To quantify the observed qualitative behaviour along the 21 TF, we calculate the R 2 score between the RDF curves of the DL-DEM approach against the target DEM ones, for Test I (Figure ) and Test II, III and IV (Figure ). Pure DL model occurs when 𝑛 𝐷𝐸𝑀 = 0, in that case we observe that the R 2 score drops rapidly after the first prediction (TF = 5). However, when we increase the number of DEM steps between DL predictions (𝑛 𝐷𝐸𝑀 = 7000 and 10000), the R 2 stabilizes above 0.9. Additional to the analysis performed with the data metrics, we proceed to calculate the electrode functional metrics of the dried electrode microstructures. In Figure , we show the obtained percentage errors for the different electrode functional metrics for each of the dried electrodes (4 test microstructures) and for the different numbers of DEM steps (𝑛 𝐷𝐸𝑀 ) in a heat map. These are calculated for the density, porosity and tortuosity factor, which are essential properties determined by the manufacturing parameters. First, the prediction of the density is accurate (with an error strictly less than 3 %) for all test microstructures and that it does not have variations between the number of DEM steps of the coupled simulations. Then, for the errors in porosity (Figure ) and tortuosity factor (Figure ), we have some difference between the tested microstructures and the number of DEM steps considered. But both cases share a similar behavior, with higher errors for 0 DEM steps (purely DL prediction) compared to the obtained with running different number of DEM steps (coupled DL-DEM prediction). For test microstructure I we have a decrease from 62 % to approximately 10 % in the porosity error when DL-DEM is implemented. In the case of the tortuosity factor, this decrease is from 52 % to around 4 %. For test microstructure II we have a decrease from 61 % to around 12 % and from 45 % to 3 % for porosity and tortuosity factor errors, respectively. While for test microstructure III these decreases go from 63 % and 46 % to 9 % and 11 %, respectively. The last test microstructure (IV) has this similar decrease for the porosity error, but the tortuosity factor error decreases slowly from 43 % to reach 10 % for 10000 nDEM steps. To see the potential of the PAML model, we also test its performance by evaluating a test dataset consisting on a different formulation in which the model has not been trained for: 94% NMC -3% C65 -3% PVdF (94 % AM and 6 % CBD), according to equation 6, 𝑋 [16] = Test I, 𝑋 [17] = Test II, 𝑋 [18] = Test III and 𝑋 [19] = Test IV. Similarly, as in the previous formulation (96 % AM and 4 % CBD), we calculate the RDF comparing the DEM target versus the prediction of our PAML model (𝑛 𝐷𝐸𝑀 = 0, 5000, 7000, 10000) at the end of the drying process (21 TF). In all cases, Test I (Figure ) and Test II, III and IV (Figure ), we see that the not inclusion of DEM steps (𝑛 𝐷𝐸𝑀 = 0), i.e. the purely data-driven DL model, does not capture well the interactions to allow a reasonable prediction of the RDF. We observe that it is difficult for the PAML model to predict accurately the first peak of the RDF, given that the model was not trained under this formulation: this is expected since there were not local environment information coming from the 94 % AM and 6 % CBD formulation, since none of the simulations were used in the training samples.