17 citations as recorded by crossref.

1. Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0 P. Huang et al. https://doi.org/10.5194/gmd-16-4767-2023
2. Machine learning model predicting hydrothermal dolomitisation for future coupling of basin modelling and geochemical simulations N. Collard et al. https://doi.org/10.1016/j.chemgeo.2023.121676
3. Fast and accurate CO2 solubility predictions: A comparative study of machine learning models and the Duan and Sun models M. Khojastehmehr et al. https://doi.org/10.1016/j.petlm.2025.02.005
4. Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations Y. Kim et al. https://doi.org/10.9719/EEG.2024.57.5.487
5. Reactive transport model of kinetically controlled celestite to barite replacement M. Tranter et al. https://doi.org/10.5194/adgeo-56-57-2021
6. Verification of TRANSPORT Simulation Environment coupling with PHREEQC for reactive transport modelling T. Kempka et al. https://doi.org/10.5194/adgeo-58-19-2022
7. A Review of Thermal–Hydrological–Mechanical–Chemical Coupling Studies on the Geological Disposal of Radioactive Waste in Japan K. Aoyagi et al. https://doi.org/10.1007/s00603-026-05607-8
8. Improving the Performance of Reactive Transport Simulations Using Artificial Neural Networks E. Demirer et al. https://doi.org/10.1007/s11242-022-01856-7
9. Geochemistry and machine learning: methods and benchmarking N. Prasianakis et al. https://doi.org/10.1007/s12665-024-12066-3
10. EURAD state-of-the-art report: development and improvement of numerical methods and tools for modeling coupled processes in the field of nuclear waste disposal F. Claret et al. https://doi.org/10.3389/fnuen.2024.1437714
11. POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups M. De Lucia et al. https://doi.org/10.5194/gmd-14-7391-2021
12. Digitalisation for nuclear waste management: predisposal and disposal O. Kolditz et al. https://doi.org/10.1007/s12665-022-10675-4
13. DecTree: a physics-based geochemical surrogate for surface complexation of uranium on clay M. De Lucia https://doi.org/10.5194/adgeo-65-45-2024
14. Speeding Up Reactive Transport Simulations in Cement Systems by Surrogate Geochemical Modeling: Deep Neural Networks and k-Nearest Neighbors E. Laloy & D. Jacques https://doi.org/10.1007/s11242-022-01779-3
15. CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics H. Jung et al. https://doi.org/10.5194/gmd-16-1683-2023
16. Using Machine Learning to Develop a Surrogate Model for Simulating Multispecies Contaminant Transport in Groundwater T. Nguyen et al. https://doi.org/10.3390/hydrology12070185
17. Geochemical and reactive transport modelling in R with the RedModRphree package M. De Lucia & M. Kühn https://doi.org/10.5194/adgeo-56-33-2021