The doctoral thesis of Samuel Tovey. In this work, I explore the role machine learning plays in computational physics, specifically, the fitting of potential for molecular dynamics simulations and control of microscopic active matter. Further, it is shown that physics concepts can be used to understand machine learning, particularly the role of data in neural network training and the evolution and learning mechanisms of neural networks while training.
