Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4713-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4713-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
29 Jul 2021
Model evaluation paper |
| 29 Jul 2021
| 29 Jul 2021
DecTree v1.0 – chemistry speedup in reactive transport simulations: purely data-driven and physics-based surrogates
GFZ German Research Centre for Geosciences, Telegrafenberg,
14473 Potsdam, Germany
Michael Kühn
GFZ German Research Centre for Geosciences, Telegrafenberg,
14473 Potsdam, Germany
University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
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Cited
18 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Reactive transport model of kinetically controlled celestite to barite replacement M. Tranter et al. https://doi.org/10.5194/adgeo-56-57-2021
- 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
- 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
- Improving the Performance of Reactive Transport Simulations Using Artificial Neural Networks E. Demirer et al. https://doi.org/10.1007/s11242-022-01856-7
- Geochemistry and machine learning: methods and benchmarking N. Prasianakis et al. https://doi.org/10.1007/s12665-024-12066-3
- 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
- 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
- Digitalisation for nuclear waste management: predisposal and disposal O. Kolditz et al. https://doi.org/10.1007/s12665-022-10675-4
- 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
- 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
- Vadose zone interfacial dynamics dictating per- and polyfluoroalkyl substances (PFAS) detection, transport, and remediation: a review A. Farid et al. https://doi.org/10.1007/s11157-026-09794-2
- 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
- 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
- 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
18 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Reactive transport model of kinetically controlled celestite to barite replacement M. Tranter et al. https://doi.org/10.5194/adgeo-56-57-2021
- 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
- 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
- Improving the Performance of Reactive Transport Simulations Using Artificial Neural Networks E. Demirer et al. https://doi.org/10.1007/s11242-022-01856-7
- Geochemistry and machine learning: methods and benchmarking N. Prasianakis et al. https://doi.org/10.1007/s12665-024-12066-3
- 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
- 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
- Digitalisation for nuclear waste management: predisposal and disposal O. Kolditz et al. https://doi.org/10.1007/s12665-022-10675-4
- 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
- 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
- Vadose zone interfacial dynamics dictating per- and polyfluoroalkyl substances (PFAS) detection, transport, and remediation: a review A. Farid et al. https://doi.org/10.1007/s11157-026-09794-2
- 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
- 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
- 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
Saved (final revised paper)
Latest update: 16 Sep 2026
Short summary
DecTree evaluates a hierarchical coupling method for reactive transport simulations in which pre-trained surrogate models are used to speed up the geochemical subprocess, and equation-based
full-physicssimulations are called only if the surrogate predictions are implausible. Furthermore, we devise and evaluate a decision tree surrogate approach designed to inject domain knowledge of the surrogate by defining engineered features based on law of mass action or stoichiometric reaction equations.
DecTree evaluates a hierarchical coupling method for reactive transport simulations in which...
