Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10795-2026
https://doi.org/10.5194/gmd-19-10795-2026
Model description paper
 | 
13 Nov 2026
Model description paper |  | 13 Nov 2026

DDA-BNN v1.0: a morphology-aware surrogate model for the optical properties of black carbon-containing particles

Payton Beeler, Sam Donald, and Laura Fierce

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Cited articles

Beeler, P. and Chakrabarty, R. K.: Constraining the particle-scale diversity of black carbon light absorption using a unified framework, Atmos. Chem. Phys., 22, 14825–14836, https://doi.org/10.5194/acp-22-14825-2022, 2022. a, b, c, d, e, f
Beeler, P., Corbin, J. C., Sipkens, T. A., and Fierce, L.: A Framework for Quantifying the Size and Fractal Dimension of Compacting Soot Particles, Environ. Sci. Technol., 59, 5994–6003, 2025. a, b, c, d, e, f, g, h
Beeler, P., Donald, S., and Fierce, L.: Training dataset for DDA-BNN v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.19324185, 2026a. a
Beeler, P., Donald, S., and Fierce, L.: DDA-BNN v1.0: A Morphology-Aware Surrogate Model for the Optical Properties of Black Carbon–Containing Particles, Zenodo [code], https://doi.org/10.5281/zenodo.21478183, 2026b (code also available: https://github.com/pnnl/DDA-BNN, last access: 12 October 2026). a, b
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D.: Weight uncertainty in neural networks, in: International Conference on Machine Learning, PMLR, 1613–1622, https://proceedings.mlr.press/v37/blundell15.html (last access: 12 October 2026), 2015. a
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We developed a surrogate model to predict how black carbon particles absorb and scatter light based on their size, shape, and mixing with other aerosol species. The model was trained on detailed physics simulations and also estimates uncertainty caused by limited training data and unresolved internal structures. It outperformed common simplified particle assumptions, especially for scattering, and can help target new simulations to improve predictions of black carbon's radiative effects.
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