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The properties of materials depend heavily on their atomistic structure; knowledge of the possible stable atomic configurations that define a material is required to understand the performance of many technologically and ecologically relevant devices, such as those used for energy storage (A. F. Harper et al., 2020; Marbella et al., 2018). First-principles crystal structure prediction (CSP) is the art of finding these stable configurations using only quantum mechanics (A. F. Harper, Evans, et al., 2020). Density-functional theory (DFT) is a ubiquitous theoretical framework for finding approximate solutions to quantum mechanics; calculations using a modern DFT package are sufficiently robust and accurate that insight into real materials can be readily obtained. The computationally intensive work is performed by well-established, low-level software packages, such as CASTEP (Clark et al., 2005) or Quantum Espresso (Giannozzi et al., 2009), which are able to make use of modern highperformance computers. In order to use these codes easily, reliably and reproducibly, many high-level libraries have been developed to create, curate and manipulate the calculations from these low-level workhorses; matador is one such framework.

matador: a Python library for analysing, curating and performing high-throughput density-functional theory calculations