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AI-enabled nanoscale imaging that is >100X faster and requires 25X less dose than traditional methods.

Scientific applications include full-scale 3D brain and chip imaging, catalysis, energy storage and conversion etc.

References:

Yao, Y., Chan, H., Sankaranarayanan, S., Balaprakash, P., Harder, R. J., & Cherukara, M. J. (2022). AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging. npj Computational Materials, 8(1), 1-8.
Kim, J. W., Cherukara, M. J., Tripathi, A., Jiang, Z., & Wang, J. (2021). Inversion of coherent surface scattering images via deep learning network. Applied Physics Letters, 119(19), 191601.
Zhou, T., Cherukara, M., & Phatak, C. (2021). Differential programming enabled functional imaging with Lorentz transmission electron microscopy. npj Computational Materials, 7(1), 1-8.
Chan, H., Nashed, Y. S., Kandel, S., Hruszkewycz, S. O., Sankaranarayanan, S. K., Harder, R. J., & Cherukara, M. J. (2021). Rapid 3D nanoscale coherent imaging via physics-aware deep learning. Applied Physics Reviews, 8(2), 021407.
1. Editor’s feature article and highlighted by AIP: https://aip.scitation.org/doi/10.1063/10.0005083
Cherukara, M. J., Zhou, T., Nashed, Y., Enfedaque, P., Hexemer, A., Harder, R. J., & Holt, M. V. (2020). AI-enabled high-resolution scanning coherent diffraction imaging. Applied Physics Letters, 117(4), 044103.
Cherukara, M. J., Nashed, Y. S., & Harder, R. J. (2018). Real-time coherent diffraction inversion using deep generative networks. Scientific reports, 8(1), 1-8.