DOE OSTI · 3028231
Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology
Abstract
A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.
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Scheinker, Alexander [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000232030963), McBride, Michael Anthony [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000252747275), Brown, Nathan Michael Ballen [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000580999161), Mirabal, Alex James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245017602), Pokharel, Reeju [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000205636142), Hooks, Daniel Edwin [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000172536689). 2026-01-21. Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology. https://doi.org/10.1149/1945-7111%2Fae36fb
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