In order to obviate such limitations, machine learning (ML) methods can be very helpful, offering significant computational speed-ups and enabling more extensive and complex simulations. ML techniques are increasingly used to assist traditional experimental and computational approaches, making it feasible to solve otherwise computationally prohibitive problems. In the study of geochemical processes, ML can significantly accelerate the notoriously slow geochemical calculations, achieving speed-ups of several orders of magnitude . This advancement not only enhances computational efficiency but also enables more realistic simulations in reasonable time, marking a significant step forward in the field. Although ML significantly improves computational efficiency, it lacks the capability for dynamic interaction with the experimental process in real-time, which may limit its potential benefits. With the integration of dynamic feedback mechanisms, ML could enable timely adjustments based on new experimental data, leading to cost savings, increased efficiency, and more responsive experimental designs. This would enhance the ability to adapt to evolving conditions during experiments, enabling optimizations that could improve both the accuracy and the overall outcomes of the results.