Scientific output

Scientific papers that used AtomAI or its predecessor, AICrystallographer. Note that there are also multiple arXiv preprints that use the package which are not on this list.

  1. Exploring causal physical mechanisms via non-Gaussian linear models and deep kernel learning: applications for ferroelectric domain structures. ACS Nano 16, 1250-1259 (2022). DOI: 10.1021/acsnano.1c09059

  2. Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning. Advanced Materials 33, 2103680 (2021). DOI: 10.1002/adma.202103680

  3. Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy. npj Comput Mater 7, 100 (2021). DOI: 10.1038/s41524-021-00569-7

  4. Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis. Soft Matter (2021). DOI: 10.1039/D1SM00645B

  5. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. Science Advances 7, eabd5084 (2021). DOI: 10.1126/sciadv.abd5084

  6. Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations. ACS Nano 15, 6471–6480 (2021). DOI: 10.1021/acsnano.0c08914

  7. Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning. Nanotechnology 32, 035703 (2020). DOI: 10.1088/1361-6528/abb8a6

  8. Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study. Science Advances 5, eaaw8989 (2019). DOI: 10.1126/sciadv.aaw8989

  9. Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions. Applied Physics Letters 115, 052902 (2019). DOI: 10.1063/1.5109520

  10. Lab on a beam—Big data and artificial intelligence in scanning transmission electron microscopy. MRS Bull. 44, 565–575 (2019). DOI: 10.1557/mrs.2019.159