ML is an approach designed to identify patterns in data and make predictions of behavior under untested conditions. At Sunthetics, we leverage proprietary algorithms in a modified Bayesian Optimization approach to learn from your data in the most efficient way. Our algorithms run millions of scenarios representing potential experiments to guide you through the optimization path. In other words, it saves you time and resources, minimizing frustration in R&D!

We take your data, suggest 1+ subsequent experiment(s) to map your system, and achieve your optimization goals up to 32x faster!

ML predictive algorithms are as accurate as the quality of the data provided for model training. However, Sunthetics’ ML algorithms autocorrect themselves. We learn from your initial data and suggest subsequent experiments designed to confirm, adapt, and correct predicted trends. The data you collect is smartly selected to fast-track the mapping of outliers and hidden optimal areas of operation.

Any kind! SuntheticsML is built to be reaction-agnostic. Our tool learns from the reaction/system at hand—the initial dataset you provide and subsequent experiments that the algorithms suggest.

Our algorithms have been used with electrochemical reactions, catalytic processes, formulation optimization, prediction of material properties, mechanochemistry, photochemistry, and much more. Look at our summary of case studies and contact us if you have questions about a particular research area!

No,SuntheticsML is designed to leverage information from very small datasets, which differentiates us from other optimization platforms. Users commonly start using our algorithms with only 5 data points.

Absolutely! We implement the best practices to protect your data.

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.

Our cloud-based software is designed to be an AI partner available to the researcher at all times. However, if you do not wish to use the software yourself, we can assign one of our engineers to your project. They will run all optimizations for you and assist you through the process.

SuntheticsML is based on a bayesian optimization approach that guides experimentation to reduce the number of experiments required. Statistical 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 improving 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.