An autonomous living database for perovskite photovoltaics

TL;DR

This paper introduces PERLA, an autonomous, self-updating database for perovskite photovoltaics that leverages AI to extract and validate complex device data from literature, enabling rapid, data-driven insights.

Contribution

The authors develop PERLA, a novel AI-powered pipeline that automates literature curation with high accuracy, transforming static data into a dynamic resource for photovoltaic research.

Findings

Identified a shift towards inverted architectures with self-assembled monolayers.

Detected a trend of voltage loss reduction over recent years.

Achieved human-level data extraction precision (>90%).

Abstract

Scientific discovery is severely bottlenecked by the inability of manual curation to keep pace with exponential publication rates. This creates a widening knowledge gap. This is especially stark in photovoltaics, where the leading database for perovskite solar cells has been stagnant since 2021 despite massive ongoing research output. Here, we resolve this challenge by establishing an autonomous, self-updating living database (PERLA). Our pipeline integrates large language models with physics-aware validation to extract complex device data from the continuous literature stream, achieving human-level precision (>90%) and eliminating annotator variance. By employing this system on the previously inaccessible post-2021 literature, we uncover critical evolutionary trends hidden by data lag: the field has decisively shifted toward inverted architectures employing self-assembled monolayers and formamidinium-rich compositions, driving a clear trajectory of sustained voltage loss reduction. PERLA transforms static publications into dynamic knowledge resources that enable data-driven discovery to operate at the speed of publication.