  We propose an original approach to describe the scientific progress in a
quantitative way. Using innovative Machine Learning techniques we create a
vector representation for the PACS codes and we use them to represent the
relative movements of the various domains of Physics in a multi-dimensional
space. This methodology unveils about 25 years of scientific trends, enables us
to predict innovative couplings of fields, and illustrates how Nobel Prize
papers and APS milestones drive the future convergence of previously unrelated
fields.
