Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement

Deriving transferable pair potentials from experimental
neutron
and X-ray scattering measurements has been a longstanding challenge
in condensed matter physics. State-of-the-art scattering analysis
techniques estimate real-space microstructure from reciprocal-space
total scattering data by refining pair potentials to obtain agreement
between simulated and experimental results. Prior attempts to apply
these potentials with molecular simulations have revealed inaccurate
predictions of thermodynamic fluid properties. In this Letter, a machine
learning assisted structure-inversion method applied to neutron scattering
patterns of the noble gases (Ne, Ar, Kr, and Xe) is shown to recover
transferable pair potentials that accurately reproduce both microstructure
and vapor–liquid equilibria from the triple to critical point.
Therefore, it is concluded that a single neutron scattering measurement
is sufficient to predict macroscopic thermodynamic properties over
a wide range of states and provide novel insight into local atomic
forces in dense monatomic systems.