Determining the number of training points required for machine learning of potential energy surfaces. - Pearson, Matt (2021)
In recent years, there has been an explosion in the use of machine learning, with applications across many fields. One application of interest to the computational chemistry field is the use of a method known as Gaussian processes to accurately derive a system's Potential Energy Surfaces (PES) from ab-initio input-output data. Gaussian processes are a stochastic process, or collection of data, each finite group of which has a multivariate distribution.