标题： 使用深度学习解密多分散硬球的散射 显示英文标题

标题： Deciphering the Scattering of Polydisperse Hard Spheres using Deep Learning

摘要： 我们引入一种深度学习方法来分析多分散硬球系统的散射函数。 我们使用基于变分自编码器的神经网络来学习散射函数与系统参数（包括体积分数和多分散性）之间的双向映射。 这样训练好的模型既可以作为生成器，从系统参数生成散射函数，也可以作为推断器，从散射函数中提取系统参数。 我们首先通过进行由截断-平移的Lennard-Jones模型描述的多分散硬球系统的分子动力学模拟来生成散射数据集，然后使用奇异值分解分析散射函数数据集以确认维度压缩的可行性。 然后我们将数据集分为训练集和测试集，并仅在训练集上训练我们的神经网络。 我们的生成器模型生成的散射函数精度显著高于传统的Percus-Yevick近似和$\beta$修正，而推断器模型提取体积分数和多分散性的精度也比传统模型函数高得多。 显示英文摘要

摘要： We introduce a deep learning approach for analyzing the scattering function of the polydisperse hard spheres system. We use a variational autoencoder-based neural network to learn the bidirectional mapping between the scattering function and the system parameters including the volume fraction and polydispersity. Such that the trained model serves both as a generator that produce scattering function from the system parameters, and an inferrer that extract system parameters from the scattering function. We first generate a scattering dataset by carrying out molecular dynamics simulation of the polydisperse hard spheres modeled by the truncated-shifted Lennard-Jones model, then analyze the scattering function dataset using singular value decomposition to confirm the feasibility of dimensional compression. Then we split the dataset into training and testing set and train our neural network on the training set only. Our generator model produce scattering function with significant higher accuracy comparing to the traditional Percus-Yevick approximation and $\beta$ correction, and the inferrer model can extract the volume fraction and polydispersity with much higher accuracy than traditional model functions.