A simple and fast analysis method to sort large data sets into groups with shared distinguishing characteristics is described and applied to single molecular break junction conductance versus electrode displacement data. The method, based on principal component analysis, successfully sorts data sets based on the projection of the data onto the first or second principal component of the correlation matrix without the need to assert any specific hypothesis about the expected features within the data. This is an improvement on the current correlation matrix analysis approach because it sorts data automatically, making it more objective and less time consuming, and our method is applicable to a wide range of multivariate data sets. Here the method is demonstrated on two systems. First, it is demonstrated on mixtures of two molecules with identical anchor groups and similar lengths, but either a $\pi$ (high conductance) or a $\sigma$ (low conductance) bridge. The mixed data are automatically sorted into two groups containing one molecule or the other. Second, it is demonstrated on break junction data measured with the $\pi$ bridged molecule alone. Again, the method distinguishes between two groups. These groups are tentatively assigned to different geometries of the molecule in the junction.

• Received 21 June 2017

DOI:https://doi.org/10.1103/PhysRevLett.120.016601