Inferring colloidal interaction from scattering by machine learning
Chi-Huan Tung, Shou-Yi Chang, Ming‐Ching Chang, Jan‐Michael Y. Carrillo, Bobby G. Sumpter, Changwoo Do, Wei‐Ren Chen
Abstract
A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which extracts the targeted static two-point correlation functions from experimentally measured scattering cross sections, and a Gaussian process framework which probabilistically infers the relevant structural parameters from the inverted correlation functions. Via a case study of charged colloidal suspensions, the feasibility of this approach for quantitative study of molecular interaction is critically benchmarked and its merit over existing deterministic approaches, in terms of numerical accuracy and computationally efficiency, is demonstrated.