ABSTRACT
The development of nanotechnology has led to the exploration and discovery of nanomaterials increasing at an exponential rate. This growth presents the challenge of accurately predicting the characterization of nanomaterials at a faster pace. Machine learning (ML), encompassing both traditional ML and deep learning tools, has the ability to address these challenges. This chapter explores the use of several ML models – including artificial neural networks (ANN), support vector machines (SVM), decision trees, convolutional neural networks (CNN), and deep neural networks (DNN) – to successfully predict various nanomaterial properties based on experimental data. These models are meticulously discussed, as well as the details of how they are utilized to predict the physicochemical properties of nanomaterials. Additionally, the challenges currently faced in using these ML tools are examined, along with discussions on the changes that must take place in the near future for ML to make a significant contribution to nanomaterial synthesis.
