Wong Kelvin, Qi Runzhang, Yang Ye, Luo Zhi, Guldin Stefan, Butler Keith T
Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
JACS Au. 2024 Sep 9;4(9):3492-3500. doi: 10.1021/jacsau.4c00368. eCollection 2024 Sep 23.
Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency ( ) and the Debye length (κ) for a given SAXS profile. This ANN, which was trained on 200,000 simulated SAXS curves, was able to predict values of and κ for a test set containing 25,000 simulated SAXS curves, where most predicted values had errors smaller than 20%. Subsequently, an ANN was used as a surrogate model in a Markov chain Monte Carlo sampling algorithm to obtain maximum a posteriori estimates of and κ, as well as the associated confidence intervals and correlations between and κ for an experimentally obtained SAXS profile.
小角X射线散射（SAXS）是一种表征技术，它通过将SAXS谱的结构因子与选定的模型及封闭关系进行拟合，来研究胶体相互作用。然而，这种方法的适用性受到可解析拟合的现有模型数量有限以及模型有效运行范围较窄的限制。在这项工作中，我们展示了一个概念验证，即使用在从蒙特卡罗（MC）模拟获得的SAXS曲线上训练的人工神经网络（ANN），来预测给定SAXS谱的有效大离子价（ ）和德拜长度（κ）的值。这个在200,000条模拟SAXS曲线上训练的ANN，能够预测包含25,000条模拟SAXS曲线的测试集的 和κ值，其中大多数预测值的误差小于20%。随后，一个ANN被用作马尔可夫链蒙特卡罗采样算法中的替代模型，以获得实验得到的SAXS谱的 和κ的最大后验估计值，以及 和κ之间的相关置信区间和相关性。
