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ACKNOWLEDGMENTS

We thank E. Chin of the Scripps Institute of Oceanography and her

students for their assistance collecting and identifying mineral

and geological samples.Funding:Supported by the U.S. Department

of Defense (DoD) [through the National Defense Science and

Engineering Graduate Fellowship (NDSEG) Program] and the ARCS

Foundation, San Diego Chapter (K.K.); the Joint DoD/Department

of Energy Munitions Technology Development Program and the

Dynamic Materials Science Campaign at LANL (C.Z.); and the Oerlikon

Group (K.S.V.).Author contributions:K.K. assisted in developing

the idea, performed the bulk of the experimental work, worked on

later versions of the neural network python code, and prepared

the initial draft of the manuscript and figures; C.Z. assisted with the

development of the idea into a scientific study, contributed his

knowledge of EBSD, assisted with the figures, and developed

MATLAB code for phase-mapping the machine-learning predictions

and pattern database management; A.S.R. developed the initial

python code for implementing the neural networks; A.S.R. and D.M.

managed the deployment of Grad-CAM tools and assisted in the

analysis of the results from the deep learning models; T.J.H.

assisted K.K. with materials selection, fabrication, and processing;

T.J.H. helped focus the research direction; E.M. assisted K.K. with

materials fabrication, processing, and analysis; K.S.V. led the

development of the idea, guided the focus of the project, and

reviewed and revised the manuscript; and all authors participated

in analyzing and interpreting the final data and contributed to

the discussions and revisions of the manuscript.Competing

interests:The authors declare no competing interests.Data and

materials availability:All data and models generated during

and/or analyzed during the current study are available from the

corresponding author upon reasonable request. The python code

for implementing these models with Keras and Tensorflow is

available at https://github.com/krkaufma/Electron-Diffraction-

CNN, Zenodo (DOI: 10.5281/zenodo.3564937) and from the

corresponding author upon request. The diffraction patterns

analyzed during the current study have not yet been deposited in

a publicly available repository because of the sheer size of the

library (nearly 3 terabytes). This is an ongoing effort and until then,

they are available upon reasonable request.

SUPPLEMENTARY MATERIALS

science.sciencemag.org/content/367/6477/564/suppl/DC1

Materials and Methods

Supplementary Text

Table S1

Figs. S1 to S10

References ( 58 – 65 )

6 June 2019; accepted 23 December 2019

10.1126/science.aay3062

Kaufmannet al.,Science 367 , 564–568 (2020) 31 January 2020 5of5

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