reduces the time and resources required to test cross-data-stream hypotheses and can effortlessly expand analysis to test hypotheses and combinations of variables not considered by human investigators. This has the potential to significantly accelerate pattern discovery, materials engineering, and process control.

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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