Empirical (EM) approaches stand in contrast to those described above, which are primarily based on a rigorous free energy formalism. Empirical methods tend to rely on sets of approximate functional forms (e.g., hydrogen bonds) with knowledge-based parameters that are optimized based on large training sets of measured pK a values. Such approaches have generated predictors with impressive accuracy at low computational cost , which have been further enhanced with the advent of machine learning .