2 research outputs found
Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water
Machine-learning (ML) has become a key workhorse in molecular simulations.
Building an ML model in this context, involves encoding the information of
chemical environments using local atomic descriptors. In this work, we focus on
the Smooth Overlap of Atomic Positions (SOAP) and their application in studying
the properties of liquid water both in the bulk and at the hydrophobic
air-water interface. By using a statistical test aimed at assessing the
relative information content of different distance measures defined on the same
data space, we investigate if these descriptors provide the same information as
some of the common order parameters that are used to characterize local water
structure such as hydrogen bonding, density or tetrahedrality to name a few.
Our analysis suggests that the ML description and the standard order parameters
of local water structure are not equivalent. In particular, a combination of
these order parameters probing local water environments can predict SOAP
similarity only approximately, and viceversa, the environments that are similar
according to SOAP are not necessarily similar according to the standard order
parameters. We also elucidate the role of some of the metaparameters entering
in the SOAP definition in encoding chemical information