Deep generative fuel design in low data regimes via multi-objective imitation

Commercial fuel discovery faces a constantly decreasing return of investment due to due to increasingly tight environmental criteria and reducing potential uses for each new fuel. In this paper, a deep generative model, termed Latent Interspace Generative Adversarial Network with a Domain of Stacking (LIGANDS), has been established to screen desired fuel molecules in the large chemical space without setting design rules manually. A variational autoencoder, a generative adversarial network and a stacking model are well integrated in LIGANDS through model convergence. Given only the structures of 255 typical highenergy???density fuels in low data regimes, LIGANDS generated 3461 new fuel molecules with similar property distribution and improved energy performance as the qualified candidates of next-generation fuels. To expand and enrich the fuel-relevant chemical space with innovative molecular entities on demand, in-depth multi-objective imitation on the key properties of target fuel is realized by LIGANDS through optimizing generative molecular structures and their distribution. ?? 2023 Elsevier Ltd. All rights reserved.

Automated summary

Commercial fuel discovery is facing decreasing return of investment due to stricter environmental criteria and reducing potential uses for each new fuel. This study proposes a deep generative model called LIGANDS, which screens desired fuel molecules in a large chemical space without manually setting design rules. LIGANDS integrates a variational autoencoder, a generative adversarial network, and a stacking model to generate new fuel molecules with similar properties and improved energy performance. The model imitates key properties of target fuel to expand and enrich the fuel-relevant chemical space with innovative molecular entities.