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Complicated S-Au interaction patterns in thiolate-protected gold nanoclusters(TP-AuNCs) are important in the formation of versatility among clusters. Inthis study, the ELFnet, a novel convolutional neural network (CNN) model istrained and tested to bridge the electron localization function (ELF) imagesand the bond lengths. As believed to be a successful bond describer, a datasetof 3959 ELF images is obtained out of Density Functional Theory (DFT)simulations, followed by a series of model architecture and hyperparameterexploration. The ELFnet with its best performance shows its correct featureextraction ability to gain insights in the prediction of bond lengths. Besides,the ELFnet also possess 3 different "ELF reading modes" which inspires otherpossibilities in understanding ELF images. The ELFnet serves as an "ELF-ruler"to give precise bond type labels (or the predicted bond lengths). SuchELF-ruler measured bond types represent a particular bond when distorted byexternal factors with a quantitative indicator. With the help of such anELF-ruler, the classification of chemical bonds in general may be extended tomore precise types.

Build a Ruler to Measure Electron Localization Function with Machine  Learning Models: S-Au bonds Quantification in Thiolate Protected Gold  Nanoclusters