There is a need to increase the performance and longevity of dental composites and accelerate the translation of novel composites to the market. This study explores the use of artificial intelligence (AI), specifically machine learning (ML) models, to predict the performance outcomes (POs) of dental composites from their composite attributes (CAs). A comprehensive dataset was carefully curated and refined from 200+ publications. Then ML models were trained for prediction of discrete POs and their performance evaluated. Also five models were employed for regression of continuous POs . We observed that the performance of ML models depends on the predicted PO. For example the k-nearest neighbor algorithm (KNN) model excelled in predicting flexural modulus (FlexMod), while Decision Tree model excelled in predicting flexural strength (FlexStr) and volumetric shrinkage (ShrinkV). . The results show that TEGDMA is a key contributor to FlexMod and ShrinkV, while BisGMA and UDMA to FlexStr. We also observed thatdepth of cure, degree of monomer-to-polymer conversion, and filler loading are main contributors for prediction of ShrinkStr. Overall, in this work we demonstrate the feasibility of the use of ML models to to support the development of new dental materials.

The authors have declared no competing interest.

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All data produced in the present study are available upon reasonable request to the authors