A lot of progress has been made in recent years in the development of machine learning (ML) potentials for atomistic simulations [1]. Neural network potentials (NNPs), which have been introduced more than two decades ago [2], are an important class of ML potentials. While the first generation of NNPs has been restricted to small molecules with only a few degrees of freedom, the...
Traditionally the “best” observations are those with the largest signal from the most tightly controlled systems. In a wide range of phenomena – the dance of proteins in function, femtosecond breaking of molecular bonds, the gestation of fetuses – tight control is neither feasible, nor desirable. Modern machine-learning techniques extract far more information from sparse random sightings...
Gaussian process regression (GPR) is a kernel-based regression tool with intrinsic uncertainty estimation, which makes it well-suited to natural science datasets. In Bayesian optimization, GPR is coupled with acquisition functions for an active learning approach, where models are iteratively refined by addition of new data points with high information content. This tutorial will use the BOSS...
In this tutorial, we introduce the AI technique of symbolic regression, combined with compressed sensing for the identification of compact, interpretable models.
Specifically, we introduce the Sure-Independence Screening and Sparsifying Operator (SISSO), together with its recent variants.
The methodology starts from a set of candidate features, provided by the user, and it builds a tree of...
