Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling

Wong, Kelvin1; Qi, Runzhang2,3; Yang, Ye1,3; Luo, Zhi4; Guldin, Stefan1,6,7; Butler, Keith T.5

2024-09-01

10.1021/jacsau.4c00368

JACS AU   影响因子和分区

2691-3704

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 (Z(eff)) and the Debye length (kappa(-1)) for a given SAXS profile. This ANN, which was trained on 200,000 simulated SAXS curves, was able to predict values of Z(eff) and kappa(-1) 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 Z(eff) and kappa(-1), as well as the associated confidence intervals and correlations between Z(eff) and kappa(-1) for an experimentally obtained SAXS profile.

small-angle scattering structure factor colloidalinteractions machine learning Markov chain MonteCarlo sampling

ESCI

英语

其他

Engineering and Physical Sciences Research Council["EP/R513143/1","EP/W524335/1","EP/Y000552/1","EP/Y014405/1"] ; NSF[DMR-0520547] ; European Union's Horizon 2020 research and innovation programme under the SINE2020 Project[654000] ; EPSRC[EP/X035859/1]

Chemistry

Chemistry, Multidisciplinary

WOS:001309475000001

AMER CHEMICAL SOC

Web of Science

1.UCL, Dept Chem Engn, London WC1E 7JE, England
2.Univ Cambridge, Ctr Misfolding Dis, Yusuf Hamied Dept Chem, Cambridge CB2 1EW, England
3.Langmu Bio, Yuhang 311112, Hangzhou, Peoples R China
4.Southern Univ Sci & Technol, Dept Biomed Engn, Guangdong Prov Key Lab Adv Biomat, Shenzhen 518055, Peoples R China
5.UCL, Dept Chem, London WC1E 6BS, England
6.Tech Univ Munich, Dept Life Sci Engn, D-85354 Freising Weihenstephan, Germany
7.TUMCREATE, Singapore 138602, Singapore

Wong, Kelvin,Qi, Runzhang,Yang, Ye,et al. Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling[J]. JACS AU,2024.

Wong, Kelvin,Qi, Runzhang,Yang, Ye,Luo, Zhi,Guldin, Stefan,&Butler, Keith T..(2024).Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.JACS AU.

Wong, Kelvin,et al."Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling".JACS AU (2024).