Scientific output

Scientific papers that used AtomAI or its predecessor, AICrystallographer:

  1. 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

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

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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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