Prediction of solar cell materials via unsupervised literature learning.

Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the natural language processing (NLP) technique to unsupervisedly predict the existence of solar cell types including the dye-sensitized solar cells and the perovskite solar cells based on literatures published prior to their first discovery without human annotation. Enlightened by this, we further identify possible solar cell material candidates via NLP starting with a comprehensive training database of 3.2 million paper abstracts published before 2021. The NLP model effectively predicts the existing solar cell materials, while an uncommon solar cell material namely PtSeis suggested as an appropriate candidate for the future solar cells. Its optoelectronic properties are comprehensive investigated via first-principles calculations to reveal the decent stability and optoelectronic performance of the NLP-predicted candidate. This study demonstrates the viability of the textual data for the data-driven materials prediction and highlights the NLP method as a powerful tool to reliably predict the solar cell materials.