In this licentiate thesis, the search for new materials is presented within the paradigm of materials informatics, which uses high-throughput, density-functional-theory-optimized workflows and machine learning to discover new materials from combinations of the elements of the periodic table. I developed workflows to investigate phase transitions in pseudo-binary fluoride perovskite solid solutions and to estimate the Curie temperature of magnetic materials. Modern material science offers a vast array of computational tools, ranging from machine learning and artificial intelligence to high-performance computing and advanced codes maintained by dedicated researchers. To leverage these techniques and perform large-scale theoretical investigations, workflows can be developed to manage the workload of executing hundreds of thousands of calculations efficiently.

In paper I, we developed a workflow to identify pseudo-binary solid solutions of fluoride perovskites that share at least one common atomic species apart from fluoride. The set of candidate endpoints investigated consists of 3,969 unique perovskites, which would yield 7,874,496 material systems if not systematically reduced through a screening process. The screening involves three steps: (i) verifying that the endpoints are non-conductive by calculating their band gaps using density functional theory (DFT), (ii) ensuring that the endpoints can form a solution with a phase transition along the composition interval, and (iii) assessing the alloy’s synthesizability by comparing it to known theoretical phases with similar stoichiometry. The screening process identified 111 promising solid solutions, and 11 were studied in detail to validate the initial predictions, showing good agreement.

In paper II, we developed a workflow that uses DFT calculations to estimate the Curie temperature of magnetic materials. The process consists of two steps: first, calculating the magnetic ground state, and second, constructing a supercell containing at least 12 magnetic atoms for a disordered local moment calculation. The resulting data, combined with parameters fitted to experimental results, enable prediction of the Curie temperature with a mean absolute error of approximately 126 K.

These works highlight the usefulness of automated computational workflows and the opportunities they create for investigating large numbers of materials and deriving meaningful conclusions from extensive datasets.