Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Gleason, Samuel P.; Rakowski, Alexander; Ribet, Stephanie M.; Zeltmann, Steven E.; Savitzky, Benjamin H.; Henderson, Matthew; Ciston, Jim; Ophus, Colin

doi:10.1103/physrevmaterials.8.093802

Title: Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Journal Article · 25 September 2024 · Physical Review Materials

DOI: https://doi.org/10.1103/physrevmaterials.8.093802 · OSTI ID:2480305

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^[2]; Henderson, Matthew ^[2];

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Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry; University of California, Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
Cornell Univ., Ithaca, NY (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry; Stanford Univ., CA (United States)

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2480305

Journal Information:: Physical Review Materials, Journal Name: Physical Review Materials Journal Issue: 9 Vol. 8; ISSN 2475-9953

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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