A Generative Deep Learning Workflow for Inverse Molecular Design of Fuels

Excerpt

arXiv:2504.12075v4 Announce Type: replace Abstract: In the present work, a generative deep learning framework combining a Co-optimized Variational Autoencoder (Co-VAE) with quantitative structure-property relationship (QSPR) techniques is developed to enable inverse molecular design of fuels. The Co-VAE approach integrates an auxiliary fuel property prediction regression head with the VAE latent space, enhancing molecular reconstruction and accurate property estimation (Research Octane Number (R