Abstract
This chapter explores the utilization of artificial neural network (ANN) models in predicting surface energy values. ANN models are a type of machine learning (ML) algorithm inspired by the way the human brain processes information. The chapter delves into the theoretical foundations of ANN models and their application in modeling surface energy, a crucial parameter in various scientific and industrial processes. By training the ANN models with relevant datasets, researchers can develop a predictive model capable of estimating surface energy values with high accuracy. The chapter discusses the methodology, challenges, and potential benefits of using an ANN-based approach for surface energy prediction, offering insights into the intersection of artificial intelligence and materials science.