Biogeochemistry-Informed Neural Network (BINN v1.0) for improving accuracy of model prediction and scientific understanding of soil organic carbon storage

Xu, Haodi; Fan, Joshua; Tao, Feng; Jiang, Lifen; You, Fengqi; Houlton, Benjamin; Sun, Ying; Gomes, Carla P.; Luo, Yiqi

doi:10.5194/gmd-19-6777-2026

Articles | Volume 19, issue 14

https://doi.org/10.5194/gmd-19-6777-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-6777-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 14

Development and technical paper

|

24 Jul 2026

Development and technical paper |

| 24 Jul 2026

Biogeochemistry-Informed Neural Network (BINN v1.0) for improving accuracy of model prediction and scientific understanding of soil organic carbon storage

Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Houlton, Ying Sun, Carla P. Gomes, and Yiqi Luo

Supplement

https://doi.org/10.5194/gmd-19-6777-2026-supplement

Data sets

BINN: v1.0.1 Hardyxu8067 and joshuafan https://doi.org/10.5281/zenodo.20405976

Model code and software

BINN: v1.0.1 Hardyxu8067 and joshuafan https://doi.org/10.5281/zenodo.20405976

Short summary

We developed the Biogeochemistry-Informed Neural Network (BINN) which embeds a process-based model inside an AI framework so the model’s parameters can be learned from empirical data. BINN could recover prescribed parameters in synthetic tests and retrieve key processes controlling the modeled soil organic carbon when applied to ~25 000 soil profiles in US. It operates more than 50 times faster than the traditional Bayesian approach for data-model integration.