A Platform for Scalable
Pattern Discovery.
Efficiently uncover patterns and relationships across multiple data sources.
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](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FUnifiedDataStreams2.bc92f076.png&w=3840&q=75)
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](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FRoughnessGrowthConditionPlot2.485a371d.png&w=1920&q=75)
![RHEED fingerprint](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FRoughnessGrowthCondition2.bc246432.png&w=1920&q=75)
Enabling Advanced Computational Techniques
Small datasets: bootstrap aggregation with linear and nonlinear models for classification and regression.
![Image of a RHEED pattern](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FBootstrapAggregation.bbab7973.png&w=3840&q=75)
![RHEED fingerprint](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FBootstrapRegression.c409d354.png&w=3840&q=75)
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](/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FBayesianOptimization.49a33a26.png&w=3840&q=75)
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.
AtomCloud helps operators extract insights from RHEED patterns across broad material systems.
Try AtomCloud yourself.
Book a demo to see how AtomCloud can scale your pattern discovery.