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

• URL: http://arxiv.org/abs/2412.14109v1
• Date: Wed, 18 Dec 2024 17:52:45 GMT
• Title: Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells
• Authors: Yang Pu, Zhiyuan Dai, Yifan Zhou, Ning Jia, Hongyue Wang, Yerzhan Mukhametkarimov, Ruihao Chen, Hongqiang Wang, Zhe Liu,
• Abstract summary: Co-Pilot for Perovskite Additive Screener (Co-PAS) is an ML-driven framework designed to accelerate additive screening for perovskite solar cells.<n>Co-PAS overcomes predictive biases by integrating scaffold-based pre-screening and latent Junction Tree Variational Autoencoder (JTVAE)<n>We identify several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccin ester (BTN)
• Score: 12.969955836781773
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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