Abstract
The United States Environmental Protection Agency's Chemical Transformation Simulator (CTS) platform implemented the first freely available reaction library to predict direct photolysis products of organic contaminants in aquatic systems. However, the initial version of the reaction library did not differentiate the formation likelihood of each predicted product, and therefore, the number of predicted products that are not observed tended to exponentially increase with the prediction generation. To alleviate this problem, we first employed relative reasoning algorithms to remove unlikely products. We then ranked different reaction schemes according to their transformation kinetics and removed slowly forming products. Applying the two strategies improved the precision (the percentage of correctly predicted products over all predicted products) by 34% and 53% for the internal evaluation set and the external evaluation set, respectively, when products from three generations were considered. This improved library also revealed new research directions to improve predictions of the dominant phototransformation products.
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