Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells

TL;DR

This paper introduces Co-PAS, an ML framework that efficiently screens organic additives for perovskite solar cells, overcoming biases and identifying promising new molecules like BTN that improve device efficiency.

Contribution

The work presents a novel ML-driven screening method combining scaffold classification and advanced molecular representations to accelerate additive discovery for PSCs.

Findings

Identified several promising passivating molecules including BTN

Achieved a device PCE of 25.20% with selected additive

Demonstrated the effectiveness of Co-PAS in high-throughput screening

Abstract

Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors. Present work thus proposes an effective approach, Co-Pilot for Perovskite Additive Screener (Co-PAS), an ML-driven framework designed to accelerate additive screening for perovskite solar cells (PSCs). Co-PAS overcomes predictive biases by integrating the Molecular Scaffold Classifier (MSC) for scaffold-based pre-screening and utilizing Junction Tree Variational Autoencoder (JTVAE) latent vectors to enhance molecular structure representation, thereby enhancing the accuracy of power conversion efficiency (PCE) predictions. Leveraging Co-PAS, we integrate domain knowledge to screen an extensive dataset of 250,000 molecules from PubChem, prioritizing candidates based on predicted PCE values and key molecular properties such as donor number, dipole moment, and hydrogen bond acceptor count. This workflow leads to the identification of several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccinimide ester (BTN), which, to our knowledge, has not been explored as an additive in PSCs and achieves a device PCE of 25.20%. Our results underscore the potential of Co-PAS in advancing additive discovery for high-performance PSCs.