Machine learning to establish zero point energy as a screening parameter for identifying vibrationally stable perovskites

The present calculations show that scanning the potential energy surface of any compositional space (i.e. perovskites, in this context) using formation energy calculations i.e. with E_hull analysis is not sufficient to comment on vibrational stability of compounds. Machine learning is used to classify the vibrationally stable perovskites from the unstable ones, which shows the zero point energy (ZPE), i.e. thermal energy content of the system at 0 K, has a strong implication on thermal stability of compounds. Below a threshold value of ZPE (∼ 40 kJmol⁻¹), 90 % of compounds are found to be vibrationally stable. This is very advantageous, since ZPE can be absolutely quantitatively calculated for each structure individually. However, it suffers lengthy vibration calculation, which can not be performed for large scale high throughput screening. Thus, one of the compress sensing techniques (SISSO) is used to provide a highly accurate regression model for the ZPE using only elemental and simple compound descriptors. This model, while providing computationally inexpensive means to predict the vibrational stability of compounds, also establishes ZPE as a reference free screening parameter for predicting the vibrational stability of the compounds, which can be extended to different compound classes and even to elevated temperatures.