{"title":"Inverse design of copolymers including stoichiometry and chain architecture","authors":"Gabriel Vogel, Jana M. Weber","doi":"10.1039/d4sc05900j","DOIUrl":null,"url":null,"abstract":"The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be beneficial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables <em>de novo</em> generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for <em>in silico</em> discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space.","PeriodicalId":9909,"journal":{"name":"Chemical Science","volume":"77 1","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical Science","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1039/d4sc05900j","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be beneficial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables de novo generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for in silico discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space.
期刊介绍:
Chemical Science is a journal that encompasses various disciplines within the chemical sciences. Its scope includes publishing ground-breaking research with significant implications for its respective field, as well as appealing to a wider audience in related areas. To be considered for publication, articles must showcase innovative and original advances in their field of study and be presented in a manner that is understandable to scientists from diverse backgrounds. However, the journal generally does not publish highly specialized research.