Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative ModelDownload PDF

27 Sept 2021, 17:50 (modified: 03 Dec 2021, 15:24)DGMs and Applications @ NeurIPS 2021 OralReaders: Everyone
Keywords: Deep generative modeling, de novo molecular design, cheminformatics, expert-in-the-loop
TL;DR: Conditional sampling method for generating novel photo-acid generator molecules for use in photolithography with deep generative models and expert-in-the-loop
Abstract: Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions) when exposed to light. These compounds are critical components of the photolithography processes that are used in the manufacture of semiconductor logic and memory chips. The exponential increase in the demand for semiconductors has highlighted the need for discovering novel photo-acid generators. While de novo molecule design using deep generative models has been widely employed for drug discovery and material design, its application to the creation of novel photo-acid generators poses several unique challenges, such as lack of property labels. In this paper, we highlight these challenges and propose a generative modeling approach that utilizes conditional generation from a pre-trained deep autoencoder and expert-in-the-loop techniques. The validity of the proposed approach was evaluated with the help of subject matter experts, indicating the promise of such an approach for applications beyond the creation of novel photo-acid generators.
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