Document Type
Article
Publication Date
3-22-2022
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Mechanical and Aerospace Engineering
Abstract
Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.
Digital Commons Citation
Musho, Terence and Tempke, Robert, "Autonomous design of new chemical reactions using a variational autoencoder" (2022). Faculty & Staff Scholarship. 3103.
https://researchrepository.wvu.edu/faculty_publications/3103
Source Citation
Tempke, R., Musho, T. Autonomous design of new chemical reactions using a variational autoencoder. Commun Chem 5, 40 (2022). https://doi.org/10.1038/s42004-022-00647-x
Comments
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This article received support from the WVU Libraries' Open Access Author Fund.