Data for paper "Automated Structure Discovery for Scanning Tunneling Microscopy"

Data for paper "Automated Structure Discovery for Scanning Tunneling Microscopy"

Description

Contents of the dataset: band.h5 -- keys are molecule indices, each molecule has the following keys: eigs: KS eigenvalues for each state coefs: KS eigenvectors for each basis set xyz: atomic positions Z: atomic species qs: mulliken point charges rotations_210611.pickle -- keys train/val/test Each set is a dict containing id -- rotation pairs rotations are 3x3 numpy arrays disks.pt -- a pretrained model for Atomic Disks predictions
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Year of publication

2024

Authors

Department of Applied Physics

Lauri Kurki - Creator

Niko Oinonen Orcid -palvelun logo - Creator

Zenodo - Publisher

Other information

Fields of science

Physical sciences

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)