Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning

Chi Huan Tung, Meng Zhe Chen, Hsin Lung Chen, Guan Rong Huang, Lionel Porcar, Ming Ching Chang, Jan Michael Carrillo, Yangyang Wang, Bobby G. Sumpter, Yuya Shinohara, Changwoo Do, Wei Ren Chen

Research output: Contribution to journalArticlepeer-review

Abstract

An innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

Original languageEnglish
Pages (from-to)1047-1058
Number of pages12
JournalJournal of Applied Crystallography
Volume57
Issue numberPt 4
DOIs
StatePublished - Aug 1 2024

Keywords

  • charge-stabilized colloids
  • deep learning
  • potential inversion
  • small-angle scattering
  • structure factors

Fingerprint

Dive into the research topics of 'Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning'. Together they form a unique fingerprint.

Cite this