Deep Learning the Small-Angle Scattering of Polydisperse Hard Rods

TL;DR

This paper introduces a deep learning framework using variational autoencoders to accurately model small-angle scattering data of polydisperse hard-rod systems, surpassing traditional analytical models in precision.

Contribution

The study develops a neural network approach trained on Monte Carlo simulation data to predict scattering functions, demonstrating improved accuracy and generality over existing analytical methods.

Findings

Neural network accurately predicts scattering functions for polydisperse rods.

Framework outperforms traditional Percus-Yevick approximation.

Model generalizes across different polydisperse distributions.

Abstract

We present a deep learning framework for modeling and analyzing the small-angle scattering data of polydisperse hard-rod systems, a widely used models for anisotropic colloidal particles. We use a variational autoencoder-based neural network to learn the mapping from the system parameters such as the volume fraction, rod length, and polydispersity, to the scattering function. The dataset for training and testing such neural network model is obtained from Markov chain Monte Carlo simulation of 20,000 hard spherocylinders using the hard particle Monte Carlo package from the HOOMD-blue. Four datasets were generated, each with 5,500 pairs of system parameters and corresponding scattering functions. We use one of the dataset to investigate the feasibility of the learning, and three additional datasets with different polydisperse distribution to demonstrate the generality of our approach. The neural network model transcends the fundamental limitations of the Percus-Yevick approximation by accurately capturing anisotropic interactions and high-concentration effects that analytical models often fail to resolve. This framework achieves significantly higher accuracy in reproducing scattering functions and enables a least-square fitting routine for quantitative data analysis.