This bachelor thesis develops a four-stage machine-learning pipeline for pointwise conductance state classification in memristive current-voltage traces from mechanically controlled break junction experiments. The aim of this thesis is to improve data retention of hysteretic traces from the raw data set for downstream physics analysis despite an absence of ground truth pointwise state classifications and heterogeneous traces. The pipeline consists of an initial data filter, pseudo-labelling with a simple linear regression Hidden Markov Model (teacher), a Temporal Convolutional Network (student) capable of large-scale pointwise conductance classification, and a final hysteretic filter. The methodology is developed with data from three reference molecules expected to exhibit two-state behaviour, while two additional target molecules that are expected to show occasional three-conductance-state behaviour are used for comparison.

On the test set of unseen data, the Temporal Convolutional Network reproduces the teacher pseudo-labels with 98% pointwise agreement. The pipeline also identifies 8% of all traces in the available dataset as hysteretic, compared with the 5% resulting from the methodology used in previous work. Manual audit and plausibility analyses indicate that the increase in retained traces is achieved without losing physical plausibility for most molecules. Limitations include a lack of definitive ground truth and reduced robustness on complex three-conductance-state target molecules. ... Read more