Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Abstract

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)-based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light-absorbing materials, electron-transporting materials, and hole-transporting materials in PSCs is successfully learned by the NLP-based machine learning model without a time-consuming human expert training process. The NLP model highlights a hole-transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole-transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.