Abstract
We present a battery screening Python pipeline, VOLTA. It allows for a novel battery active material explorative workflow, prioritizing the cell level performance indicators, such as cell capacity and voltage profile. This is achieved by the construction of a starting dataset of both observed and virtual active materials from the Materials Project, the implementation of the physics-based ARTISTIC project pipeline for the assessment of practical electrode properties like porosity and thickness, and the coupling of the electrodes into virtual cells, whose figures of merit are calculated, like voltage and capacity. The screening can be conducted by applying filters to these cell-level properties, achieving a indirect selection of the most suitable active materials.The approach is validated through comparison to current commercial battery technology, and we demonstrate that VOLTA is able to identify promising electrode materials for high energy batteries, like the wellknown LiCoO2, LiNiO2 and graphite. We also illustrate a case-study, where the pipeline is used to identify suitable low-voltage, realistic virtual batteries obtained by combining entries of the Materials Project battery database (a battery revealing type of task).