Anthropic descriptors have been developed to rationalize and predict materials properties from domain knowledge. Examples include proxies for stability, e.g. the energy above the convex hull, or more specific descriptors like the Li phonon-band center for Li-ion conductor materials. [3] While such a modeling strategy has proven useful, finding correlation between a certain descriptor and a property does not necessarily imply causation: screening with anthropic descriptors always needs some caution, as pointed out by Ghiringhelli et al. This classic screening strategy mainly relies on Density Functional Theory (DFT), thanks to its high accuracy and its capability to output readable, physically meaningful descriptors, like formation energies. The high computational cost of DFT does not allow for the direct exploration of a wide chemical space, hence funnel-like screening pipelines had to be developed to progressively reduce it. Material screening for energy storage applications, and batteries in particular, can naturally be conducted with such an approach. Traditional computation-based science is giving a tremendous impulse to an accurate and high-throughput prediction of battery-relevant materials' properties.