Scientific output
=================

Scientific papers that used AtomAI or its predecessor, `AICrystallographer <https://github.com/pycroscopy/AICrystallographer>`_. Note that there are also multiple arXiv preprints that use the package which are not on this list.

1. Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy. *Adv. Sci.* 2203957 (2022). DOI: 10.1002/advs.202203957

2. Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline. *npj Comput Mater* 8, 74 (2022). DOI: 10.1038/s41524-022-00733-7

3. 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

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

5. 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

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

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

8. 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

9. 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

10. 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

11. 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

12. 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