Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing

arXiv:2402.19462v1 Announce Type: cross
Abstract: We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified. Our approach demonstrates a potential reduction in discovery time by approximately 75 %, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to …

abstract active learning arxiv cells cond-mat.mtrl-sci cs.cl data data-driven discovery error extract faster insights language language processing literature materials natural natural language natural language processing novel physics.app-ph pipeline processing property solar strategies type