Transfer learning discovery of molecular modulators for perovskite solar cells

TL;DR

This paper introduces a transfer learning approach using pre-trained neural networks to efficiently predict and identify effective molecular modulators for perovskite solar cells, significantly accelerating discovery and improving efficiency.

Contribution

It develops a transfer learning framework with diverse molecular representations, enabling high-throughput virtual screening and experimental validation of modulators for PSCs.

Findings

Achieved high accuracy in predicting modulator effects on PCE.

Screened over 79,000 molecules for potential modulators.

Validated top modulators with a champion PCE of 26.91%.

Abstract

The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error experimental screening. Concurrently, machine learning (ML) offers significant potential for accelerating materials discovery. However, applying ML to PSCs remains a major challenge due to data scarcity and limitations of traditional quantitative structure-property relationship (QSPR) models. Here, we apply a chemical informed transfer learning framework based on pre-trained deep neural networks, which achieves high accuracy in predicting the molecular modulator's effect on the power conversion efficiency (PCE) of PSCs. This framework is established through systematical benchmarking of diverse molecular representations, enabling lowcost and high-throughput virtual screening over 79,043 commercially available molecules. Furthermore, we leverage interpretability techniques to visualize the learned chemical representation and experimentally characterize the resulting modulator-perovskite interactions. The top molecular modulators identified by the framework are subsequently validated experimentally, delivering a remarkably improved champion PCE of 26.91% in PSCs.