6 papers
Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compres…
Machine Learning Inversion from Small-Angle Scattering for Charged Polymers
Lijie Ding, Chi-Huan Tung, Jan-Michael Y. Carrillo +2
We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, whic…
Elongated particles in flow: Commentary on small angle scattering investigations
Guan-Rong Huang, Lionel Porcar, Ryan P. Murphy +7
This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution…
Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks
Chi-Huan Tung, Lijie Ding, Ming-Ching Chang +8
Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in struct…
Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering
Lijie Ding, Chi-Huan Tung, Zhiqiang Cao +5
Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applicati…
Machine Learning Inversion from Scattering for Mechanically Driven Polymers
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include…