  We outline a machine learning strategy for determining the effective
interaction in the condensed phases of matter using scattering. Via a case
study of colloidal suspensions, we showed that the effective potential can be
probabilistically inferred from the scattering spectra without any restriction
imposed by model assumptions. Comparisons to existing parametric approaches
demonstrate the superior performance of this method in accuracy, efficiency,
and applicability. This method can effectively enable quantification of
interaction in highly correlated systems using scattering and diffraction
experiments.
