Sunthetics ML

Sunthetics ML is an easy-to-use machine learning (ML) platform that accelerates the optimization and development of formulations, products, and processes.

Leveraging a combination of chemical engineering concepts and ML predictive algorithms, we can use very few data points to quickly predict optimal formulations and process conditions for enhanced performance.

Our software can improve existing manufacturing processes, unlock unprecedented efficiencies, and it can be further used to identify anomalies and facilitate diagnostics. 

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- What types of reactions does the tool work with?
SuntheticsML is built to be reaction-agnostic. Our tool learns from the reaction/system at hand. Our algorithms can be used with electrochemical reactions, catalytic processes, formulation optimization, prediction of material properties, and much more. If you have a question about a particular kind of reaction, contact us!

- What type of variables does it take?
Our current platform takes 2-8 numerical variables (reaction parameters) to predict one target output variable. New features for higher dimensionality and the use of categorical features are under development!

- Do I need to know AI, programming, or statistics to use this?
No! Our tool is extremely easy to use. Users upload an excel or csv file with the data from a few experiments and our algorithms will do all the work.

- Do I need hundreds of data points to use ML?
No! Our tool is designed to leverage information from very small datasets. Users commonly start using our tool after running only 4-5 experiments

- Is this the same as DoE?

SuntheticsML is based on a bayesian optimization approach that guides experimentation to reduce the number of life experiments required. Traditional DoE can have limitations when modeling complex reactions that have multiple local mimina/maxima. SuntheticsML can map complex reaction spaces more effectively than traditional DoE, achieving higher performance with less experiments.

SuntheticsML is a stand-alone tool capable of improve scientists' productivity, guiding experimentation, and achieving unprecedented efficiencies. However, users can always complement their DoE-enabled analysis with our tool to enhance the accuracy of the models and predictions.