Combining supervised and unsupervised machine learning for materials classification

DIPC Seminars

Andreas Leitherer, OMAD Laboratory at the FHI of the Max-Planck- Gesellschaft and IRIS-Adlershof of the Humboldt- Universität zu Berlin
Donostia International Physics Center
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Combining supervised and unsupervised machine learning for materials classification AI is driving a paradigm shift in various social and scientific areas, including condensed matter physics and materials science. Given the large and growing amount of information provided in computational databases (millions of entries) [1] and high-resolution experiments [2, 3], automatic analytical tools need to be developed. In this talk, we use machine learning to address a crucial step in the characterization of any material: the classification of its crystal geometry. Specifically, we introduce ARISE [4], a crystal- structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat – in automatic fashion - more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian deep-learning model allows to obtain principled uncertainty estimates. Notably, these are found to be correlated with crystalline order, which is demonstrated by analyzing crystal growth in experimental, time-resolved measurements of metallic nanoparticles. We also discuss a connection to unsupervised learning, allowing exploration of materials dataspaces: the neural-network model automatically learns data representations that contain information on structurally diverse crystal geometries. Using state-of-the-art clustering, physically meaningful subgroups can be identified in the neural-network latent space, which are shown, e.g., to correspond to distinct, experimentally verified grain-boundary phases [3]. Moreover, dimension- reduction analysis allows us to create low-dimensional, interpretable materials charts that visualize complex (i.e., single-, poly-, quasi-crystalline and amorphous) data from both theoretical and experimental origin [2, 3]. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments. [1] C. Draxl, M. Scheffler, MRS Bull. 43, 676-682 (2018) [2] Y. Yang et al. Nature 592, 60 (2021) [3] T. Meiners, T. Frolov, R.E. Rudd, et al. Nature 579, 375– 378 (2020) [4] A. Leitherer, A. Ziletti and L. M. Ghiringhelli. Nat. Commun. 12, 6234 (2021) Host: Roma Orus