A machine learning inversion scheme for determining effective interaction of charged colloidal suspensions using scattering
ORAL
Abstract
We outline a machine learning strategy for determining the effective interaction of charged colloidal suspensions using scattering. 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.
*This research was performed at he Spallation Neutron Source and the Center for Nanophase Materials Sciences, which are DOE Office of Science User Facilities operated by ORNL. MD simulations used resources of the Oak Ridge Leadership Computing Facility, which is supported by DOE Office of Science under Contract DE-AC05-00OR22725.
–
Publication: M.-C. Chang, C.-H. Tung, S.-Y. Chang, J. M. Carrillo, Y. Wang, B. G. Sumpter, G.-R. Huang, C. Do, and W.-R. Chen, submitted. Manuscript is available at https://arxiv.org/abs/2103.14883
Presenters
-
Chi-Huan Tung
- Natl Tsing Hua Univ