Pretrained weights for the machine learning models used in the paper "Electrostatic Discovery Atomic Force Microscopy". The weights are stored in the Pytorch .pth format. Several sets of weights are provided: base: The base model used for all predictions in the main paper and used for comparison in the various test in the supplementary information of the paper. single-channel: Model trained on only a single CO-tip AFM input. CO-Cl: Model trained on alternative tip combination of CO and Cl. Xe-Cl: Model trained on alternative tip combination of Xe and Cl. constant-noise: Model trained using constant noise amplitude instead of normally distributed amplitude. uniform-noise: Model trained using uniform random noise amplitude instead of normally distributed amplitude. no-gradient: Model trained without background-gradient augmentation. matched-tips: Model trained on data with matched tip distance between CO and Xe, instead of independently randomized distances.

2021