Curated Training Sets for Innovative AI Predictions

A large diversified chemical company was seeking to use machine learning methods to develop a novel electronic device with specific, innovative properties. While the company had collected a relatively large volume of data that could be used to begin training their predictive models, the team quickly realized that they did not have the needed training sets internally to support full model development.  The model struggled to predict properties used elsewhere in the industry due to the bias from their internal assets. To expand the model’s narrow focus, diverse scientific data was required, yet the required training sets were not available internally and difficult to find. 

Utilizing only internal data can lead to the lack of specific electronic, structure and device properties required to generate innovative machine learning predictions.  Learn how one company turned to custom-curated data to expand a model’s view of the scientific landscape.

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