Artificial Intelligence Applied to the Prediction of Organic Materials
Steven Bennett, Andrew Tarzia, Martijn A. Zwijnenburg, Kim E. Jelfs
Abstract
Artificial intelligence is beginning to significantly increase the rate at which new materials are discovered, by influencing almost all aspects of the materials design process, especially structure and property prediction. Embracing more efficient, data-driven approaches has the potential to significantly increase the number of organic materials that can be screened for useful applications. However, there are various challenges, including representing extended materials in a machine-readable format and obtaining sufficient amounts of training data to generate useful predictive models. This chapter discusses some of the key artificial intelligence techniques that have been applied to organic material prediction and discovery and covers examples of the application of artificial intelligence to the fields of porous organic materials, organic electronics, and organic systems with other desired physical properties.