Data-driven descriptors are generally achieved and employed by the means of machine learning (ML), and they can be useful when trying to reduce the computational cost of modeling tasks. Moreover, data-driven descriptors might both avoid human biases and allow for the analysis of multi dimensional relations between descriptors and real properties. Examples of these descriptors are the weights and biases of neural networks, whose iterative optimization (i.e. training) can lead to predictions comparable in accuracy to the ones of DFT. Such accuracy, though, comes at the cost of model interpretability, albeit some strategies can be used to improve on that like the use of increasingly "expressive" material encodings. The use of ML is not limited to property prediction, but it branches out to the use of text mining to retrieve information about synthesis methods and conditions and the use of neural networks to predict the synthesis path of target compounds, illustrating the high flexibility of this kind of approach. As pointed out by Ling et al. the emergence of ML has brought a sensible push forward in general purpose material science: from reaction and synthesizability network analysis [15] to the acceleration of "traditional" force field calculations and material discovery as a whole. The field of property (i.e. anthropic descriptors) prediction can benefit from ML models as well, in taking advantage of the cheaper computational costs of the models training as compared to their calculation, that is essentially based on modeling with data-driven descriptors. [9] Battery modeling, though, can hardly rely only on first principle cal-culations to achieve a "holistic" description of a whole galvanic cell.