10 citations as recorded by crossref.

1. An Adaptive Auto‐Reduction Solver for Speeding Up Integration of Chemical Kinetics in Atmospheric Chemistry Models: Implementation and Evaluation in the Kinetic Pre‐Processor (KPP) Version 3.0.0 H. Lin et al. https://doi.org/10.1029/2022MS003293
2. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
3. Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models K. Bates et al. https://doi.org/10.5194/acp-21-18351-2021
4. Guaranteed Mass-Conserving Deep Learning Model for Stiff Kinetics in Atmospheric Chemistry H. Feng et al. https://doi.org/10.1021/acsearthspacechem.6c00004
5. A Neural Network-Assisted Euler Integrator for Stiff Kinetics in Atmospheric Chemistry Y. Huang & J. Seinfeld https://doi.org/10.1021/acs.est.1c07648
6. Performance of current atmospheric observation networks on quantifying sub-continental emissions of halogenated gases X. Hu et al. https://doi.org/10.1016/j.atmosenv.2026.122118
7. A deep learning model for predicting daily PM 2.5 concentration in response to emission reduction S. Liu et al. https://doi.org/10.1126/sciadv.aef5759
8. A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1 L. Shen et al. https://doi.org/10.5194/gmd-15-1677-2022
9. An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry M. Kelp et al. https://doi.org/10.1029/2021MS002926
10. Reduced-Precision Chemical Kinetics in Atmospheric Models K. Sophocleous & T. Christoudias https://doi.org/10.3390/atmos13091418