Data-mining and High-throughput Approaches to Predict Novel Perovskite Materials

Although perovskites are archetypal extended periodic solid-state structures, predicting their crystal structures ex nihilo is as challenging as the organic molecular materials, in where the discrete molecular polymorphic controllers that are relatable to the polyhedral tilts in the perovskite crystal lattices preclude finding stable stationary points [1]. Here, we present first-principles-based theoretical high throughput workflows accounting systematic data-mining schemes to predict synthesizable perovskite structure candidates. The schemes involve judicious choices of symmetry selections, group-subgroup Bärnighausen trees, and phonon modulations for phase transitions [2-4].

[1] E. A. Wood, Acta Crystallographica 4, 353 (1951).

[2] F. Naaz, M. S. Chauhan, K. Yadav, S. Singh, A. Kumar, and D. L. V. K. Prasad, arXiv:2301.05691 (2023).

[3] G. Bergerhoff, R. Hundt, R. Sievers, and I. D. Brown, J. Chem. Inf. Comput. Sci. 23, 66 (1983).

[4] E. Blokhin and P. Villars The Pauling File Project and Materials Platform for Data Science: From Big Data Toward Materials Genome (Springer, 2018).