  The study explores machine learning methods for revealing chemical
sensitivity in Helium spin-echo spectroscopy, in order to obtain
ultra-sensitive surface analytic technique. We model bi-species co-adsorbed
systems and demonstrate that by using deep-learning neural-networks partial
surface concentrations are obtainable. An example system of particles with mass
50 and 100 a.m.u was tested with characteristic inter-adsorbate and
adsorbate-substrate interactions, with partial surface concentrations being
resolvable up to 20% occupancy of adsorption sites, and with modestly high
noise level of 4%.
